IJACSA Volume 11 Issue 4 - thesai.org

IJACSA Volume 11 Issue 4

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: A Deep Neural Network Study of the ABIDE Repository on Autism Spectrum Classification

Abstract: The objective of this study is to implement deep neural network (DNN) models to classify autism spectrum disorder (ASD) patients and typically developing (TD) participants. The experimental design utilizes functional connectivity features extracted from resting-state functional magnetic resonance imaging (rs-fMRI) originating in the multisite repository Autism Brain Imaging Data Exchange (ABIDE) over a significant set of training samples. Our methodology and results have two main parts. First, we build DNN models using the TensorFlow framework in python to classify ASD from TD. Here we acquired an accuracy of 75.27%. This is significantly higher than any known accuracy (71.98%) using the same data. We also obtained a recall of 74% and a precision of 78.37%. In summary, and based on our literature review, this study demonstrated that our DNN (128-64) model achieves the highest accuracy, recall, and precision on the ABIDE dataset to date. Second, using the same ABIDE data, we implemented an identical experimental design with four distinct hidden layer configuration DNN models each preprocessed using four different industry accepted strategies. These results aided in identifying the preprocessing technique with the highest accuracy, recall, and precision: the Conﬁgurable Pipeline for the Analysis of Connectomes (CPAC).

Author 1: Xin Yang

Author 2: Paul T. Schrader

Author 3: Ning Zhang

Keywords: DNN; ASD; rs-fMRI; ABIDE; CPAC

Paper 2: Issues and Challenges: Cloud Computing e-Government in Developing Countries

Abstract: Cloud computing has become essential for IT resources that can be delivered as a service over the Internet. Many e-government services that are used worldwide provide communities with relatively complex applications and services. Governments are still facing many challenges in their implementation of e-government services in general, including Saudi Arabia, such as poor IT infrastructure, lack of finance, and insufficient data security. This research paper investigates the challenges of e-government cloud service models in developing countries. This paper finds that governments in developing countries are influenced by how the top management deals with the attention to the adoption of cloud computing. Further, organisational readiness levels of technologies, such as IT infrastructure, internet availability and social trust of the adoption of new technology as cloud computing, still present limitations for e-government cloud services adoption. Based on the findings of the critical review, this paper identifies the issues and challenges affecting the adoption of cloud computing in e-government such as IT infrastructure, internet availability, and trust adopted new technologies thereby highlighting benefits of cloud computing-based e-government services. Furthermore, we propose recommendations for developing IT systems focused on trust when adopting cloud computing in e-government services (CCEGov).

Author 1: Naif Al Mudawi

Author 2: Natalia Beloff

Author 3: Martin White

Keywords: Challenges; issue; privacy; security; social; e-governance services; citizen

Paper 3: Conceptual Framework for Finding Approximations to Minimum Weight Triangulation and Traveling Salesman Problem of Planar Point Sets

Abstract: We introduce a novel Conceptual Framework for finding approximations to both Minimum Weight Triangulation (MWT) and optimal Traveling Salesman Problem (TSP) of planar point sets. MWT is a classical problem of Computational Geometry with various applications, whereas TSP is perhaps the most researched problem in Combinatorial Optimization. We provide motivation for our research and introduce the fields of triangulation and polygonization of planar point sets as theoretical bases of our approach, namely, we present the Isoperimetric Inequality principle, measured via Compactness Index, as a key link between our two stated problems. Our experiments show that the proposed framework yields tight approximations for both problems.

Author 1: Marko Dodig

Author 2: Milton Smith

Keywords: Computational geometry; minimum weight triangu-lation; combinatorial optimization; traveling salesman problem

Paper 4: Content Delivery Networks in Cross-border e-Commerce

Abstract: Cross-border e-commerce has been growing in recent years. Purchasing goods from abroad is getting easier due to global deliveries, well-known payment methods and decreasing language barriers. Content Delivery Network (CDN) is a technical solution. Deploying CDN for cross-border e-commerce can improve performance and consumer experience. In this paper, four sets of cross-border online stores, containing together 57 e-commerce stores, are examined. Each set is a group of online stores operating in many European markets with different Domain Name System (DNS) settings. Two sets use Cloudflare CDN and its DNS server. Other two sets use country DNS server settings without CDN. Results show that DNS lookup time significantly decrease for cross-border users when online stores are using a CDN, improving overall website time load. Increase of speed for resolving domain names is on average from 40ms to 5ms. No significant improvement was observed for user in the same country location.

Author 1: Artur Strzelecki

Keywords: e-Commerce; cross-border e-commerce; content delivery networks; DNS lookup time; time to first byte

Paper 5: P System Framework for Ant Colony Algorithm in IoT Data Routing

Abstract: The Internet of Things (IoT) is a critical part of current information technology. When designing IoT data routes, device limited resources such as the computation speed, available amount of memory, remaining battery power or channel bandwidth, to name a few, must be considered. Since the Ant Colony System (ACS) is successfully applied to solving different routing problems, the implementation of ACS for routing in the IoT has also been considered. A P system inspired by the nature of membrane processes not only simplifies a complex system behavior annotation but also delivers a good balance between performance, flexibility and scalability. For this reason, the P system framework for ACS in IoT data routing has been investigated. From the research conducted, MMAPS, which is a combination of the P system and the Max-Min Ant System, is seen to perform better than the ACS.

Author 1: Aurimas Gedminas

Author 2: Liudas Duoba

Author 3: Dalius Navakauskas

Keywords: Membrane computing; P system; Ant Colony System; Internet of Things; energy consumption; load balancing

Paper 6: A New Framework of Moving Object Tracking based on Object Detection-Tracking with Removal of Moving Features

Abstract: Object Tracking (OT) on a Moving Camera so-called Moving Object Tracking (MOT) is extremely vital in Computer Vision. While other conventional tracking methods based on fixed camera can only track the objects in its range, a moving camera can tackle this issue by following the objects. Moreover, single tracker is used widely to track object but it is not effective due to the moving camera because the challenges such as sudden movements, blurring, pose variation. The paper proposes a method inherited by tracking by detection approach. It integrates a single tracker with object detection method. The proposed tracking system can track object efficiency and effectively because object detection method can be used to find the tracked object again if the single tracker loses track. Three main contributions are presented in the paper as follow. First, the proposed Unified Visual based-MOT system can do the tasks such as Localization, 3D Environment Reconstruction and Tracking based on Stereo Camera and Inertial Measurement Unit (IMU). Second, it takes into account camera motion and the moving objects to improve the precision rate in localization and tracking. Third, proposed tracking system based on integration of single tracker as Deep Particle Filter and Object Detection as Yolov3. The overall system is tested on the dataset KITTI 2012, and it has achieved a good accuracy rate in real time.

Author 1: Ly Quoc Ngoc

Author 2: Nguyen Thanh Tin

Author 3: Le Bao Tuan

Keywords: Moving object tracking; object detection; camera localization; 3D environment reconstruction; tracking by detection

Paper 7: Variation of Aerosol Pollution in Peru during the Quarantine Due to COVID-19

Abstract: Due to COVID-19, which is a type of pneumonia produced by a coronavirus family virus, the Peruvian government has decreed mandatory social isolation. This isolation is extended until 26 April 2020. Due to this situation, people must stay at home and only go out to make purchases to cover basic needs. This situation, between other things, probably causes pollution reduction that is important for our ecosystem. In Peru, there is not a measurable way to quantify the impact of social isolation on air pollution. The present work aims to show more objectively how much decrease the aerosol pollution in Peru. For this purpose, one uses remote sensing data from Copernicus Data Hub of the European Space Agency, specifically, Sentinel-5 Precursor satellite. The results show an essential reduction of aerosol pollution in different regions of Peru, especially in Lima and the Amazon regions.

Author 1: Avid Roman-Gonzalez

Author 2: Natalia I. Vargas-Cuentas

Keywords: COVID-19; coronavirus; pollution; Sentinel-5P; Peru

Paper 8: Augmented Reality Application for Hand Motor Skills Rehabilitation

Abstract: The paper presents an augmented reality based solution for hand movement rehabilitation using visual and tactile feedback. The proposed approach to the rehabilitation process combines the effects on visual, auditory and tactile channels of perception and simulation scenarios. In order to develop a hand movement model capable of solving rehabilitation tasks there was studied the concept of immersive virtual reality. Original 3D models and scenes have been developed to simulate the basic hand positions and motor functions. To provide efficient hand motion fixation it is recommended to implement a mechanical position tracking system based on a sensor glove improved by using the resistive transducers. Augmented reality is implemented to inspire and motivate the user to perform the required exercises by generating the corresponding pictures. Personalized comparative analysis of the dynamics of the patient's initial condition and rehabilitation results help to study the motor function and restore everyday skills. The proposed solution allows achieving accuracy and adequacy of fingers movements sufficient to satisfy the requirements of medical rehabilitation applications.

Author 1: Alexandr Kolsanov

Author 2: Sergey Chaplygin

Author 3: Sergey Rovnov

Author 4: Anton Ivaschenko

Keywords: 3D modeling; augmented reality; virtual reality; simulation; hand motor skills rehabilitation

Paper 9: Textile EEG Cap using Dry-Comb Electrodes for Emotion Detection of Elderly People

Abstract: Emotions are fundamental to human life and can impact elderly healthcare encounters between caregiver and patient. Detecting emotions by monitoring the physical signals with wearable smart devices offers new promises for care support. While there are multiple studies on wearable devices, few of these pertain to soft electroencephalogram (EEG) caps designed for long-time wear by elderly people. In this study, a 4-channel textile cap was designed with dry electrodes held by an ultra-soft gel holder, while fashion and ergonomic design features were introduced to enhance wearability and comfort. The dry-electrode textile cap performed highly for monitoring EEG signals; closely matching the wet electrodes equipment. All participants reported positive feedback stating that the textile cap was softer, lighter, and more comfortable than other devices. A cumulative contribution rate of 72.199% for two factors (materials properties factor and design pattern factor) was achieved using the principal factor method (PFA), which are influencing the usability of the wearable devices. An average emotion classification accuracy of 81.32% was obtained from 5 healthy elderly subjects. It was thus concluded that the proposed method provides a stable monitoring and comfortable user experience for users, and can be used to detect emotions for elderly people with good results in the future.

Author 1: Fangmeng ZENG

Author 2: Panote Siriaraya

Author 3: Dongeun Choi

Author 4: Noriaki Kuwahara

Keywords: EEG monitoring; elderly people; emotion detection; textile EEG cap; wearing-comfort

Paper 10: Arduino based Smart Home Automation System

Abstract: Around the World massive quantities of energy are consumed in residential buildings leading to a negative impact on the environment. Also, the number of wireless connected devices in use around the World is constantly and rapidly increasing, leading to potential health risks due to over exposer to electromagnetic radiation. An opportunity appears to reduce the energy consumption in residential buildings by introducing smart home automation systems. Multiple such solutions are available in the market with most of them being wireless, so the challenge is to design such systems that would limit the quantity of newly generated electromagnetic radiation. For this we look at several wired, serial communication methods and we successfully test such a method using a simple protocol to exchange data between an Arduino microcontroller board and a Visual C# app running on a Windows computer. We aim to show that if desired, smart home automation systems can still be built using simple viable alternatives to wireless communication.

Author 1: Daniel Chioran

Author 2: Honoriu Valean

Keywords: Energy consumption; home automation; serial communication; microcontrollers

Paper 11: Application of Dual Artificial Neural Networks for Emergency Load Shedding Control

Abstract: This paper proposes a new model in emergency control of load shedding based on the combination of dual Artificial Neural Network to implement the load shedding, restore the power system frequency and prevent the power system blackout. The first Artificial Neural Network (ANN1) quickly recognizes the state with or without load shedding when a short-circuit occurs in the electrical system. The second Artificial Neural Network (ANN2) identifies and controls the selection of load shedding strategies. These load shedding strategies include pre-designed rules which is built on the AHP algorithm to calculate the importance factor of the load units and select the priority of the load shedding. In case the ANN1 results in a load shedding, the load shedding control strategy is immediately implemented. Therefore, the decision making time is much shorter than the under frequency load shedding method. The effectiveness of the proposed method is tested on the IEEE 39-bus system which proves the effectiveness of this method.

Author 1: Nghia. T. Le

Author 2: Anh. Huy. Quyen

Author 3: Au. N. Nguyen

Author 4: Binh. T. T. Phan

Author 5: An. T. Nguyen

Author 6: Tan. T. Phung

Keywords: Load shedding; Artificial Neural Network; AHP algorithm; emergency control; frequency stability

Paper 12: A Robust Pneumonia Classification Approach based on Self-Paced Learning

Abstract: This study proposes a self-paced learning scheme that integrates self-training and deep learning to select and learn labeled and unlabeled data samples for classifying anterior-posterior chest images as either being pneumonia-infected or normal. With this new approach, a model is first trained with labeled data. The model is evaluated on unlabeled data to generate pseudo labels for the unlabeled data. Using a novel selection scheme, the pseudo-labeled samples are then selected to update the model in next training iteration of the semi-supervised training process. The selected pseudo-labeled images to be added to the next training iteration are images with the most confident probabilities from every unlabeled class. Such a selection scheme prevents mistake reinforcement, which is a prevalent occurrence in self-training. With deep models having the tendency to latch onto well-represented class samples while ignoring less transferable and represented classes, especially in the case of unbalanced data, the proposed method utilizes a novel algorithm for the generation and selection of reliable top-K pseudo-labeled samples to be used in updating the model during the next training phase. Such an approach does not only force the model to learn the hard samples in the training data, it also helps enlarge the training set by generating enough samples that satisfy the hunger of deep models. Extensive experimental evaluation of the proposed method yields higher accuracy results compared to methods mentioned in the literature on the same dataset, an indication of the effectiveness of the proposed method.

Author 1: Sarpong Kwadwo Asare

Author 2: Fei You

Author 3: Obed Tettey Nartey

Keywords: Anterior-posterior chest images; self-paced learn-ing; self-training; pneumonia classification

Paper 13: Predicting the Optimal Date and Time to Send Personalized Marketing Messages to Repeat Buyers

Abstract: Most of today’s digital marketing campaigns which are sent through email and mobile messaging are bulk campaigns which deliver the same message at the same time to all customers, regardless of their needs and preferences. The outcomes are bad customer experience, low engagement and low conversion rates. Modern marketing automation tools aim to facilitate personalized communications, such as scheduling of individual marketing messages based on each individual subscriber’s profile. This research focuses on the problem of automatically deciding on the optimal date and time for sending consent-based personalized marketing messages. We specifically focus on the case of repeat consumers of consumer packaged goods (CPG) which require regular replacement or replenishment. The objective is to timely anticipate the needs of consumers in order to increase their level of engagement as well as the rate at which they repurchase products. The proposed solution is based on a regression model trained with transactional data and instant messaging metadata. We describe the way such a model can be created and deployed to a scalable high-performance environment and provide pilot evaluation results that suggest a significant improvement in marketing effectiveness.

Author 1: Alexandros Deligiannis

Author 2: Charalampos Argyriou

Author 3: Dimitrios Kourtesis

Keywords: Personalized marketing automation; customer re-lationship management; conversion rate optimization; customer engagement; machine learning; XGBoost regression; cloud com-puting; data privacy

Paper 14: The Effects of Various Modes of Online Learning on Learning Results

Abstract: The demand for online learning particularly in a college is necessity to be developed and implemented as an alternative method of delivery learning materials in this millennial era. Nowadays, the developments are strongly supported by the advancement of Information Technology and Communication (ICT) and Multimedia Technology. Nevertheless, during the development or engineering process of the online learning, the principles of interactive, creative and effective learning deserve attention. The challenge now is the suitable mode of online learning decided to be developed and applied so that the learning process is conducted effectively. There are few things to be considered in the development of the learning, such as: how large the percentages of the number of online meetings are in comparison to face-to-face meetings and how the content type. This study aims to investigate the effects of various modes of online learning to the learning result. There are some teaching methods or modes namely face-to-face, blended, web, and online learning. This experiment is conducted to implement all the same learning materials and is available online for the four online learning modes. The research subject observed is the students of ITB STIKOM BALI who attend the Multimedia Learning course in odd semester 2019/2020. There are four classes with 108 students and each class is given a different mode of online learning. The method of analysis of this study is the statistical analysis, ANCOVA Univariate, on which 1 factor with 4 treatments. The result of this study revealed that there is equality of the students’ learning results toward the four modes of online learning. Therefore, the development of online learning for conceptual types of teaching materials or the achievement of student learning at the level of understanding is recommended.

Author 1: Muhammad Rusli

Keywords: Online learning; web learning; blended learning; face to face learning; interactive multimedia learning; learning results

Paper 15: Transformation of SysML Requirement Diagram into OWL Ontologies

Abstract: The Requirement Diagrams are used by the System Modeling Language (SysML) to depict and model non-functional requirements, such as response time, size, or system functionality, which cannot be accommodated in the Unified Modeling Language (UML). Nevertheless, SysML still lacks the capability to represent the semantic contexts within the design. Web Ontology Language (OWL) can be used to capture the semantic context of system design; hence, the transformation of SysML diagrams into OWL is needed. The current method of SysML Diagrams transformation into OWL is still done manually so that it is very vulnerable to errors, and the translation process requires more time and effort for system engineers. This research proposes a model that can automatically transform a SysML Requirement Diagram into an OWL file so that system designs can be easily understood by both humans and machines. It also allows users to extract knowledge contained in the previous diagrams. The transformation process makes use of a transformation rule and an algorithm that can be used to change a SysML Requirement Diagram into an OWL ontology file. XML Metadata Interchange (XMI) serialization is used as the bridge to perform the transformation. The produced ontology can be viewed in Protégé. The class and subclass hierarchy, as well as the object properties and data properties, are clearly shown. In the experiment, it is also shown that the model can conduct the transformation correctly.

Author 1: Helna Wardhana

Author 2: Ahmad Ashari

Author 3: Anny Kartika Sari

Keywords: SysML Diagram; Requirement Diagram; ontology; OWL; transformation

Paper 16: Clustering Social Networks using Nature-inspired BAT Algorithm

Abstract: The widespread extent of internet availability at low cost impels user activities on social media. As a result, a huge number of networks with a lot of varieties are easily accessible. Community detection is one of the significant tasks to understand the behavior and functionality of such real-world networks. Mathematically, community detection problem has been modeled as an optimization problem and various meta-heuristic approaches have been applied to solve the same. Progressively, many new nature-inspired algorithms have also been explored to handle the diverse optimization problems in the last decade. In this paper, nature-inspired Bat Algorithm (BA) is adopted and a new variant of Discrete Bat algorithm (NVDBA) is recommended to identify the communities from social networks. The recommended scheme does not require the number of communities as a prerequisite. The experiments on a number of real-world networks have been performed to assess the performance of the proposed approach which in turn confirms its validity. The results confirm that the recommended algorithm is competitive with other existing methods and offers promising results for identifying communities in social networks.

Author 1: Seema Rani

Author 2: Monica Mehrotra

Keywords: Community detection; nature inspired optimization; bat algorithm; discrete particle swarm optimization; social network

Paper 17: Power Allocation Evaluation for Downlink Non-Orthogonal Multiple Access (NOMA)

Abstract: Fifth-generation of wireless cellular systems has the potential to increase capacity, spectral efficiency, and fairness among users. The Non-Orthogonal Multiple Access based wireless networks (NOMA) is the next generation multiplexing technique. NOMA breaks the orthogonality of traditional multiple access to allow multiple users to share the same radio resource simultaneously. The main challenge in designing NOMA is the selection of the resource allocation algorithms since user pairing and power allocation are coupled. This paper compares the performance of three power allocation schemes: fixed power allocation, fractional transmit power allocation and full search power allocation. The algorithms are analyzed in different simulation scenarios using three performance metrics of the spectrum efficiency and energy efficiency and sum rate. Additionally, the impact of user pairing algorithms studied through two user pairing schemes: random user pairing and channel state sorting based user pairing. Results indicate the superiority of NOMA to increase the capacity compared to traditional orthogonal multiple access. On the other hand, full search power allocation is the best performance compared to the other power allocation schemes though it is highly complex compared to fractional transmit power that gives a suboptimal performance.

Author 1: Wajd Fahad Alghasmari

Author 2: Laila Nassef

Keywords: Non-Orthogonal Multiple Access; NOMA; Power Allocation; User Pairing; Spectral Efficiency; Sum Rate

Paper 18: Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

Abstract: In recent times, dimension size has posed more challenges as compared to data size. The serious concern of high dimensional data is the curse of dimensionality and has ultimately caught the attention of data miners. Anomaly detection based on local neighborhood like local outlier factor has been admitted as state of art approach but fails when operated on the high number of dimensions for the reason mentioned above. In this paper, we determine the effects of different distance functions on an unlabeled dataset while digging outliers through the density-based approach. Further, we also explore findings regarding runtime and outlier score when dimension size and number of nearest neighbor points (min_pts) are varied. This analytic research is also very appropriate and applicable in the domain of big data and data science as well.

Author 1: Mujeeb Ur Rehman

Author 2: Dost Muhammad Khan

Keywords: High dimensional data; density-based anomaly detection; local outlier; outlier detection

Paper 19: Marathi Document: Similarity Measurement using Semantics-based Dimension Reduction Technique

Abstract: Textual data is increasing exponentially and to extract the required information from the text, different techniques are being researched. Some of these techniques require the data to be presented in the tabular or matrix format. The proposed approach designs the Document Term Matrix for Marathi (DTMM) corpus and converts unstructured data into a tabular format. This approach has been called DTMM in this paper and it fails to consider the semantics of the terms. We propose another approach that forms synsets and in turn reduces dimensions to formulate a Document Synset Matrix for Marathi (DSMM) corpus. This also helps in better capturing the semantics and hence is context-based. We abbreviate and call this approach as DSMM and carry out experiments for document-similarity measurement on a corpus consisting of more than 1200 documents, consisting of both verses as well as proses, of Marathi language of India. Marathi text processing has been largely an untouched area. The precision, recall, accuracy, F1-score and error rate are used to prove the betterment of the proposed technique.

Author 1: Prafulla B. Bafna

Author 2: Jatinderkumar R. Saini

Keywords: Cosine similarity; marathi; synset; term matrix; wordnet

Paper 20: Study on Extended Scratch-Build Concept Map to Enhance Students’ Understanding and Promote Quality of Knowledge Structure

Abstract: Many studies reported that an open-ended concept map technique is a standard for reflecting learners' knowledge structure. However, little information has been provided that expands open-ended concept mapping to improve students' learning outcomes and meaningful learning. This study aimed to investigate the effects of Extended Scratch-Build (ESB) concept mapping on students' learning outcomes, consisting of understanding, map size, and quality of knowledge structure. ESB is an extended open-ended technique that requests students to connect a prior-existing original concept map with a new additional map on related material topics. ESB offers an expansion of concept maps by adding new propositions and linking them to previous existing maps to enhance meaningful learning. Twenty-five university students have participated in the present study. The collected data included a pre-test, post-test, delayed-test, map size, and quality of map proposition scores. The Wilcoxon signed-rank test was used to confirm the ESB performance. The statistical results indicated that ESB could improve meaningful learning through extended concept mapping approach and had a positive effect on students' learning outcomes. This study also emphasized that there was a correlation between the original and additional maps on students' learning outcomes.

Author 1: Didik Dwi Prasetya

Author 2: Tsukasa Hirashima

Author 3: Yusuke Hayashi

Keywords: Concept map; open-ended; extended; knowledge structure

Paper 21: The Neural Network Conversation Model enables the Commonly Asked Student Query Agents

Abstract: One of the challenges in academic counselling is to provide an automated service system for students. There several query questions asking the faculty staffs about related-academic services each semester. Offered the communication interface more convenience, the novel approach based on neural network model is introduced to investigate the automated conversational agent. The pre-defined dialogue sentences were collected manually from the student query questions and used as the training dataset. The questions have been varied and grouped by topic-categorizing queried from the registration help desk of the department. Artificial intelligence and machine learning have contributed each other to build the conversational agent so-call KUSE-ChatBOT plugged and used in the modern messenger application, LINE. The system is also included the dialogue back-end management system to use in further deep learning model updating. Tensorflow, the machine learning development platform originated by Google, was performed and obtained the learning model using Python development kits. The LINE Messaging APIs is then contributed as the user interface where users could have FAQs' conversation via the LINE application. The KUSE-ChatBOT is outperformed and efficient by providing automated consultation to the students precisely with the accuracy rate over 75 percent. The system could assist the staffs to be able to lessen the workload of answering the same question repeatedly and give response to the student timely.

Author 1: Nittaya Muangnak

Author 2: Natakorn Thasnas

Author 3: Thapani Hengsanunkul

Author 4: Jakkarin Yotapakdee

Keywords: Automated conversational agent; chatbot; natural language processing; FAQs’ bot; artificial neural network; artificial intelligence; machine learning

Paper 22: Mobile Health Services in Saudi Arabia-Challenges and Opportunities

Abstract: In this work, the mobile health services (MHS) approach has been introduced to encourage locals with different educational backgrounds. This work intends to minimize personal interaction hours between patients and doctors in a real-time healthcare environment. The increasing number of pilgrims to Saudi Arabia (SA) demands such an arrangement for the benefit of both people and service provider authorities. Especially dealing with the patients visiting at the time of Ramadan is going to be a challenging task for the authorities and healthcare service providers if some kind of virus spreads in the Kingdom. The recent Corona virus threat is making most of the people panic and almost all the countries in the world are feeling the heat to tackle such a scenario. Due to a famous pilgrim destination, dealing the visitor’s flow is always a challenging task. Therefore, the proposed MHS uses the latest applications of neural networks (NN), artificial intelligence (AI), bigdata (BD) and predictive data analytics (PDA) for improving the performance of healthcare operations. At the initial stage of this research, the risk prediction and mitigation process of various events have seen an accuracy of 95 %. Applications of AI and BD are being extensively used to upgrade the patient records and information at a faster rate to enhance the overall performance of healthcare services.

Author 1: Amr Jadi

Keywords: m-Health; IoT; Saudi Hospitals; challenges

Paper 23: Empirical Investigation on the Impact of Public Expenditures on Inclusive Economic Growth in Morocco: Application of the Autoregressive Distributed Lag Approach

Abstract: Today more than ever, the international institutions (the IMF, the World Bank, the OECD and the UN) as well as the public authorities are interested in questions related to the development issue in general, and more particularly to inclusive growth. The reason is that in most developing countries, such as Morocco, the increase in economic growth does not necessarily and automatically have an effect on poverty and social disparities reduction. In this context, the study aims to analyse the impact of public expenditures, in particular the human capital development expenditure (education and health) and the public investment, on inclusive economic growth in Morocco through the use of the autoregressive distributed lag (ARDL) model on annual macroeconomic data from 1980 to 2018 and the bounds cointegration test of Pesaran. The results of the estimates show that, in the long term, public investment expenditures positively contribute to economic growth. Furthermore, they revealed that strong government action on human capital development expenditures is the most powerful instrument for enhancing inclusive economic growth in Morocco.

Author 1: Imad KHANCHAOUI

Author 2: Abdeslam EL MOUDDEN

Author 3: Sara El Aboudi

Keywords: Inclusive economic growth; public expenditures; human capital development expenditures; public investment expenditures; cointegration; ARDL model

Paper 24: Deep Neural Networks Combined with STN for Multi-Oriented Text Detection and Recognition

Abstract: Developing systems for interpreting visuals, such as images, videos is really challenging but important task to be developed and applied on benchmark datasets. This study solves the very challenge by using STN-OCR model consisting of deep neural networks (DNN) and Spatial Transformer Networks (STNs). The network architecture of this study consists of two stages: localization network and recognition network. In the localization network it finds and localizes text regions and generates sampling grid. Whereas, in the recognition network, text regions will be input and then this network learns to recognize text including low resolution, curved and multi-oriented text. Deep learning-based approaches require a lot of data for training effectively, therefore, this study has used two benchmark datasets, Street View House Numbers (SVHN) and International Conference on Document Analysis and Recognition (ICDAR) 2015 to evaluate the system. The STN-OCR model achieves better results than literature on these datasets.

Author 1: Saif Hassan Katper

Author 2: Abdul Rehman Gilal

Author 3: Abdullah Alshanqiti

Author 4: Ahmad Waqas

Author 5: Aeshah Alsughayyir

Author 6: Jafreezal Jaafar

Keywords: Spatial Transformer Networks (STNs); Deep Neural Networks (DNN); ICDAR dataset; multi-oriented text; STN-OCR

Paper 25: Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data

Abstract: The ability to predict students’ academic performance is critical for any educational institution that aims to improve their students' learning process and achievement. Although students’ performance prediction problem is studied widely, it still represents a challenge and complex issue for educational institutions due to the different features that affect students learning process and achievement in courses. Moreover, the utilization of web-based learning systems in education provides opportunities to study how students learning and what learning behavior leading them to success. The main objective of this research was to investigate the impact of assessment grades and online activity data in the Learning Management System (LMS) on students’ academic performance. Based on one of the commonly used data mining techniques for prediction, called classification. Five classification algorithms were applied that decision tree, random forest, sequential minimal optimization, multilayer perceptron, and logistic regression. Experimental results revealed that assessment grades are the most important features affecting students' academic performance. Moreover, prediction models that included assessment grades alone or in combination with activity data perform better than models based on activity data alone. Also, random forest algorithm performs well for predicting student a cademic performance, followed by decision tree.

Author 1: Amal Alhassan

Author 2: Bassam Zafar

Author 3: Ahmed Mueen

Keywords: Predict student performance; learning management system; data mining; educational data mining; classification model

Paper 26: Regression Model and Neural Network Applied to the Public Spending Execution

Abstract: Artificial Neural Networks are connectionist systems formed by numerous process units called neurons connected to each other, which adapt their structure through learning techniques to solve problems of function approximation and pattern classification. They process information that is supplied to them, either to obtain relationships between them and the objective function that is intended to be approximated, or by classifying these data into different categories. Regression analysis aims to determine the type of functional relationship that exists between a dependent variable and one or more independent variables. The purpose of the research is to use regression methods (multiple regression) and artificial neural networks (multilayer perceptron) to determine the influence of spending execution on the regional government's public budget. 95% of the variability of the budget of Moquegua region has been determined and explained by the three sectors (primary, secondary and tertiary) and 5% is determined by other factors outside the regional government budget. The determination coefficients R2 = 95.9% in the regression model and R2 = 95.3% in the neural network (multilayer perceptron). It has been demonstrated that Artificial neural networks and regression models have obtained very similar results, achieving good and good-fit models.

Author 1: José Morales

Author 2: José Huanca

Keywords: Regression; neural network; multilayer perceptron; institutional budget; public spending

Paper 27: Arrhythmia Classification using 2D Convolutional Neural Network

Abstract: Arrhythmia is an abnormal situation of heartbeat rate that may cause a critical condition to our body and this condition gets more dangerous as our cardiovascular system gets more vulnerable as we grow older. To diagnose this abnormality, the arrhythmia expert or cardiologist uses an electrocardiogram (ECG) by analyzing the pattern. ECG is a heartbeat signal that is produced by a tool called an electrocardiograph sensor that records the electrical impulses produced by the heart. Convolutional Neural Networks (CNN) is often used by researchers to classify ECG signals to Arryhtmia classes. The state-of-the-art research had applied CNN 2D (CNN 2D) with accuracy up to 99% with 128x128 image size obtained by transforming the ECG signal. In this paper, authors try to classify arrhythmia disorder with a different approach by creating simpler image classifier using CNN 2D with a smaller variety of input size that is smaller than state-the-art input and group the classes based on transformed ECG signal from MIT-BIH Arrhythmia database with the purpose to know what the most optimum input and the best accuracy to classify ECG signal image. The result of this research had produced an accuracy of up to 98.91% for 2 Classes, 98.10% for 7 Classes dan 98.45% for 8 Classes.

Author 1: Robby Rohmantri

Author 2: Nico Surantha

Keywords: Convolutional neural network; CNN; CNN 2D; image classifier; electrocardiogram; ECG; arrhythmia

Paper 28: Analysis of an eHealth app: Privacy, Security and Usability

Abstract: Obesity and overweight are considered a health threat globally. Saudi Arabia is a country that has a high percentage of people suffering from obesity. These people can be helped to lose weight through the usage of mobile apps as these apps can collect users’ personal information. These collected data is used to provide precise and personalized weight loss advices. However, weight loss apps must be user friendly, provide data security and user privacy protection. In this paper, we analyze the usability, security, and privacy of a weight loss app. Our main aim to clarify the data privacy and security procedure and test the usability level of the new Arabic weight loss app ‘Akser Waznk’ that is developed considering the social and cultural norms of Saudi users.

Author 1: Ryan Alturki

Author 2: Valerie Gay

Author 3: Nabeela Awan

Author 4: Mohammad Alshehri

Author 5: Mohammed J. AlGhamdi

Author 6: Mehwish Kundi

Keywords: Obesity; usability; data security; privacy; app

Paper 29: Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning

Abstract: The increasing number of mobile devices using the Android operating system in the market makes these devices the first target for malicious applications. In recent years, several Android malware applications were developed to perform certain illegitimate activities and harmful actions on mobile devices. In response, specific tools and anti-virus programs used conventional signature-based methods in order to detect such Android malware applications. However, the most recent Android malware apps, such as zero-day, cannot be detected through conventional methods that are still based on fixed signatures or identifiers. Therefore, the most recently published research studies have suggested machine learning techniques as an alternative method to detect Android malware due to their ability to learn and use the existing information to detect the new Android malware apps. This paper presents the basic concepts of Android architecture, Android malware, and permission features utilized as effective malware predictors. Furthermore, a comprehensive review of the existing static, dynamic, and hybrid Android malware detection approaches is presented in this study. More significantly, this paper empirically discusses and compares the performances of six supervised machine learning algorithms, known as K-Nearest Neighbors (K-NN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR), which are commonly used in the literature for detecting malware apps.

Author 1: Talal A.A Abdullah

Author 2: Waleed Ali

Author 3: Rawad Abdulghafor

Keywords: Android; malware applications; machine learning

Paper 30: Near Duplicate Image Retrieval using Multilevel Local and Global Convolutional Neural Network Features

Abstract: In this work, we present an approach based on multilevel local as well as global Convolutional Neural Network (CNN) feature matching to retrieve near duplicate images. CNN features are suitable for visual matching. The CNN features of entire image may not give accuracy in retrieval due to various image editing/capturing operations. Our retrieval task focuses on matching image pairs based on local and global levels. In local matching, an image is segmented into fixed size blocks followed by extracting patches by considering neighboring regions at different levels. Matching local image patches at different levels provides robustness to our retrieval model. In local patch extraction, we select blocks containing SURF feature points instead of selecting all blocks. CNN features are extracted and stored for each image patch and then followed by extraction of global CNN features. Finally, similarity between image pairs is computed by considering all extracted CNN features. Our similarity function is based on correlation and number of blocks found in matching. We implemented our proposed approach on benchmarking Holiday dataset. Retrieval results show remarkable improvement in mean average precision (mAP) on the dataset.

Author 1: Tejas Mehta

Author 2: C. K. Bhensdadia

Keywords: Near duplicate image retrieval; local CNN features; global CNN features

Paper 31: Using Combined List Hierarchy and Headings of HTML Documents for Learning Domain-Specific Ontology

Abstract: HTML pages contain unstructured and diverse information. However, these documents lack semantics and are not machine understandable. Semantic webs aim to add formal semantics to web data, whereas ontology provides formal semantics to a domain and is thus considered a foundation of semantic webs. Domain ontologies can be constructed manually, but this process is tedious and inefficient. Thus, this study presents an ontology learning (OL) model to create domain ontologies automatically from a set of HTML pages. The key insight of this research is that it combines the list structure and headings of HTML pages to recognize the ontology vocabulary. The approach also incorporates synonym relationships with ontology and allows the semantic interpretation of ontology concepts. We implement the proposed OL approach to build sports ontology from a collection of sports domain HTML documents. The new sports ontology is tested using FaCT++ reasoner; results show no inconsistency in the ontology. Furthermore, experts evaluate the successful mapping of HTML lists and headings to the ontology vocabulary. The proposed OL approach performs effectively and achieves 92.7% and 95.4% precision values for list and heading mapping, respectively.

Author 1: Muhammad Ahsan Raza

Author 2: Binish Raza

Author 3: Taiba Jabeen

Author 4: Sehrish Raza

Author 5: Munnawar Abbas

Keywords: Ontology learning; semantic web; sports ontology; HTML documents; knowledge extraction; ontology engineering

Paper 32: Sentiment Analysis for Assessment of Hotel Services Review using Feature Selection Approach based-on Decision Tree

Abstract: To get the best hotel accommodation equipped with great services is all what a tourist want. Hotel reviews found in social media sometimes become a reference to book a hotel room. The problem is there is sometimes inaccuracy in understanding the reviewer’s sentiment; therefore sentiment analysis approach is used in this study. The sentiment analysis approach use three algorithms within this article; Naïve Bayes, Support vector machines, and decision tree. The result of the experiment is that decision tree is the best algorithm, however the accuracy level still become a focus since it is not optimal. The purpose of this study is to find a hybrid sentiment analysis model of an intelligent application that can be used as a decision support for hotel service assessment recommendations problem. In this paper, we proposed a model which was developed using the feature selection (FS) approach, whereas the improvement of model accuracy was done using information gain (IG). In this study, the experiment was carried out through five stages, namely taking the research dataset in the form of hotel service assessment texts, data pre-processing, weighting, experimental models, and evaluation. Experiments were conducted to get the best accuracy on the proposed model, while the evaluations were carried out to determine the accuracy of the model. Based on the experimental results, the best accuracy level in the model is 88.54%.

Author 1: Dyah Apriliani

Author 2: Taufiq Abidin

Author 3: Edhy Sutanta

Author 4: Amir Hamzah

Author 5: Oman Somantri

Keywords: Sentiment analysis; feature selection; decision tree; support vector machines; Naïve Bayes; hotel services

Paper 33: Using Concordance to Decode the Ideological Weight of Lexis in Learning Narrative Literature: A Computational Approach

Abstract: In learning narrative literature, students find difficulty in comprehending and approaching the ideological messages beyond the usage of recurrent lexical items in literary narrative texts. This problem comes as a result of the huge number of lexical items literary texts, particularly the narrative, abound in. The use of the computer and of computational linguistics work makes it possible to process and examine large data for a variety of purposes and to investigate questions that could not feasibly be answered if the analysis was carried manually. This paper, therefore, investigates the relevance of using concordance to decode the ideological weight of lexis in narrative literature. The main objective of the paper is to explore the extent to which certain ideologies and themes are decoded in literary narrative texts undergone a computational concordance analysis. This is conducted by means of a computational concordancing that is intended for providing two verifiable inputs: Frequency Distribution (FD) and Key Word in Context (KWIC). The paper is grounded on an experimental study, where 39 majoring English students attending one novel course at Prince Sattam bin Abdulaziz University, were voluntarily involved in an optional course addressing the study of a narrative literary text: Animal Farm. Participants were divided into two groups: an experimental group and a control group. The former was allowed to use concordance, whereas the latter was only permitted to use conventional methods of studying narrative texts (mere reading). Both groups are assigned to find out the themes and the ideological meanings inferred from a selected list of 13 words from the novel. Results show that the experimental group manages, by applying concordance, to decode the ideological weight of the selected words in a more accurate, credible and faster way than the control group. This in turn facilitates the process of determining the intended message addressed in the novel, either at the character-character level of discourse or at the author-readers one.

Author 1: Ayman F. Khafaga

Author 2: Iman El-Nabawi Abdel Wahed Shaalan

Keywords: Concordance; computational linguistics; learning; ideology; narrative literature; lexical items

Paper 34: A Multiple Linear Regressions Model for Crop Prediction with Adam Optimizer and Neural Network Mlraonn

Abstract: Due to the increase in population, demand for the food is increasing day by day. Crop prediction is necessary or need of the hour to fill the gap between the demand and the supply. Instead of following a traditional system for crop selection method, a successful crop selection for the given soil properties will help the farmers to get the expected crop yield. The objective of the proposed work is to develop one such system. The proposed system is developed using real data with various soil parameters acquired from soil laboratory located in Chennai. This system uses 16 parameters of soil which includes all the micro, macro nutrients along with that pH, EC, OM values and the recommended crop for the soil parameter. The proposed Mlraonn (Multiple Linear Regression with Adam Optimization in Neural Network) model is developed using Keras software mainly used for Deep Learning. A neural network approach is used to construct a regression model. The model is evaluated with Loss Metrics such as RMSE, MSE, and MAE. The proposed algorithm is compared with the existing standardized machine learning algorithms. It is found that the proposed algorithm gave very minimal error as output in all the above three categories of loss metrics than the standardized algorithm such as Random Forest Regression and Multiple Linear Regression.

Author 1: M. Lavanya

Author 2: R. Parameswari

Keywords: Multiple Linear Regression; Adam Optimization; Neural Network; Keras; Machine learning algorithm; Root Mean Square Error (RMSE); Mean Square Error (MSE); Mean Absolute Error (MAE); presence of Hydrogen (pH); Electrical Conductivity (EC); Organic Matter (OM)

Paper 35: Privacy, Security and Usability for IoT-enabled Weight Loss Apps

Abstract: Obesity is considered as the main health issue worldwide. The obesity rate within Saudi’s citizens is rising alarmingly. The Internet of Things (IoT)-enabled mobile apps can assist obese Saudi users in losing weight via collecting sensitive personal information and then providing accurate and personalized weight loss advice. These data can be collected using embedded IoT devices in a smartphone. However, these IoT-enabled apps should be usable and able to provide data security and user privacy protection. This paper aims to continue our usability study for two Arabic weight loss IoT-enabled apps by performing a qualitative analysis for them. It discusses users’ and health professionals’ feedbacks, concerns and suggestions. Based on the analysis, a comprehensive usability guideline for developing a new Arabic weight loss IoT-enabled app for obese Saudi users is provided.

Author 1: Ryan Alturki

Author 2: Valerie Gay

Author 3: Nabeela Awan

Author 4: Mohammad Alshehri

Author 5: Mohammed J. AlGhamdi

Author 6: Ateeq ur Rehman

Keywords: Internet of things; obesity; usability; data security; privacy; app

Paper 36: Understanding Proximity Mobile Payment Acceptance among Saudi Individuals: An Exploratory Study

Abstract: The electronic method of payments such as cash, debit, or credit as the new method of mobile payment, which is a paradigm shift in the pattern payment. Mobile payment is increasingly an essential electronic service in the banking sector, which also plays a competitive advantage among banks. Although mobile payment has gradually been accepted in Saudi Arabia, limited research has been conducted to explore the barriers to accepting mobile payment among Saudi nationals. This study examined the factors of mobile payment acceptance among Saudis. An online survey was conducted among 414 respondents. The study has succeeded in identifying Ease of Use, Utility, Security, and Awareness as the factors that could result in accepting mobile payment options. It is also found that male respondents have better mobile acceptance than females. A few suggestions to enhance mobile acceptance among the Saudi population are also provided. It is anticipated that the present study will act as a trigger for further studies in this noteworthy area.

Author 1: Rana Alabdan

Author 2: Sulphey MM

Keywords: Awareness; mobile payment; Saudi; quantitative study; e-payment; banking; ease of use

Paper 37: Modeling and Analyses the Equivalent-Schema Models for OSRR and COSRR Coupled to Planar Transmission Lines by Scattering Bond Graph

Abstract: Following consumer demand, international competition has become increasingly high, suddenly industrialists are mobilizing to meet the requirements. Faced with this challenge, and in order to be able to respond to these prerequisites, manufacturers are looking for techniques that will allow them to gain productivity by increasing the rate of perfection before going to manufacturing. Modeling presents the most important phase in a construction chain since it allows not only analysis and understanding of the physical system but also to improve its behavior according to the desired objective from the design phase. The results presented in this article concern the modeling of the transmission lines of metamaterials loaded with OSRR "Open Split-Ring Resonators" and COSRR "Complementary Open Split-Ring Resonators" resonators, with the aim of improving analysis, synthesis and understanding of this system. By using the Scattering Bond Graph technique, which improves the adaptation of the impedance, and reduces the bandwidth. This technique allows us to deduce the scattering parameters (matrix [S]) of the OSRR / COSRR TL elements from the wave matrix [W], hence this matrix is determined through on the specific properties of the equivalent Bond Graph presentation based on the notion of causality.

Author 1: Islem Salem

Author 2: Hichem Taghouti

Author 3: Abdelkader Mami

Keywords: Scattering Bond Graph (SBG); metamaterials; wave matrix [W]; matrix scattering [S]; transmission line; OSRR and OCSRR

Paper 38: An Efficient and Rapid Method for Detection of Mutations in Deoxyribonucleic Acid - Sequences

Abstract: The comparison of genomic sequences plays a key role in determining the structural and functional relationships between genes. This comparison is carried out by identifying the similarities, differences and mutations between genomic sequences. This makes it possible to study and analyze the genetic and the evolutionary relationships between organisms. Alignment algorithms have been in the spotlight for the last few decades, due to a vast genomic data explosion. They have attracted a great deal of interest from many researchers who focus on the development of practical solutions to ensure effective alignments with an optimal response time. In this paper, a novel algorithm based on Discrete To Continuous "DTC" approach has been developed. The proposed methodology was compared against other existing methods, which are largely based on the concept of string matching. Experimental results show that the DTC algorithm delivers supremely efficient alignment with a reduced response time.

Author 1: Wajih Rhalem

Author 2: Jamal El Mhamdi

Author 3: Mourad Raji

Author 4: Ahmed Hammouch

Author 5: Aqili Nabil

Author 6: Nassim Kharmoum

Author 7: Hassan Ghazal

Keywords: Alignment algorithms; genomic sequences; dynamic polynomial interpolation; mutations

Paper 39: The Impact of Translation Software on Improving the Performance of Translation Majors

Abstract: The recent years have witnessed a high demand for professional translation services due to the global nature of world economy, accessibility to data in different languages, and the development of unprecedented communication channels. Translators can no longer meet these growing needs of customers and businesses. As such, different translation technologies have been developed to help translation learners and professionals improve their performance in producing a high quality translation. These technologies are now widely integrated into translation programs in different universities, institutes and training centers around the world for their usefulness and reliability in improving the performance of translation learners and professionals in the delivery of trustworthy and professional translation services. Nevertheless, surveys show that the Saudi labor market still has serious problems with qualified translators who are familiar with translation technologies that have negative impacts on the quality and delivery of translation services. This study, therefore, seeks to explore the opportunities and challenges of incorporating translation software and latest technologies into translation pedagogy in the Saudi universities. An open-ended interview with 37 translation instructors from 9 Saudi universities was conducted. Results indicate that the integration of translation software and technologies is still less than expected. This can be attributed to the fact that the majority of instructors prefer manual translation over computer-assisted translation (CAT), translation technologies are not provided by the institutions, and learning outcomes are not linked to labor market needs.

Author 1: Abdulfattah Omar

Author 2: Ayman F. Khafaga

Author 3: Iman El-Nabawi Abdel Wahed Shaalan

Keywords: CAT; Saudi Universities; SDL Trados Studios; translation pedagogy; translation technologies

Paper 40: Fuzzy Logic based Anti-Slip Control of Commuter Train with FPGA Implementation

Abstract: In the railway industry, slip control has always been essential due to the low friction and low adhesion between the wheels and the rail and has been an issue for the design, activity, and operation of railroad vehicles. Slip is an unpredictable parameter in the railroad that disintegrates the surface of the railroad with a contact surface of the boggy wheel brought about by the mechanical force of traction phenomena, it destabilizes the railway traction which does not fulfill safety and punctuality requirements. In this paper, we present the work based on developing a fuzzy logic-based anti-slip controller for the commuter train using FPGA implementation which minimizes slip parameters. The development of a fuzzy logic-based anti-slip controller for the commuter train is designed in MATLAB and then tested for area-performance parameters in FPGA through the system generator library. Simulation is performed to demonstrate the effectiveness of the proposed fuzzy logic control system for anti-slip control under various parameters, the results of simulation prove the effectiveness of the proposed control system as compared with conventional PID controller and shows high anti-slip control performance under nonlinearity of brake dynamics.

Author 1: Fozia Hajano

Author 2: Tayab D Memon

Author 3: Farzana Rauf Abro

Author 4: Imtiaz Hussain Kalwar

Author 5: Burhan

Keywords: Wheel rail contact condition; anti-slip; railway wheelset fuzzy logic; FPGA hardware estimation

Paper 41: A Z Specification for Reliability Requirements of a Service-based System

Abstract: The utilization of a Web services based application depends not only on meeting its functional requirements but also its non-functional requirements. The nonfunctional requirements express the quality of service (QoS) expected from a system. The QoS describes the capability of the service to meet the requirements of its consumers. In the context of Web services, considerations of QoS are critical for a number of reasons. Reliability is among the important QoS requirements of such distributed components as it enhances confidence in the services provided. Although the importance of QoS requirements are well established, they are often ignored until the end of the development cycle. Reasons cited for this are that they are difficult to define and represent precisely, and relay on entities that may not be known at early stages. This articles aims to address the challenges of incorporating the QoS at an early stage of service development and represent it in a precise manner. To achieve this goal, this paper makes use of a process model to facilitate the incorporation of the QoS attributes and Z as the specification language for its formalism. Reliability is used to exemplify the process. The Z schemas have been checked for syntax and type using the Fuzz type checker.

Author 1: Manoj Lall

Author 2: John A. Van Der Poll

Keywords: Reliability; non-functional requirements; Web services; Quality of Service; Formal specification; UML modelling; Z

Paper 42: CASC 3N vs. 4N: Effect of Increasing Cellular Automata Neighborhood Size on Cryptographic Strength

Abstract: Stream ciphers are symmetric cryptosystems that rely on pseudorandom number generators (PRNGs) as a primary building block to generate a keystream. Stream ciphers have been extensively studied and many designs were proposed throughout the years. One of the popular designs used is the combination of linear feedback shift registers (LFSRs) and nonlinear feedback shift registers (NFSRs). Although this design is suitable for both software and hardware implementation and provides a good randomness behavior, it is still subject to attacks such as fault attacks and correlations attacks. Cellular automata (CAs) based stream ciphers are another design class that has been proposed. CAs display good cryptographic properties as well as a good randomness behavior, also high computational speed and a higher level of security. The use of CAs as cryptographic primitives is not recent and has been thoroughly investigated, especially the use of three-neighborhood one-dimensional cellular automata. In this article, the authors investigate the impact of increasing the neighborhood size of CAs on the security level and the cryptographic properties provided. Thereafter, four-neighborhood one-dimensional CAs are studied and a stream cipher algorithm is proposed. The security of the proposed algorithm is demonstrated by using the results of standard tests (i.e. NIST Test Suite and Dieharder Battery of Tests), particularly by computing the cryptographic properties of the used CAs and by showing the resistance of the suggested algorithm to mostly known attacks.

Author 1: Fatima Ezzahra Ziani

Author 2: Anas Sadak

Author 3: Charifa Hanin

Author 4: Bouchra Echandouri

Author 5: Fouzia Omary

Keywords: Stream ciphers; cellular automata; neighborhood size; dieharder; NIST STS; cryptographic properties; attacks on stream ciphers

Paper 43: Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Abstract: Air quality is crucial for the environment and the life quality of citizens. Therefore, in the present study a software application is developed to predict air quality on the basis of 2.5 particulate matter (〖PM〗_(2.5)) and 10particulate matter (〖PM〗_10), in the city of Upper Hunter, Australia, as it is considered to be one of the cities with the lowest air quality levels worldwide. For this purpose, it has been decided to use the methodology of long-short term memory (LSTM) from data collected by NSW department of planning industry and environment during the period of 30 September 2012 to 30 September 2019, to predict the behavior of the mentioned particulate matter during the month of October 2019. A comparison between the average and maximum values suggested by the software and the actual values has been made and it is shown that the predicted results of the study are quite close to reality. Finally, the results obtained in this study may serve as a basis for local authorities to proceed with the necessary protocols and measures in case an alarming prediction occurs.

Author 1: Alexi Delgado

Author 2: Ramiro Ricardo Maque Acuña

Author 3: Chiara Carbajal

Keywords: Air quality; long-short term memory (LSTM); 2.5 particulate matter (PM₂․₅); 10 Particulate matter (PM₁₀)

Paper 44: Fermat Factorization using a Multi-Core System

Abstract: Factoring a composite odd integer into its prime factors is one of the security problems for some public-key cryptosystems such as the Rivest-Shamir-Adleman cryptosystem. Many strategies have been proposed to solve factorization problem in a fast running time. However, the main drawback of the algorithms used in such strategies is the high computational time needed to find prime factors. Therefore, in this study, we focus on one of the factorization algorithms that is used when the two prime factors are of the same size, namely, the Fermat factorization (FF) algorithm. We investigate the performance of the FF method using three parameters: (1) the number of bits for the composite odd integer, (2) size of the difference between the two prime factors, and (3) number of threads used. The results of our experiments in which we used different parameters values indicate that the running time of the parallel FF algorithm is faster than that of the sequential FF algorithm. The maximum speed up achieved by the parallel FF algorithm is 6.7 times that of the sequential FF algorithm using 12 cores. Moreover, the parallel FF algorithm has near-linear scalability.

Author 1: Hazem M. Bahig

Author 2: Hatem M. Bahig

Author 3: Yasser Kotb

Keywords: Integer factorization; fermat factorization; parallel algorithm; multi-core

Paper 45: Design of Cooperative Activities in Teaching-Learning University Subjects: Elaboration of a Proposal

Abstract: University professors face the challenge of incorporating activities that promote student engagement, discussion, conflict resolution, and teamwork. In this context, cooperative learning emerges as the pedagogical model that fosters teamwork; organizes students into groups where joint and coordinated work reinforces individual and collective learning. The proposal presented facilitates the design of cooperative activities that consider the necessary interdependence between learning, teaching, content and context. In addition to explaining how to articulate all these aspects, it also places the student as the center of the training process, for this it collects the main guidelines of cooperative learning and enriches the learning environment with the potential of management knowledge and communication provided by Information and Communication Technologies. To inform the proposal, the results obtained in four subjects of a mathematical nature are presented; results showing improvements in student learning.

Author 1: Norka Bedregal-Alpaca

Author 2: Arasay Padron-Alvarez

Author 3: Elisa Castañeda-Huaman

Author 4: Víctor Cornejo-Aparicio

Keywords: Cooperative learning; competency focus; cooperative techniques; evaluation

Paper 46: Using Fuzzy-Logic in Decision Support System based on Personal Ratings

Abstract: The decision making process of selecting a service is very complex. Current recommendation systems make a generic recommendation to users regardless of their personal standards. This can result in a misleading recommendation because different users normally have different standards in evaluating services. Some of them might be harsh in their assessment while others are lenient. In this paper, we propose a standard-based approach to assist users in selecting their preferred services. To do so, we develop a judgement model to detect users’ standards then utilize them in a service recommendation process. To study the accuracy of our approach, 65536 service invocation results are collected from 3184 service users. The experimental results show that our proposed approach achieves better prediction accuracy than other approaches.

Author 1: Hmood Al-Dossari

Author 2: Sultan Alyahya

Keywords: Service recommendation; standard detection; user ratings; predication; fuzzy logic; decision support

Paper 47: A Novel Framework for Enhancing QoS of Big Data

Abstract: The dire increase in number of devices connected to the internet is making inherent growth in creation of data. The use of data science in research is creating opportunities for better business analytics and generation of future trends. The data is growing with ever increasing rate and the exponential growth of data is creating opportunities for utilizing the same in process of analysis. The techniques and technology in place is not able to cater the needs of growing data on the Internet. The research work presented here provides a novel framework for improving the performance and management of big data clusters. The research proposed provides a detailed aspect how big data can be handled in the respective domains. The prime aim of this research is to formulate and implement an algorithm by testing with different data sets which can make the process of mining and handling big data easy in the organizations. The framework provides optimized results as compared to traditional systems.

Author 1: Dar Masroof Amin

Author 2: Munishwar Rai

Keywords: IoT (internet of things); big data; DSDSS (Domain Specific Data Distribution Algorithm); AI (Artificial Intelligence); ML (Machine Learning)

Paper 48: Retinal Blood Vessel Extraction using Wavelet Decomposition

Abstract: One important part of the eye that is critical for processing visual information before it is sent through the optic nerve to the visual cortex is the retina. The retina of each individual has its own uniqueness that can be used as a characteristic feature in identifying, verifying, and authenticating. The traditional authentication process has various weaknesses such as forgetting the PIN code or losing the ID card used for obtaining system authentication. The results of extracted retinal blood vessels can be used as a feature in the formation of an individual identification system. In the imaging using a fundus camera, the retina’s blood vessel has distinguishing shape and number of candidates from one human retina to another. In this research, researchers will develop an algorithm for extracting the retinal fundus image’s blood vessels. The feature extraction is done by taking the fundus image feature which is the blood vessel as one of the unique characteristics in forming an individual identification system. The number of blood vessel candidates will then be calculated from the extracted blood vessel result. This research uses wavelet function by looking at the very complex texture of blood vessels using the approximation coefficient. The direction detail coefficient on the wavelet is also used to perform the extraction of retinal blood vessels where the structure of the retinal blood vessels in the fundus image is in all directions. The results of these blood vessel candidates will be used in further research to formulate a biometric system that is formed by unique features in the retinal fundus image which will be used to identify individuals using body traits.

Author 1: Diana Tri Susetianingtias

Author 2: Sarifuddin Madenda

Author 3: Fitrianingsih

Author 4: Dea Adlina

Author 5: Rodiah

Author 6: Rini Arianty

Keywords: Blood vessels; extraction; fundus retina; identification; wavelet

Paper 49: Resource Optimisation using Multithreading in Support Vector Machine

Abstract: Image processing is one of the most important features for vision-based robotic and being used in various applications to increase productivity. Various researchers reported issues computation problem to detect objects in low cost device such as vision-based robotic car. In the fast-paced development of technology, a system that runs automatically with the right results is essential to the completion of a job. This study aims to propose an effective multithreading for road sign recognition. We implemented multithreading algorithm for train and detector processes in SVM to utilise the multicore CPU and evaluate in various condition on by a Raspberry Pi platform. It aims to solve the real-time computation issue using Pi camera. Experimental results show significant improvement of performance to the detection accuracy. In conclusion multithreading significantly improve the detection performance using Raspberry Pi processors with various image resolution and number of SVM model.

Author 1: Wong Soon Fook

Author 2: Abdul Hadi Abd Rahman

Author 3: Nor Samsiah Sani

Author 4: Afzan Adam

Keywords: Robot vision; recognition; multithreading; real-time

Paper 50: FLA-IoT: Virtualization Enabled Architecture for Heterogeneous Systems in Internet of Things

Abstract: A flexible architecture is always required when trying to communicate with heterogeneous kind of systems, and IoT is the largest communication network of the history, which is bringing life to everything around us. Currently available three and four layered communication architectures are the popular basic structures to implement IoT. Where three Layers architecture is composed of perception, network and application layers and four layer architecture is composed of perception, network, service, application layer. The problem with existing architectures is that some layers are not well managed and complex in structure and lacks in the interoperability of different kind devices. In this research we present a virtualization enabled architecture Flexible Layered Architecture for Internet of Things (FLA-IoT) to overcome those challenges. FLA-IoT provides a simple structure with well-organized layers and introduces the creation of Virtual Mote (virtual object) from all real-world devices to enable the communication between unlike devices. This results in an indiscriminate communication between different real-world devices with a well-managed layered architecture.

Author 1: Irfan Latif Memon

Author 2: Shakila Memon

Author 3: Junaid Ahmed Bhatti

Author 4: Raheel Ahmed Memon

Author 5: Abdul Sattar Chan

Keywords: Internet of Things; virtualization; virtual mote; cloud; heterogeneous systems

Paper 51: An Ontological Model of Hadith Texts

Abstract: The Hadith being the second source of legislation after the Holy Qur'an in the religion of Islam, it represents a large body of knowledge in unstructured textual form. The specification of Hadiths makes its automatic exploitation a rather robust and an almost impossible task. To enable different types of computer systems to exploit this knowledge, various researchers used a formal representation of the semantics of Hadith. The widely used semantic representation is ontology defined as concepts and relations extracted from the Hadith in the form of a structure interpretable both by the machine and the human. In this article, we propose an ontology of the Hadith using an approach inspired by the "METHONTOLOGY" methodology. In this project, we are dealing with religious texts in traditional Arabic, and we face many difficulties in achieving complete precision and correctness. Hence, we decided to follow an entirely manual process to ensure the correctness of the results. Since manual ontology development is both time and effort consuming, we decided to focus only on “Wudhu2” related Hadiths.

Author 1: Bendjamaa Fairouz

Author 2: Taleb Nora

Author 3: Arari Amina Nouha

Keywords: Ontology engineering; Islamic ontology; Methontology; semantic representation

Paper 52: Enhance Medical Sentiment Vectors through Document Embedding using Recurrent Neural Network

Abstract: Adverse Drug Reaction (ADR) extraction is the process of identifying drug implications mentioned in social posts. Handling medical text for the identification of ADR is vital to research in terms of configuring the side effect and other medical-related entities within any medical text. However, investigating the role of such effect in the context of positive and negative is the responsibility of sentiment classification task where every medical review document would be categorized into its polarity, this is known as Medical Sentiment Analysis (MSA). Several studies have presented various techniques for MSA. Most of the recent studies have concentrated on architectures such as the Convolutional Neural Network (CNN) to get the document embedding. Yet, such architecture focuses only on the input without considering the previous or latter input. This might lead to weaker embedding for the document where some terms would not be considered. Hence, this paper proposes a new document embedding approach based on the Recurrent Neural Network (RNN) to improve the sentiment classification. Using a benchmark dataset of medical sentiments, the proposed method showed greater performance of sentiment classification accuracy. Such finding proves the effectiveness of RNN in producing document embedding.

Author 1: Rami N. M. Yousef

Author 2: Sabrina Tiun

Author 3: Nazlia Omar

Author 4: Eissa M. Alshari

Keywords: Adverse drug reaction; medical sentiment analysis; recurrent neural network; support vector machine; logistic regression

Paper 53: Analysis of Vulnerability in Emergency Situations in Kindergarten and Primary School Education Centers in Peru

Abstract: The people who require greater protection and safety are children, mainly when they are in an educational center, where teachers are responsible for their care, therefore, it is important to have prepared teachers to face emergency situations, since, the sense of insecurity is greater in national schools due to the shortage of prepared teachers to handle emergencies situations in Peru; there are studies which mention that 98.2% of accidents in educational centers are trauma and falls, also 1 of every 4 students suffers a fracture, therefore, in this study, spatial data of kindergarten and primary education is presented from Peru, relating the number of students per teacher for the year 2019. The regions whose student-teacher relationship is risky for the welfare of the students are presented and analyzed by georeference, this data is public and is provided by the Ministerio de Educación de Perú (MINEDU), and using tools from the Geographic Information System (GIS), and it was possible to generate maps at the district level. Observing at the maps, it was possible to identify that the areas with the greatest risk are in the natural region of the jungle. Base on the spatial distribution of vulnerable points and outliers of the student-teacher relationship at the levels of kindergarten and primary education, it is recommended that governmental and non-governmental institutions in Peru allocate resources urgently to reduce student vulnerability, reducing the relationship between the number of students and teachers, in order to get better the response to any accident or natural disaster.

Author 1: Witman Alvarado-Díaz

Author 2: Alva Mantari Alicia

Author 3: Meneses-Claudio Brian

Author 4: Avid Roman-Gonzalez

Keywords: Geographic information systems; student-teacher relationship; students; teachers; maps; number of vulnerable students

Paper 54: A Review of Critical Research Areas under Information Diffusion in Social Networks

Abstract: An online social network is a network where people exchange their ideas or opinions. Exchange of ideas between users leads to spread of information at a larger scale in the social networks. This spread of information is also called information diffusion. This work is dedicated to identifying research areas under the umbrella of Information Diffusion. The objective of this work is to present an extensive review of such areas, identify the existing research gaps and explore future directions of work. The review also identifies the methodologies, features and aspects studied in the current literature and proposes the optimal feature set to improve performance. This review will enable researchers to quickly identify the research areas, the current gaps and steer them into the possible future directions associated with them.

Author 1: Surbhi Kakar

Author 2: Monica Mehrotra

Keywords: Information diffusion; influence maximization; retweet prediction; influence models

Paper 55: Acoustic Modeling in Speech Recognition: A Systematic Review

Abstract: The paper presents a systematic review of acoustic modeling (AM) techniques in speech recognition(SR). Acoustic modeling establishes a relationship between acoustic information and language construct in SR. Over the past decades, researchers presented studies addressing specific concerns in AM. However, all previous research works lack a systematic and comprehensive review of acoustic modeling issues. A systematic review is introduced to understand the acoustic modeling issues in speech recognition. This paper provides an extensive and comprehensive inspection of various researches that have been performed since 1984. The extensive investigation and analysis into AM was performed by getting the relevant data from 73 research works chose after the screening process between the years from 1984 to 2020. The systematic review process was divided into different parts to investigate acoustic modeling issues. Main issues in acoustic modeling such as feature extraction techniques, acoustic modeling units, speech corpora, classification methods, different tools used, language issues applied, and evaluation parameters were investigated. This study helps the reader to understand various acoustic modeling issues with comprehensive details. The research outcomes presented in this study depict research trends and shed light on new research topics in AM. The result of this review can be used to build a better speech recognition system by choosing a suitable acoustic modeling construct in SR.

Author 1: Shobha Bhatt

Author 2: Anurag Jain

Author 3: Amita Dev

Keywords: Acoustic modeling; speech recognition; systematic review; acoustic unit; MFCC; classification

Paper 56: Parkinson’s Disease Classification using Gaussian Mixture Models with Relevance Feature Weights on Vocal Feature Sets

Abstract: In order to perceive automatically the manifestation of dysarthria in Parkinson’s disease, we propose a novel classifier which is able to categorize acoustic features and detects articulatory deficits. The proposed approach incorporates relevance feature weighting to the Gaussian mixture model in order to address the issue of high dimensionality. Besides, it learns the relevance feature weights with respect to each model along with the Gaussian mixture model parameters to deal with the specificity of the class models. In order to assess the performance of the proposed approach, we used the data collected by the department of neurology in Cerrahpaşa faculty of medicine at Istanbul University. The obtained results of the Gaussian mixture models with relevance feature weights algorithm are first compared to the GMM results, and to the most recent related work. The experimental results showed the effectiveness of the proposed approach with an accuracy of 0.89 and an MCC score of 0.7.

Author 1: Ouiem Bchir

Keywords: Gaussian Mixture Models; relevance feature weights; Parkinson’s disease; acoustic feature sets

Paper 57: BlockChain with IoT, an Emergent Routing Scheme for Smart Agriculture

Abstract: Blockchain is an emerging field of study in a number of applications and domains. Especially when combine with Internet of Things (IoT) this become truly transformative, opening up new plans of action, improving engagement and revolutionizing many sectors including agriculture. IoT devices are intelligent and have high critical capabilities but low-powered and have less storage, and face many challenges when used in isolation. Maintaining the network and consuming IoT energy by means of redundant or fabricated data transfer lead to consumption of high energy and reduce the life of IoT network. Therefore, an appropriate routing scheme should be in place to ensure consistency and energy efficiency in an IoT network. This research proposes an efficient routing scheme by integrating IoT with Blockchain for distributed nodes which work in a distributed manner to use the communicating links efficiently. The proposed protocol uses smart contracts within heterogeneous IoT networks to find a route to Base Station (BS). Each node can ensure route from an IoT node to sink then base station and permits IoT devices to collaborate during transmission. The proposed routing protocol removes redundant data and blocks IoT architecture attacks and leads to lower consumption of energy and improve the life of network. The performance of this scheme is compared with our existing scheme IoT-based Agriculture and LEACH in Agriculture. Simulation results show that integrating IoT with Blockchain scheme is more efficient, uses low energy, improves throughput and enhances network lifetime.

Author 1: Sabir Hussain Awan

Author 2: Sheeraz Ahmed

Author 3: Asif Nawaz

Author 4: Sozan Sulaiman Maghdid

Author 5: Khalid Zaman

Author 6: M.Yousaf Ali Khan

Author 7: Zeeshan Najam

Author 8: Sohail Imran

Keywords: IoT; efficient; energy scheme; agriculture

Paper 58: Three Levels of Modeling: Static (Structure/Trajectories of Flow), Dynamic (Events) and Behavioral (Chronology of Events)

Abstract: Constructing a conceptual model as an abstract representation of a portion of the real world involves capturing the (1) static (things/objects and trajectories of flow), (2) the dynamic (event identification), and (3) the behavior (e.g., acceptable chronology of events) of the modeled system. This paper focuses on examining the behavior notion in modeling and current works in the “behavior space” to illustrate that the problem of behavior and its related concepts in modeling lacks a clear-cut systematic basis. The purpose is to advance the understanding of system behavior to avoid ambiguity-related problems in system specification. It is proposed to base the notion of behavior on a new conceptual model, called the thinging machine, which is a tool for modeling that establishes three levels of representation: (1) a static structural description that is constructed upon the flow of things in five generic operations (activities; i.e., create, process, release, transfer and receive); (2) a dynamic representation that identifies hierarchies of events based on five generic events; and (3) a chronology of events. This is shown through examples that support the thinging machine as a new methodology suitable for all three levels of specification.

Author 1: Sabah Al-Fedaghi

Keywords: System behavior; static model; chronology of events; conceptual representation; dynamic specification

Paper 59: A Meta-Model for Strategic Educational Goals

Abstract: Metamodeling technique is adopted widely in different fields related to software and system engineering. A meta-model represents the abstraction of a detailed design at multiple level. It is used in any structured environment ruled by a certain constraints and obligations and instantiate different platform specific domains from a single platform independent domain. This paper proposes a new model-driven approach for generating and analyzing automatically the outcome measures of strategic educational goals model. A new meta-model augmented with arithmetic semantics is created for Strategic Educational Goals where a set of outlines defines the enhancement framework of an academic organization. The vision, mission, program educational objectives and student outcomes are the four common strategic educational goals. These Goals support the performance roadmap to measures the institution situation and progress. The proposed meta-model is used to evaluate the strategic educational goals in a formal way, improve the continuous improvement process in academic organizations and allows the assessment at different level of management.

Author 1: Mohammad Alhaj

Author 2: Ashraf Sharah

Keywords: Model-driven engineering; meta-model; goal model; ecore modeling framework; object constraint language; strategic educational goals

Paper 60: Soft Computing for Scalability in Context Aware Location based Services

Abstract: Ubiquitous computing blended with context awareness gives user the facility of “anywhere anytime” computing. Location based services represents a class of context aware computing. Involvement of location as the primary input in location based services triggered concerns for user’s privacy. Most of the privacy work in domain of location based services relies on obfuscation strategy along with K anonymity. The proposed work acknowledges the idea of calculating value of K for K anonymity using context factors in fuzzy format. However, with increasing number of these fuzzy context factors resulting in more fuzzy rules, the system will tend to get slower. In order to address this issue, requirement is to reduce the size of rule base without hampering the performance much. Goal of the proposed work is to attain scalability and high performance for the above said system. Towards this, reduction of number of rules in the rule base, of fuzzy inference system has been done using Fuzzy C Means and Genetic Algorithm. Results of reduced rule base have been compared with the results of exhaustive rule base. It has been identified that number of rules can be reduced up to considerable extent with comparable performances and acceptable level of error.

Author 1: Priti Jagwani

Author 2: Saroj Kaushik

Keywords: Context aware LBS; Fuzzy C Means; Genetic Algorithm; location privacy; K anonymity; scalability

Paper 61: Modelling a Hybrid Wireless/Broadband over Power Line (BPL) Communication in 5G

Abstract: 5G will explore wireless communication dynamically, which will provide high-speed internet with low latency. So, real-time communication will be possible, and a vast number of devices will connect from different points. 5G has introduced five enabling technologies. Millimeter wave and small cell are two of them. Small cell connects the user devices by using the millimeter wave. As the frequency range has limitations of distance and high attenuation, it should be reliable for uninterrupted communication. To ensure the uninterrupted communication, hybrid communication network can be a significant solution. In this research, hybrid wireless and Broadband over Power Line (BPL) communication model has been proposed, and the model integrates both technologies in the small cell end. A simulation of BPL and theoretical analysis of wireless communication have also been shown in this paper. From those analysis, the total throughput of the hybrid model has been calculated. Broadband over Power Line is chosen as well as wireless communication in this model because of its infrastructure availability both in city and rural areas, cost-effectiveness and quick installation process. Moreover, the hybrid network will increase the throughput volume, and both communications will act as a backup in an emergency.

Author 1: Mohammad Woli Ullah

Author 2: Mohammad Azazur Rahman

Author 3: Md. Humayun Kabir

Author 4: Muhammad Mostafa Amir Faisal

Keywords: 5G; wireless; broadband over power line; hybrid; millimeter wave; small cell

Paper 62: BlockTrack-L: A Lightweight Blockchain-based Provenance Message Tracking in IoT

Abstract: Data tracking is of great significance and a central part in digital forensics. In today's complex network design, Internet of Things (IoT) devices communicate with each other and require strong security mechanisms. In maintaining an audit trail of IoT devices or provenance of IoT device data, it is important to know the origins of requests to ensure certain level of trust in IoT data. Blockchain can provide traceability of records generated from IoT devices in a sensitive environment. In this paper, we present an application layer data provenance model that works on execute-order architecture for cloud based IoT networks. It supports high throughput of transactions on the blockchain network with lightweight security overhead by using outsourced encryption on edge nodes. All communications among the IoT devices are connected to a blockchain network and stored on permissioned blockchain peers. The proposed system is evaluated to have less cryptographic load by offloading the IoT nodes with Edge nodes.

Author 1: Muhammad Shoaib Siddiqui

Author 2: Toqeer Ali Syed

Author 3: Adnan Nadeem

Author 4: Waqas Nawaz

Author 5: Sami S. Albouq

Keywords: Data provenance; cloud-based IoT; blockchain; attribute based encryption; light-weight signature generation; light-weight authentication

Paper 63: Analysis of Customer Satisfaction Factors on e-Commerce Payment System Methods in Indonesia

Abstract: e-Commerce companies are currently competing to make it easier for customers to make transactions with a variety of payment system methods that have been provided and developed. The research aims to find out the factors that influence customer satisfaction in using the payment system method. The variables used in the study are service, comfort, speed, convenience, benefits, active use and security in conducting transactions. The results of the study concluded what factors influence satisfaction to develop a payment system method. The research model and questionnaire use a modified research model of the successful information system model DeLone and McLane and technology acceptance by Tella (2012) and in analyzing the results of the questionnaire, researchers used descriptive statistics and Structural Equation Model (SEM) analysis using AMOS V.26. The results of the management of these data the researchers concluded that there is one variable that is perceived comfort does not significantly affect satisfaction. Results of this study are expected to provide a reference that can be used by digital business people, particularly financial technology or e-commerce companies in improving services in applying the Payment System Method by factors that influence the level of customer satisfaction to maintain customer loyalty to the company.

Author 1: Hafidz Risqiadi Putra

Author 2: Sfenrianto

Keywords: e-Commerce; payment system methods; DeLone and McLean; Structural Equation Model (SEM)

Paper 64: Image Classification Considering Probability Density Function based on Simplified Beta Distribution

Abstract: Method for image classification considering Probability Density Function (PDF) based on simplified beta distributions is proposed. In this paper, image classification for Synthetic Aperture Radar (SAR) data is concerned. In particular, Probability Density Function (PDF) of SAR data is followed by not multivariate normal distribution but Chi-Square like distribution. It, however, is not always true that the PDF of SAR data is followed by Chi-Square distribution. Due to the mismatch between Chi-Square distribution and actual distribution, classification performance gets worth. In this paper, simplified beta distribution is assumed for the PDF of the SAR data. Furthermore, it is used to add texture information to the SAR data when the Maximum Likelihood classification is applied. In the paper, “Contrast” of texture feature is added to the SAR data. Through the experiments with real SAR data, it is found that matching error between real PDF and the proposed simplified beta distribution is smaller than the normal distribution. It is also found that applying the proposed distribution-adaptive maximum likelihood method using the simplified beta-distribution could achieve a classification accuracy improvement of 94.7% and 12.1%.

Author 1: Kohei Arai

Keywords: Synthetic aperture radar (SAR); maximum likelihood classification: MLH; probability density function (PDF); simplified beta distribution

Paper 65: A Novel Two Level Edge Activated Carry Save Adder for High Speed Processors

Abstract: In today’s increasing demand of higher integration levels of VLSI and ULSI processors memory capacity and ALU efficiency plays a critical role in designing. The chip-size of memory depends on number of Flip-Flop’s (FF) which are the micro cells to store binary values. An efficient adder is always a parameter to estimate the cost effectiveness of multipliers used by ALU. In this paper the authors focuses on frequency clock utilization and also on low power consumption. It presents a novel Carry Save Adder (CSA) combined with the concept of two level clock triggering for high speed integrated circuits. The authors proposes a new Two Level Edge Triggered (TLET) FF’s built with 14Transistors (14T) and 12Transistors (12T), efficient in terms of switching power dissipation and delay in this paper. The innovative idea deals with CSA 14T and 12T which is compared in terms of Switching Power Dissipation (SPD) from 0.8V to 2.0V. The difference in SPD from 0.8V to 2.0V supply voltage analysis is 132.0nWatts for CSA using 16T FFs, 85.6nWatts for CSA using 14T and only 70.3nWatts for CSA using 12T FFs. In this paper, there is full utilization of clock signal.

Author 1: K Mariya Priyadarshini

Author 2: R.S Ernest Ravindran

Author 3: Ipseeta Nanda

Keywords: Carry Save Adder; digital integrated circuits; flip-flop; switching power dissipation; two level edge triggering

Paper 66: Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

Abstract: Although the Internet of things (IoT), cloud computing (CC), and Big Data (BGD) are three different approaches that have evolved independently of each other over time; however, with time, they are becoming increasingly interconnected. The convergence of IoT, CC, and BGD provides new opportunities in various real-time applications, including telecommunication, healthcare, business, education, science, and engineering. Together, these approaches are facing various challenges during data gathering, processing, and management. The focus of this research paper is to pinpoint the emerging trends in IoT, CC, and BGD. The convergence of these approaches and their impact on various real-time applications, benefits, and challenges associated with all these approaches, current industry trends, and future research directions with especial focus on the healthcare domain. The paper also provides a conceptual framework that integrates IoT, CC, and BGD and provides an IoT centric cloud infrastructure using BGD. Finally, this paper summarizes by providing directions for researchers and practitioners about how to leverage the benefits of combining these approaches.

Author 1: Mamoona Humayun

Keywords: Internet of Things (IoT); big data (BGD); cloud computing (CC); sensors; actuators; healthcare

Paper 67: From Traditional to Intelligent Academic Advising: A Systematic Literature Review of e-Academic Advising

Abstract: Academic advising plays a crucial role in the achievement of the educational institution purposes. It is an essential element in solving students' academic problems and maximizing their satisfaction and loyalty. Universities around the world have always tried to improve academic advising to personalize the student’s experience. In fact, technology has the power to improve the advising process and facilitate its corresponding tasks and this has historically taken different forms. Accordingly, this paper provides an overview of academic advising and the technologies proposed to improve it. The authors present a systematic literature review on research papers that proposed an electronic academic advising system to view the research trends and identify electronic academic advising major challenges. The main contribution of this paper is to survey the different aspects and trends about the electronic systems that have been proposed to serve academic advising. This paper is a part of major research that aims to transfer the traditional academic advising to one based on Artificial Intelligence, via the current phase of academic advising.

Author 1: Abeer Assiri

Author 2: Abdullah AL-Malaise AL-Ghamdi

Author 3: Hani Brdesee

Keywords: Academic advising system; electronic advising system; higher education; intelligent systems

Paper 68: Quantitative Exploratory Analysis of the Variation in Hemoglobin Between the Third Trimester of Pregnancy and Postpartum in a Vulnerable Population in VRAEM - Perú

Abstract: The study is based on determining the difference in hemoglobin in the third trimester of pregnancy and the immediate puerperium of childbirths attended at the Hospital de Apoyo San Francisco, during 2018, located in the VRAEM area, a vulnerable area due to the level of poverty and exposure of the area. The type of the research was observational, retrospective longitudinal section in related samples, the study has the descriptive level, it has a sample of 107 childbirths, 55 vaginal and 52 cesarean, the documentary review technique was used, the data was analyzed with the statistical program "R" for data analysis, the non-parametric test Wilcoxon was used. Results: a difference of 1.52 g/dl has been found between hemoglobin of the third trimester of pregnancy 11.89 g/dl and immediate puerperium 10.37 g/dl; when analyzing the difference in vaginal childbirths, hemoglobin in the third trimester was found at 11.90 g/dl and the immediate puerperium was 10.65 g/dl, and in cesarean childbirths was 11.94 g/dl in the third trimester of pregnancy and 10.14 g/dl in the immediate puerperium, finding differences of 1.52 g/dl in vaginal childbirths and 1.8 g/dl in cesarean. Conclusion, there is a significant difference in hemoglobin in the third trimester and postpartum at a p-value of 0.05, being higher in cesarean childbirths; the average postpartum hemoglobin denotes anemia despite the fact that the blood losses were within normal parameters, which indicates that it must achieve that the pregnant women reach the third trimester of pregnancy with at least 13 g/dl of hemoglobin.

Author 1: Lina Cardenás-Pineda

Author 2: Raquel Aronés-Cárdenas

Author 3: Gabriela Ordoñez- Ccora

Author 4: Mariza Cárdenas

Author 5: Doris Quispe

Author 6: Jenny Mendoza

Author 7: Alicia Alva Mantari

Keywords: Hemoglobin; pregnant; puerperium; childbirth; cesarean; anemia; vulnerable area

Paper 69: Towards Robust Combined Deep Architecture for Speech Recognition : Experiments on TIMIT

Abstract: Over the last years, many researchers have engaged in improving accuracies on Automatic Speech Recognition (ASR) task by using deep learning. In state-of-the-art speech recognizers, both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) based Reccurent Neural Network (RNN) have achieved improved performances compared to Convolutional Neural Network (CNN) and Deep Neural Network (DNN). Due to the strong complementarity of CNN, LSTM-RNN and DNN, they may be combined in one architecture called Convolutional Long Short-Term Memory, Deep Neural Network (CLDNN). Similarly we propose to combine CNN, GRU-RNN and DNN in a single deep architecture called Convolutional Gated Recurrent Unit, Deep Neural Network (CGDNN). In this paper, we present our experiments for phoneme recognition task tested on TIMIT data set. A phone error rate of 15.72% has been reached using the proposed CGDNN model. The achieved result confirms the superiority of CGDNN over all their baselines networks used alone and also over the CLDNN architecture.

Author 1: Hinda DRIDI

Author 2: Kais OUNI

Keywords: Automatic speech recognition; deep learning; phoneme recognition; convolutional neural network; long short-term memory; gated recurrent unit; deep neural network; recurrent neural network; CLDNN; CGDNN; TIMIT

Paper 70: An Improved Method for Taxonomy Development in Information Systems

Abstract: Theories of information systems (IS) can be categorized into five types: analytic, explaining, prediction, explaining and prediction, and design and action theory. A taxonomy could be considered a type of analysis theory which specifies the dimensions and characteristics of objects of interest by defining their shared features. Developing a taxonomy can be well suited to Design Science Research (DSR), since the primary goal of DSR is to develop an artifact. DSR is a scientific method that attempts to combine knowledge about the design and development of a solution to enhance existing systems, solve problems, and create a new artifact, such as a taxonomy. Taxonomy is crucial for understanding any phenomenon. It provides a holistic view of that phenomenon, and the classification of objects helps researchers and practitioners to understand complex domains. Nickerson, Varshney and Muntermann offered a method to develop a taxonomy based on well-established literature. Their method considered the only well-established taxonomy development method in the IS discipline. However, the literature reveals that the taxonomy development process in IS research often remains vague and taxonomies are rarely evaluated. This paper aims to improve the taxonomy development method by adopting comprehensive steps from DSR. This includes developing an integration framework for all forms of reasoning logic that are used for developing taxonomy components. The improved method supports creativity by including abduction as a reasoning logic. It also facilitates the efforts of developing a taxonomy for novice researchers by providing a complete taxonomy development roadmap.

Author 1: Badr Omair

Author 2: Ahmad Alturki

Keywords: Classification; design science; design science research; taxonomy; taxonomy development; typology

Paper 71: Authentication using Robust Primary PIN (Personal Identification Number), Multifactor Authentication for Credit Card Swipe and Online Transactions Security

Abstract: In view of effectiveness, ease of access and profitability, the advancement in e-Commerce is an immense step in forward. The development is went with further unusual vulnerabilities to worry. The significant issue all through the world in credit card management is credit card fraud. Because of extortion and fraudsters persistently look for better approaches to confer unlawful activities the organizations, users and establishment finds tremendous loss yearly. In the common Credit Card extortion process, fraudulent transaction will be distinguished only after the transaction is finished. In recent Studies, the security of Credit Card Transaction from unauthorized admittance or usage are addressed by diverse access control methods. This paper illustrates a new scheme of Authentication using Primary PIN and Multifactor authentication to secure credit card transactions.

Author 1: S. Vaithyasubramanian

Keywords: Credit card fraud; online transaction; card swipe transaction; Personal Index Number (PIN); Card Verification Value (CVV); One Time Password (OTP); Primary Personal Index Number; security

Paper 72: Enhanced Data Lake Clustering Design based on K-means Algorithm

Abstract: In recent years, Big Data requirements have evolved. Organizations are trying more than ever to accent their efforts on industrial development of all data at their disposal and move further away from underpinning technologies. After investing around Data Lake concept, organizations must now overhaul their data architecture to face IoT (Internet of Things) and AI (Artificial Intelligence) expansion. Efficient and effective data mapping treatments could serve in understanding the importance of data being transformed and used for decision-making process endorsement. As current relational databases are not able to manage large amounts of data, organizations headed towards NoSQL (Not only Structured Query Language) databases. One such known NoSQL database is MongoDB, which has a high scalability. This article mainly put forward a new data model able to extract, classify, and then map data for the purpose of generating new more structured data that meet organizational needs. This can be carried out by calculating various metadata attributes weights, which are considered as important information. It also processed on data clustering stored into MongoDB. This categorization based on data mining clustering algorithm named K-Means.

Author 1: Jabrane Kachaoui

Author 2: Abdessamad Belangour

Keywords: Big data; Data Lake; NoSQL; MongoDB; K-means; metadata

Paper 73: Representing and Simulating Uncertainty of the Quality of Service of Web Services using Fuzzy Cognitive Map Approach

Abstract: Web services are growing rapidly to provide clients, either organizations or individuals, with multiple Internet services and to offer solutions for the integration of many applications. Quality of Service (QoS) of a Web service is the key consideration of both service providers and users. Thus, measuring the QoS requires, in addition to its normative requirements, engaging the views of clients and service providers and environmental factors. Human intervention and the environment may lead to uncertainty and result in uncertain factors in assessing QoS. In such a case, traditional computing and statistical techniques cannot provide an accurate representation of inherited uncertainties, especially when uncertain variables are connected with ambiguous (fuzzy) relationships. An alternative is to use a soft computing approach. This paper proposes a Fuzzy Cognitive Map (FCM) model as a soft computing approach that can represent and simulate the uncertainty of QoS. FCM represents the uncertain variables in the domain knowledge and their connections in the form of a signed directed graph consisting of nodes representing the variables and directed arrows representing the cause-effect relationships. In addition, it allows representing imprecise data either using numeric data, i.e. in the ranges [0, 1] and [-1, 1], or linguistic data, i.e., "low, medium, high". For calculations, FCM is converted to an adjacency matrix to find the effects of variables on each other. Scenario simulations can also be implemented to help decision makers to investigate appropriate outcomes. Finally, the proposed approach is tested by an experiment to demonstrate its reasonability and admissibility of the representation and simulation of the uncertainty of the QoS domain knowledge.

Author 1: Mamoon Obiedat

Author 2: Ahmad Khasawneh

Author 3: Mustafa Banikhalaf

Author 4: Ali Al-yousef

Keywords: Web services; quality of services; uncertainty; fuzzy cognitive maps; simulation; decision support systems

Paper 74: Machine Learning Techniques to Visualize and Predict Terrorist Attacks Worldwide using the Global Terrorism Database

Abstract: Terrorist attacks affect the confidence and security of citizens; it is a violent form of a political struggle that ends in the destruction of order. In the current decade, along with the growth of social networks, terrorist attacks around the world are still ongoing and have had potential growth in recent years. Consequently, it is necessary to identify where the attacks were committed and where is the possible area for an attack. The objective is to provide assertive solutions to these events. As a solution, this research focuses on one of the branches of artificial intelligence (AI), which is the Automatic Learning, also called Machine Learning. The idea is to use AI techniques to visualize and predict possible terrorist attacks using classification models, the decision trees, and the Random Forest. The input would be a database that has a systematic record of worldwide terrorist attacks from 1970 to the last recorded year, which is 2018. As a final result, it is necessary to know the number of terrorist attacks in the world, the most frequent types of attacks and the number of seizures caused by region; furthermore, to be able to predict what kind of terrorist attack will occur and in which areas of the world. Finally, this research aims to help the scientific community use artificial intelligence to provide various types of solutions related to global events.

Author 1: Enrique Lee Huamaní

Author 2: Alva Mantari Alicia

Author 3: Avid Roman-Gonzalez

Keywords: Artificial intelligence; decision trees; machine learning; random forest; terrorist attack

Paper 75: A Novel Human Action Recognition and Behaviour Analysis Technique using SWFHOG

Abstract: In this paper, a new local feature, called, Salient Wavelet Feature with Histogram of Oriented Gradients (SWFHOG) is introduced for human action recognition and behaviour analysis. In the proposed approach, regions having maximum information are selected based on their entropies. The SWF feature descriptor is formed by using the wavelet sub-bands obtained by applying wavelet decomposition to selected regions. To improve the accuracy further, the SWF feature vector is combined with the Histogram of Oriented Gradient global feature descriptor to form the SWFHOG feature descriptor. The proposed algorithm is evaluated using publicly available KTH, Weizmann, UT Interaction, and UCF Sports datasets for action recognition. The highest accuracy of 98.33% is achieved for the UT interaction dataset. The proposed SWFHOG feature descriptor is tested for behaviour analysis to identify the actions as normal or abnormal. The actions from SBU Kinect and UT Interaction dataset are divided into two sets as Normal Behaviour and Abnormal Behaviour. For the application of behaviour analysis, 95% recognition accuracy is achieved for the SBU Kinect dataset and 97% accuracy is obtained for the UT Interaction dataset. Robustness of the proposed SWFHOG algorithm is tested against Camera view angle change and imperfect actions using Weizmann robustness testing datasets. The proposed SWFHOG method shows promising results as compared to earlier methods.

Author 1: Aditi Jahagirdar

Author 2: Manoj Nagmode

Keywords: Action recognition; behaviour analysis; HOG; salient wavelet feature; neural network; wavelet transform; SWFHOG

Paper 76: A Method to Detect and Avoid Hardware Trojan for Network-on-Chip Architecture based on Error Correction Code and Junction Router (ECCJR)

Abstract: Modern technologies has changed our life, such as everywhere computing communication and internet. Number of transistors increasing in a system day by day and this trend will continue further. The wire connection is easily breakable and not a reliable technology in field of networks. In conventional network dedicated wired path is used among the intellectual property (IP) core for the purpose of communication and due to this wired connection network is not reliable and not scalable. Network-on-Chip Architecture was introduced to solve these problems and gave notable improvements over conventional bus and crossbar communication architectures. Many companies prefer third party vendors for the development of their design in order to reduce the cost. It gives advantage but due to the access of design anyone can do changes at any stage of development cycle. This type of malicious modification of hardware during design or fabrication process is known as Hardware Trojan (HT). It can change the functional behavior of a system or may leak the secret information of critical application which results in degradation of system performance. The proposed research is based on combination of Error Correcting Code and Junction router to detect and avoid HT which can be used for reliable communication in NoC architecture. Simulation results showed good performance of proposed algorithm in term of Packet Latency and Reliability.

Author 1: Hafiz Ali Hamza Gondal

Author 2: Sajida Fayyaz

Author 3: Arooj Aftab

Author 4: Saira Nokhaiz

Author 5: Muhammad Bilal Arshad

Author 6: Waqas Saleem

Keywords: Hardware trojan; network-on-chip; intellectual property; error correcting code; junction router

Paper 77: Feature Selection for Phishing Website Classification

Abstract: Phishing is an attempt to obtain confidential information about a user or an organization. It is an act of impersonating a credible webpage to lure users to expose sensitive data, such as username, password and credit card information. It has cost the online community and various stakeholders hundreds of millions of dollars. There is a need to detect and predict phishing, and the machine learning classification approach is a promising approach to do so. However, it may take several phases to identify and tune the effective features from the dataset before the selected classifier can be trained to identify phishing sites correctly. This paper presents the performance of two feature selection techniques known as the Feature Selection by Omitting Redundant Features (FSOR) and Feature Selection by Filtering Method (FSFM) to the 'Phishing Websites' dataset from the University of California Irvine and evaluates the performance of phishing webpage detection via three different machine learning techniques: Random Forest (RF) tree, Multilayer Perceptron (MLP) and Naive Bayes (NB). The most effective classification performance of these machine learning algorithms is further rectified based on a selected subset of features set by various feature selection methods. The observational results have shown that the optimized Random Forest (RFPT) classifier with feature selection by the FSFM achieves the highest performance among all the techniques.

Author 1: Shafaizal Shabudin

Author 2: Nor Samsiah Sani

Author 3: Khairul Akram Zainal Ariffin

Author 4: Mohd Aliff

Keywords: Relevant features; phishing; web threat; classification; machine learning; feature selection

Paper 78: A Design of Packet Scheduling Algorithm to Enhance QoS in High-Speed Downlink Packet Access (HSDPA) Core Network

Abstract: Voice over Internet Protocol (VOIP) in an efficient manner is a basic requirement of modern era. The real time and non-real traffics demand customized communication provisioning to get guarantee of service. For this we proposed a user fulfillment design for facilitating packets switching in 3G cellular network to insure provisioning of QoS (quality of service) in DiffServ (Differentiated Services) Network. To enhance QoS for real time traffic by reducing delay, packet loss and jitter, we proposed Low latency queuing (LLQ) algorithm. In this paper, we focused on packet scheduling, Diffserv and QoS classes mapping into Universal Mobile telecommunication System (UMTS) classes and buffering. To associate different types of real time multimedia traffic, the QoS provisioning mechanism used different code points of Diffserv. The new idea in LLQ is to map the video and voice traffics against two separate queues and used priority queuing in Low latency queuing for voice traffic. The results got from reproductions shows that proposed calculation meets the QoS prerequisites.

Author 1: Sohail Ahmed

Author 2: Mubashar Ali

Author 3: Abdullah Baz

Author 4: Hosam Alhakami

Author 5: Bilal Akbar

Author 6: Imran Ali Khan

Author 7: Adeel Ahmed

Author 8: Muhammad Junaid

Keywords: Packet Scheduling; Classification; DiffServ; LLQ; EURANE

Paper 79: Deep Learning based Intelligent Surveillance System

Abstract: In the field of developing innovation, pictures are assuming as an important entity. Almost in all fields, picture base data is considered very beneficial, like in the field of security, facial acknowledgment, or therapeutic imaging, pictures make the existence simple for people. In this paper, an approach for both human detection and classification of single human activity recognition is proposed. We implement the pre-processing technique which is the fusion of the different methods. In the first step, we select the channel, apply the top hat filter, adjust the intensity values, and contrast stretching by threshold values applied to enhance the quality of the image. After pre-processing a weight-based segmentation approach is implemented to detect and compute the frame difference using cumulative mean. A hybrid feature extraction technique is used for the recognition of human action. The extracted features are fused based on serial-based fusion and later on fused features are utilized for classification. To validate the proposed algorithm 4 datasets as HOLLYWOOD, UCF101, HMDB51, and WEIZMANN are used for action recognition. The proposed technique performs better than the existing one.

Author 1: Muhammad Ishtiaq

Author 2: Sultan H. Almotiri

Author 3: Rashid Amin

Author 4: Mohammed A. Al Ghamdi

Author 5: Hamza Aldabbas

Keywords: HMG; ALMD; PBoW; DPNs LOP; BoF; CT; LDA; EBT

Paper 80: Improved Security Particle Swarm Optimization (PSO) Algorithm to Detect Radio Jamming Attacks in Mobile Networks

Abstract: Jamming attack is one of the most common threats on wireless networks through sending a high-power signal to the network in order to corrupt legitimate packets. To address Jamming attacks problem, the Particle Swarm Optimization (PSO) algorithm is used to describe and simulate the behavior of a large group of entities, with similar characteristics or attributes, as they progress to achieve an optimal group, or swarm. Therefore, in this study enhanced version of PSO is proposed called the Improved PSO algorithm aims to enhance the detection of jamming attack sources over randomized mobile networks. The simulation result shows that Improved PSO algorithm in this study is faster at obtaining the location of the given mobile network at which coverage area is minimal and hence central compared to other algorithms. The Improved PSO as well was applied to a mobile network. The Improved PSO algorithm was evaluated with two experiments. In the First experiment, The Improved PSO was compared with PSO, GWO and MFO, obtained results shown the Improved PSO is the best algorithm among others to fine obtain the location for jamming attack. In Second experiment, Improved PSO was compared with PSO in mobile network environment. The obtain results prove that Improved PSO is better than PSO for obtaining the location in mobile network where coverage area is minimal and hence central.

Author 1: Ahmad K. Al Hwaitat

Author 2: Mohammed Amin Almaiah

Author 3: Omar Almomani

Author 4: Mohammed Al-Zahrani

Author 5: Rizik M. Al-Sayed

Author 6: Rania M.Asaifi

Author 7: Khalid K. Adhim

Author 8: Ahmad Althunibat

Author 9: Adeeb Alsaaidah

Keywords: Jamming attacks; Mobility; PSO; mobile networks; attacked detection; network security

Paper 81: Cross-site Scripting Research: A Review

Abstract: Cross-site scripting is one of the severe problems in Web Applications. With more connected devices which uses different Web Applications for every job, the risk of XSS attacks is increasing. In Web applications, hacker steals victims session details or other important information by exploiting XSS vulnerabilities. We studied 412 research papers on cross-site scripting, which are published in between 2002 to 2019. Most of the existing XSS prevention methods are Dynamic analysis, Static analysis, Proxy based method, Filter based method etc. We categorized existing methods and discussed solutions presented on papers and discussed impact of XSS attacks, different defensive methods and research trends in XSS attacks.

Author 1: PMD Nagarjun

Author 2: Shaik Shakeel Ahamad

Keywords: Cross-site scripting; web security; web applications; XSS attacks; mobile

Paper 82: Exploratory Study of the Effect of Obstetric Psychoprophylaxis on the Cortisol Level in Pregnant Women, Huancavelica - Perú

Abstract: Objective: To determine the cortisol level of patients who make use of the Obstetric Psychoprophylaxis (OPP) service in a first-level health center, February - May 2018. Material and methods: Descriptive, prospective, cross-sectional. Results: 68.75% of pregnant women have a stable conjugal relationship, while 25% are single and 6.25% separated, 50% have a higher education degree and 50% have a secondary education degree. Apparently, cortisol does not change according to gestational age, however, the number of OPP sessions influences the level of cortisol, so more assisted sessions means less cortisol. Conclusion: the greater exposure to obstetric psychoprophylaxis, the less levels of cortisol am (morning) in serum are observed. It could be due to psychoprophylaxis has a component that works the mental state; further studies are recommended.

Author 1: Lina Cardenás-Pineda

Author 2: Alicia Alva Mantari

Author 3: Rossibel Muñoz

Author 4: Gabriela Ordoñez-Ccora

Author 5: Tula Guerra

Author 6: Sandra Jurado-Condori

Keywords: Cortisol; obstetric; psychoprophylaxis; pregnant women; sessions; gestational; serum; observed

Paper 83: DMTree: A Novel Indexing Method for Finding Similarities in Large Vector Sets

Abstract: In a vector set, to find similarities we will compute distances from the querying vector to all other vectors. On a large vector set, computing too many distances as above takes a lot of time. So we need to find a way to compute less distance and the MTree structure is the technique we need. The MTree structure is a technique of indexing vector sets based on a defined distance. We can solve effectively the problems of finding similarities by using the MTree structure. However, the MTree structure is built on one computer so the indexing power is limited. Today, large vector sets, not fit in one computer, are more and more. The MTree structure failed to index these large vector sets. Therefore, in this work, we present a novel indexing method, extended from the MTree structure, that can index large vector sets. Besides, we also perform experiments to prove the performance of this novel method.

Author 1: Phuc Do

Author 2: Trung Phan Hong

Author 3: Huong Duong To

Keywords: MTree; DMTree; spark; distributed k-NN query; distributed range query

Paper 84: Machine Learning Model for Personalizing Online Arabic Journalism

Abstract: The paper discusses a model of generating dynamic profile for Arabic News Users, capturing user preference by analyzing his review of historical news, and recommend him news as soon as he creates account on News Mobile App, Preference is calculated based on article main keywords score, which is extracted from article headline & body as NLP (Natural Language Processing), when user reads an article, its keywords are calculated with rate of interest to his profile. Machine Learning techniques are used in the proposed model to recommend user the relevant news to his preferences and provide him personalization. The model used hybrid filtering techniques to recommend user suitable articles to his preferences, as Content-Based, Collaborative, and Demographic filtering techniques with KNN (K-nearest neighborhood). The model combined between those techniques to enhance the recommendation process, after recommendation happened, that the model tracks User behavior with the recommended articles, whether he reviewed it or not, and the actions he did on the article page to calculate his rate of interest, then dynamically updates his profile in real time with interested keywords score , thus By having User profile and defined preference, the model can help Arabic news publisher to classify users into segments, and track changes in their opinion and inclination, using observation method of read news from different user segments, and which articles attract them, thus it leads publishers to visualize their data and raise their profitability, and to follow the international trend in e-journalism industry to be a data driven organization.

Author 1: Nehad Omar

Author 2: Yasser M. K. Omar

Author 3: Fahima A. Maghraby

Keywords: Personalization; e-journalism; KNN (K-nearest Neighborhood); dynamic user profile; NLP (Natural Language Processing); data driven organization

Paper 85: Profiling Patterns in Healthcare System: A Preliminary Study

Abstract: In the 21st century, our planet revolves around data and is known as a digital earth. The astonishing growth in data has resulted in an increase in interest of Big Data Analytics to capture, store, process, analyze and visualize unprecedented amount of information. Big data has undoubtedly and will continue to shape modern information driven society where behind all the available data, there is a hidden potential to discover meaningful insights and patterns which may impact businesses in unexpected measures. The exponential growth of data is also present in the healthcare sector. In Malaysia, most employees are provided with medical benefits which includes general medical costs to hospitalization benefits and insurance coverages. With the healthcare data and information stored with the Human Resource (HR), employers could potentially analyze and identify patterns in the historical medical claims which could then help in making specific decisions to understand their employee population health and the usage of the premium coverage. Therefore, the aim of this research is to better understand the patterns presented in the employees’ healthcare data. Through the analysis and understanding of the patterns in past medical claim history, potential strategies can be proposed to allow employers to provide proactive and reactive measures to potentially help sustain medical expenditure.

Author 1: Nicholas Khin-Whai Chan

Author 2: Angela Siew-Hoong Lee

Author 3: Zuraini Zainol

Keywords: Big data analytics; data mining; descriptive analysis; healthcare; pattern profiling

Paper 86: Design and Construction of a Low-Cost Device for the Evaluation of Redox Behaviour using Lineal Voltammetry Techniques

Abstract: Electrochemical techniques have been generating great interest due to their wide range of applications and their ease of use. For this reason, in recent years’ electrochemical techniques such as cyclic voltammetry, anodic voltametric stripping, chronoamperometry and linear voltammetry have been developed. Linear voltammetry is one of the most widely used electrochemical techniques, where a voltage range is applied to a solution with the analyte and then current data is collected as a response. For this, an electrochemical cell with its 3 electrodes (working electrode, counter electrode, reference electrode) and a device for voltage control and current evaluation (potentiostat) is used. A potentiostat is an electronic device that allows the voltage or current to be regulated according to the electrochemical technique to be performed. The devices are usually very expensive due to their high precision, for this reason, our project is focused on the development of a low cost system that allows us to recognize redox systems by using linear voltammetry. our potentiostat system was able to differentiate in a redox salt (sodium chloride) from the support electrolytes (chlorohydric acid, nitric acid, sulfuric acid), allowing us to evaluate redox behavior at a cost of less than $ 40.

Author 1: Kevin Rodriguez-Villarreal

Author 2: Alicia Alva

Author 3: Daniel Ramos-Sono

Author 4: Michael Cieza Terrones

Author 5: Avid Roman-Gonzalez

Keywords: Potentiostat; blood lead; toxicity

Paper 87: Application of Piecewise Linear Approximation Method for the Estimation of Origin-Destination Matrix

Abstract: This paper presents a Mixed-Integer Programming Model for the urban freight transport planning problem through the estimation of the Origin-Destination Matrix. The Origin-Destination Matrix is used to know the pattern of travel or vehicle flow between different zones of a city and is estimated from the counting of vehicles on the routes of a road network. For the estimation of the Origin-Destination Matrix, the Entropy Maximization approach is applied. This approach is based on a non-linear optimization model. In order to overcome this difficulty, an optimization model based on the Piecewise Linear Approximation Method is proposed. To test the proposed model, an instance was built based on a road network of a real case. The proposed model obtained good results in a reduced computational time, demonstrating its usefulness for the urban freight transport planning.

Author 1: Miguel Fernández

Author 2: Enrique Lee Huamaní

Author 3: Aldo Fernández

Author 4: Avid Roman-Gonzalez

Keywords: Urban freight transport; origin-destination matrix; mixed-integer programming model; piecewise linear approximation method

Paper 88: Image Search based on Words Extracted from Others’ Utterances for Effective Idea Generation

Abstract: People often engage in brainstorming because they want to develop attractive products that involve a new idea. Consequently, many studies, methods, and systems that aim to help people generate ideas have been proposed. We developed the search websites images using search suggestions (SWISS) system, which displays images based on a word extracted from brainstorming participants’ utterances and adds additional words using an autosuggest function to stimulate idea generation. We aimed to determine whether the images searched based on the other participants’ utterances or those of other participants were more effective for idea generation. Sixteen university students participated in a brainstorming session using SWISS in two conditions. In Condition A, the participants could see the images searched based on the other participants’ utterances. These were projected onto a wide display behind each participant during the brainstorming session. In Condition B, the participants could see the images searched based on their utterances, which were displayed on a smartphone. The results indicate that the rate at which the images were related to the ideas in Condition A was higher than in Condition B. SWISS could spread the participants’ ideas through the images using an autosuggest function and extract words from the other participants’ utterances.

Author 1: Yutaka Yamaguchi

Author 2: Daisuke Shibata

Author 3: Chika Oshima

Author 4: Koichi Nakayama

Keywords: Autosuggest; brainstorming; search word; smart-phone; SWISS

Paper 89: Detection of Suicidal Intent in Spanish Language Social Networks using Machine Learning

Abstract: Suicide is a considerable problem in our population, early intervention for its prevention has a very important role, in order to counteract the number of deaths from suicide. Today, just over half of the world’s population uses social networks, where they express ideas, feelings, desires, including suicide intentions. Motivated by these factors, the main objective is the automatic detection of suicidal ideations in social networks in the Spanish language, in order to serve as a base component to alert and achieve early and specialized interventions. For this, a Spanish suicide phrase classification model has been implemented, since currently no related works in this language with a machine learning approach were found. However, there were some challenges in performing this task, such as understand-ing natural language, generating training data, and obtaining reliable accuracy in classifying these phrases. To construct our classification model, two opposite and popular types of phrase embeddings were chosen, and the most widely used classification algorithms in the literature were compared. Obtaining, as a result, the confirmation that it is possible to classify phrases with suicidal ideation in the Spanish language with good accuracy using semantic representations.

Author 1: Kid Valeriano

Author 2: Alexia Condori-Larico

Author 3: Josè Sulla-Torres

Keywords: Spanish; suicide ideation; embeddings; machine learning; phrases classification

Paper 90: General Variable Neighborhood Search for the Quote-Travelling Repairman Problem

Abstract: The Quota-Travelling Repairman Problem (Q-TRP) tries to find a tour that minimizes the waiting time while the profit collected by a repairman is not less than a predefined value. The Q-TRP is an extended variant of the Travelling Repairman Problem (TRP). The problem is NP-hard problem; therefore, metaheuristic is a natural approach to provide near-optimal solutions for large instance sizes in a short time. Currently, several algorithms are proposed to solve the TRP. However, the quote constraint does not include, and these algorithms cannot be adapted to the Q-TRP. Therefore, developing an efficient algorithm for the Q-TRP is necessary. In this paper, we suggest a General Variable Neighborhood Search (GVNS) that combines with the perturbation and Adaptive Memory (AM) techniques to prevent the search from local optima. The algorithm is implemented with a benchmark dataset. The results demonstrate that good solutions, even the optimal solutions for the problem with 100 vertices, can be reached in a short time. Moreover, the algorithm is comparable with the other metaheuristic algorithms in accordance with the solution quality.

Author 1: Ha-Bang Ban

Keywords: Q-TRP; GVNS; AM; GRASP

Paper 91: Proposed Authentication Protocol for IoT using Blockchain and Fog Nodes

Abstract: The IoT offers enormous opportunities and also brings some challenges. Authentication considered one of the main challenges introduced by IoT. IoT devices are not able to protect themselves due to there limited processing and storage capabilities. Researchers proposed authentication algorithms with either a lack of scalability or vulnerable to cyberattacks. In this paper, we propose a decentralized token-based authentication based on fog computing and blockchain. The protocol provides a secure authentication protocol using access token, ECC cryptog-raphy, and also blockchain as decentralized identity storage. The blockchain uses cryptographic identifiers, records immutability, and provenance, which allows the implementation of a decentral-ized authentication protocol. These features ensure a light and secure identity management system. We evaluate this protocol communication between controller, gateways, and devices using AVISPA/ HLPSL, and results obtained from AVISPA simulation shows that our protocol is safe based on secrecy and strong authentication criteria. The paper uses four test cases to test the Ethereum smart contract implementation to ensure the system functions properly.

Author 1: Ahmed Nabil Abdalah

Author 2: Ammar Mohamed

Author 3: Hesham A. Hefny

Keywords: Internet of Things; smart contract; blockchain; fog computing; authentication; access token

Paper 92: Balochi Non Cursive Isolated Character Recognition using Deep Neural Network

Abstract: The text recognition research in artificial intelli-gence has enabled machines not only to recognize the human spoken languages but also to interpret them. Optical character recognition is a subarea of AI that converts scanned text images into an editable document. The researchers proposed various text recognition techniques to identify cursive and connected scripts written from left to right but their correct recognition is still a challenging problem for the visual methods. The Balochi language is one of them spoken by a significant part of the world population and no research conducted on the recognition this regional language of Pakistan. In this paper, we propose a convolutional neural network based model for Balochi script recognition for non-cursive characters. Our model optimized small VGGNet model and achieved exceptional precision and speed over the state of the art methods of machine learning. We experimented and compared the proposed method with the baseline LeNet model, the results showed the proposed method improved over the baseline method with a precision of 96%. We additionally collected and processed the Balochi characters dataset and made it public to carry further research in the future.

Author 1: Ghulam Jan Naseer

Author 2: Abdul Basit

Author 3: Imran Ali

Author 4: Arif Iqbal

Keywords: Convolutional neural network; data augmentation; character recognition; cursive character recognition; detection; text segmentation

Paper 93: Recovery of Structural Controllability into Critical Infrastructures under Malicious Attacks

Abstract: The problem of controllability of networks can be seen in critical infrastructure systems which are increasingly susceptible to random failures and/or malicious attacks. The ability to recover controllability quickly following an attack can be considered a major problem in control systems. If this is not ensured, it can enable the attacker to create more disrup-tions as well as, like the electric power networks case, violate real-time restrictions and result in the control of the network degrading and its observability reducing significantly. Thus, the present paper examines structural controllability problem that has been in focus through the equivalent problem of the Power Dominating Set (PDS) introduced in the context of electrical power network control. However, the controllability optimisation problem can be seen as computationally infeasible regarding large complex networks because such problems are considered NP-hard and as having low approximability. Hence, the ability of structural controllability recoverability will be explored as per the PDS formulation, especially following perturbations in which an attacker with sufficient knowledge of the network topology is only able to completely violate the current driver control nodes of the original control network leading to a degradation of controllability of dependent nodes. The results highlight that the use of directed Laplacian matrix can be a useful approach for analysing structural controllability of a network. The simulation results show also that an increase of a connectivity probability of the distribution of links in Directed ER networks can minimise the number of driver control nodes which is highly desirable while monitoring the entire network.

Author 1: Bader Alwasel

Keywords: Structural controllability; control systems; cyber physical systems; power dominating set; recovery from attacks

Paper 94: Educational Data Mining Applications and Techniques

Abstract: Educational data mining (EDM) uses data mining techniques to analyze huge amounts of student data in the educa-tional environments. The main purpose of EDM is to analyze and solve educational issues and, consequently, improve educational processes. With the emergence of EDM applications in the educational environments, several techniques have been identified to implement these applications. This paper reviews the relevant studies in EDM including datasets and techniques used in those studies and identifies the most effective techniques. The most prevalent applications include predicting student performance, detecting undesirable student behaviors, grouping students and student modeling. These applications aim to help decision makers in the educational institutions to understand student situations, improve students’ performance, identify learning priorities for different groups of students and develop learning process. The prediction accuracy is selected as the evaluation criteria for the effectiveness of educational data mining techniques. The results show that Bayesian Network and Random Forest are the most effective techniques for predicting student performance, Social Network Analysis is the best technique for detecting undesirable student behaviors, Clustering and Social Network Analysis are the most effective techniques for grouping students and student modeling, respectively. This study recommends conducting more comprehensive and extended studies to evaluate the effectiveness of EDM techniques with an extended evaluation criteria.

Author 1: Fatima Alshareef

Author 2: Hosam Alhakami

Author 3: Tahani Alsubait

Author 4: Abdullah Baz

Keywords: Educational data mining; student performance; pre-diction; classification; clustering

Paper 95: A Development of Simulator Considering Behavioral Psychology of Japanese to Improve Evacuation Ratio in Flood

Abstract: In Japan, the natural disasters causes a lot of damages of residents. For example, the flood caused by heavy rain and house collapse due to earthquake. As you know, no one can evacuate from earthquake because it is not knowable that when the earthquake will occur. The residents, however, often have chances to evacuate from flood caused by heavy rain because there is a little time left before the flood occurs. In order to improve the evacuation ratio, the system to share the evacuation status of neighbors has been proposed. Although a survey showed that the system is so effective to improve the evacuation ratio, the number of neighbors to share the evacuation status has not been clear. The aim of this study is a development of the simulation of the residents in order to find the optimal number of the neighbors to share the evacuation status. In this paper, the simulator based on the behavioral psychology for the evacuation ratio in flood is considered. The main target in the simulation is the action of human, so the game theory is appliable. The residents, which is players in the game theory or agents in the simulator, will make decisions based on the statuses of their neighbors. In the experiments, the actual evacuation ratio can be obtained by a simulation with a premise that the residents can never know the evacuation status of their neighbors. For the future work, the optimal number of neighbors to share evacuation status should be simulated in view of the improvement of evacuation ratio in flood.

Author 1: Tatsuki Fukuda

Keywords: Simulator; game theory; flood

Paper 96: 3D Hand Gesture Representation and Recognition through Deep Joint Distance Measurements

Abstract: Hand gestures with finger relationships are among the toughest features to extract for machine recognition. In this paper, this particular research challenge is addressed with 3D hand joint features extracted from distance measurements which are then colour mapped as spatio temporal features. Further patterns are learned using an 8-layer convolutional neural network (CNN) to estimate the hand gesture. The results showed a higher degree of recognition accuracy when compared to similar 3D hand gesture methods. The recognition accuracy for our dataset KL 3DHG with 220 classes was around 94.32%. Robustness of the proposed method was validated with only available benchmark 3D skeletal hand gesture dataset DGH 14/28.

Author 1: P. Vasavi

Author 2: Suman Maloji

Author 3: E. Kiran Kumar

Author 4: D. Anil Kumar

Author 5: N. Sasikala

Keywords: Gesture recognition; 3D motion capture; deep learn-ing; joint relational distance maps

Paper 97: Overview of Fault Tolerance Techniques and the Proposed TMR Generator Tool for FPGA Designs

Abstract: The FPGA has been involved in many safety and mission-critical applications in the last few decades. FPGA designs are also critical to errors and failures due to radiations. Fault-tolerant systems should be designed to overcome the effect of faults or failure during the operation of the systems. The primary objective of any fault tolerance technique is to produce a dependable system. Fault tolerance techniques add the capability to perform proper functioning in the presence of a fault. Fault-tolerant techniques can detect the faults and correct them, or mask the faults. The overview of the most standard techniques used for FPGA designs is described in the paper. Among them, it is found that the Triple Modular Redundancy (TMR) technique is the most straight forward in terms of implementation and easy to use. The proposed TMR code generator for implementing the FPGA design is also described. These FPGA designs are written in Verilog Hardware Description Language (HDL) at the different abstraction levels.

Author 1: Abdul Rafay Khatri

Keywords: FPGA designs; fault tolerance; TMR technique; Verilog HDL

Paper 98: Metamorphic Testing of AI-based Applications: A Critical Review

Abstract: Metamorphic testing is the youngest testing ap-proach among other members of the testing family. It is de-signed to test software, which are complex in nature and it is difficult to compute test oracle for them against a given set of inputs. Metamorphic testing approach tests the software with the help of metamorphic relations that guide the tester to check if the observed output can be produced after applying a certain input. Since its first appearance, a lot of research has been done to check its effectiveness on different complex families of software applications like search engines, compilers, artificial intelligence (AI) and so on. Artificial intelligence has gained immense attention due to its successfully application in many of the computer science and even other domains like medical science, social science, economic, and so on. AI-based applications are quite complex in nature as compared to other conventional software applications and because of that they are hard to test. We have selected specifically testing of AI-based applications for this research study. Although all the researchers claim to propose the best set of metamorphic relations to test AI-based applications but that still needs to be verified. In this study, we have performed a critical review supported by rigorous set of parameters that we have prepared after thorough literature survey. The survey shows that researchers have applied metamorphic testing on applications that are either based on Genetic Algorithm (GA) or Machine Learning (ML). Our analysis has helped us identifying the strengths and weaknesses of the proposed approaches. Research still needs to be done to design a generalized set of metamorphic rules that can test a family of AI applications rather than just one. The findings are supported by strong arguments and justified with logical reasoning. The identified problem domains can be targeted by the researchers in future to further enhance the capabilities of metamorphic testing and its range of applications.

Author 1: Muhammad Nadeem Khokhar

Author 2: Muhammad Bilal Bashir

Author 3: Muhammad Fiaz

Keywords: Metamorphic testing; metamorphic relation; test oracle problem; artificial intelligence; genetic algorithm; machine learning

Paper 99: On-Road Deer Detection for Advanced Driver Assistance using Convolutional Neural Network

Abstract: Animal-vehicle collision (AVC) is a major concern in road safety that affects human life, properties, and wildlife. Most of the collisions happen with large animals especially deer that enters the road suddenly. Furthermore, the threat is even more alarming in poor visibility conditions such as night-time, fog, rain, etc. Therefore, it is vital to detect the presence of deer on roadways to mitigate the severity of deer-vehicle collision (DVC). This paper presents an efficient methodology to detect deer on roadways both during the day and night-time conditions using deep learning framework. A two-class CNN model differentiating a deer from its background is developed. The background will have a few classes of objects such as motorcycles, cars, and trees which are frequently encountered on roadways. A self-constructed dataset with both RGB and thermal images is used to train the CNN model. Sliding window technique is used to localize the spatial region of deer in an image. The performance of the proposed CNN model is compared with state-of-the art classifiers and pre-trained CNN models and the results validate its effectiveness.

Author 1: W Jino Hans

Author 2: V Sherlin Solomi

Author 3: N Venkateswaran

Keywords: Computer vision; animal detection; deep learning; Animal Vehicle Collision (AVC)

Paper 100: Clustering Nodes and Discretizing Movement to Increase the Effectiveness of HEFA for a CVRP

Abstract: A Capacitated Vehicle Routing Problem (CVRP) is an important problem in transportation and industry. It is challenging to be solved using some optimization algorithms. Unfortunately, it is not easy to achieve a global optimum solution. Hence, many researchers use a combination of two or more optimization algorithms, which based on swarm intelligence methods, to overcome the drawbacks of the single algorithm. In this research, a CVRP optimization model, which contains two main processes of clustering and optimization, based on a discrete hybrid evolutionary firefly algorithm (DHEFA), is proposed. Some evaluations on three CVRP cases show that DHEFA produces an averaged effectiveness of 91.74%, which is much more effective than the original FA that gives mean effectiveness of 87.95%. This result shows that clustering nodes into several clusters effectively reduces the problem space, and the DHEFA quickly searches the optimum solution in those partial spaces.

Author 1: Ubassy Abdillah

Author 2: Suyanto Suyanto

Keywords: Swarm intelligence; capacitated vehicle routing problem; firefly algorithm; differential evolution; hybrid evolution-ary firefly algorithm

Paper 101: Development of a Practical Tool in Pick-and-Place Tasks for Human Workers

Abstract: We introduce smart hand, a practical tool for human workers in pick-and-place tasks. It is developed to avoid picking up the wrong thing from one location or place the things in an unexpected location. Smart hand features sensors (e.g., imaging sensors, motion sensors) to sense the world and offers suggestions or aid based on the sensed results when a human worker is performing a pick-and-place task. A smart hand prototype is made in the study. In our design, the smart hand has an RGB-D sensor and an inertial measurement unit (IMU). RGB-D sensor is used to do object detection and distance/position estimation while IMU is used to track the motion of the smart hand. An experiment is conducted to compare the two working conditions that a subject performs the pick-and-place tasks with or without the smart hand. The experiment results proved that the smart hand can avoid human errors in pick-and-place tasks.

Author 1: Yunan He

Author 2: Osamu Fukuda

Author 3: Daisuke Sakaguchi

Author 4: Nobuhiko Yamaguchi

Author 5: Hiroshi Okumura

Author 6: Kohei Arai

Keywords: Pick-and-place task; human-robot collaboration; cognitive system; hand tools

Paper 102: Clone Detection Techniques for JavaScript and Language Independence: Review

Abstract: Code clone detection is an active field of study in computer science. Despite its rich history, it lacks focus on web scripting languages. Due to the expansion of web applications and web development amongst developers of varying education and experience levels, they inevitably resort to cloning through out the web. The spread of code clones is further increased by websites like StackOverflow and GitHub. In this paper, we will be focusing on clone detection research done to target clones in JavaScript code and discuss its areas of concern. Also, we will summarize language independent research done and possibility of its application on JavaScript and web applications.

Author 1: Danyah Alfageh

Author 2: Hosam Alhakami

Author 3: Abdullah Baz

Author 4: Eisa Alanazi

Author 5: Tahani Alsubait

Keywords: Clone detection; code clones; JavaScript; language independent clone detection; web applications

Paper 103: A Multi-Criteria Recommendation Framework using Adaptive Linear Neuron

Abstract: Recent developments in the field of recommender systems have led to a renewed interest in employing some of the sophisticated machine learning algorithms to combine multiple characteristics of items during the process of making recom-mendations. Considerable number of research papers have been published on multi-criteria recommendation techniques. Most of these studies have focused only on using some basic statistical methods or simply by extending the similarity computation of the traditional heuristic-based techniques to model the system. Researchers have not treated the uncertainty that exists about the relationship between multi-criteria modelling approaches and effectiveness of some of the complex and powerful machine learning techniques; in fact, no previous study has investigated the role of artificial neural networks to design and develop the system using aggregation function approach. This paper seeks to remedy these challenges by analysing the performance of multi-criteria recommender systems, modelled by integrating an adaptive linear neuron that was trained using delta rule, and asymmetric sin-gular value decomposition algorithms. The proposed model was implemented, trained and tested using a multi-criteria dataset for recommending movies to users based on action, story, direction, and visual effects of movies. Taken together, the empirical results of the study suggested that there is a strong association between artificial neural networks and the modelling approaches of multi-criteria recommendation technique.

Author 1: Mohammed Hassan

Author 2: Mohamed Hamada

Author 3: Saratu Yusuf Ilu

Keywords: Multi-criteria recommender systems; adaptive linear neuron; artificial neural network; singular value decomposition; prediction accuracy

Paper 104: Introducing the Urdu-Sindhi Speech Emotion Corpus: A Novel Dataset of Speech Recordings for Emotion Recognition for Two Low-Resource Languages

Abstract: Speech emotion recognition is one of the most active areas of research in the field of affective computing and social signal processing. However, most research is directed towards a select group of languages such as English, German, and French. This is mainly due to a lack of available datasets in other languages. Such languages are called low-resource languages given that there is a scarcity of publicly available datasets. In the recent past, there has been a concerted effort within the research community to create and introduce datasets for emotion recognition for low-resource languages. To this end, we introduce in this paper the Urdu-Sindhi Speech Emotion Corpus, a novel dataset consisting of 1,435 speech recordings for two widely spoken languages of South Asia, that is Urdu and Sindhi. Furthermore, we also trained machine learning models to establish a baseline for classification performance, with accuracy being measured in terms of unweighted average recall (UAR). We report that the best performing model for Urdu language achieves a UAR = 65.00% on the validation partition and a UAR = 56.96% on the test partition. Meanwhile, the model for Sindhi language achieved UARs of 66.50% and 55.29% on the validation and test partitions, respectively. This classification performance is considerably better than the chance level UAR of 16.67%. The dataset can be accessed via https://zenodo.org/record/3685274.

Author 1: Zafi Sherhan Syed

Author 2: Sajjad Ali Memon

Author 3: Muhammad Shehram Shah

Author 4: Abbas Shah Syed

Keywords: Speech emotion recognition; affective computing; social signal processing

Paper 105: Identifying Muscle Strength Imbalances in Athletes using Motion Analysis Incorporated with Sensory Inputs

Abstract: Movement analysis is one of the commonly used methods in the context of physiotherapy to identify dysfunctions in the human musculoskeletal system. The overhead squat is a popular movement pattern that is also approved by NASM (National Academy of Sports Medicine of USA) among the various movement patterns that are used to identify muscle dysfunctions. It is commonly used to draw conclusions on an athlete’s muscle imbalance in the clinical field based on observed compensations of the movement pattern. It is used by trainers as well as fitness enthusiasts to routinely assess their movement patterns. The correct execution of movements in every athlete is crucial since the incorrect bio-mechanics can result in injuries that would take a considerable amount of time to recover through rehabilitation. Thus, there is a need to evaluate injury risks accurately. The primary purpose of this research is to propose a method of detecting muscle imbalances in collegiate athletes with the aid of a low-cost motion tracking device. This proposed method facilitates the detection of muscle imbalances in both upper-body as well as lower-body during the execution of the overhead squat.

Author 1: Sameera S. Vithanage

Author 2: Maneesha S. Ratnadiwakara

Author 3: Damitha Sandaruwan

Author 4: Shiromi Arunathileka

Author 5: Maheshya Weerasinghe

Author 6: Chathuranga Ranasinghe

Keywords: Musculoskeletal imbalance; movement analysis; motion tracking; injury prevention

Paper 106: On the Recovery of Terrestrial Wireless Network using Cognitive UAVs in the Disaster Area

Abstract: Natural disasters such as earthquakes, floods and fires may cause the existing wireless network infrastructure to collapse, leaving behind several disconnected network parts. UAVs could help to establish communication between these disconnected parts using their ability to hover and fly across the affected region. However, UAV deployment faces several problems, including how many UAVs would be sufficient and where they could be placed. Such problems can be addressed centrally in a situation with verified information about the segmented network, such as the number of disconnected parts, the number of nodes in each part and the location of each node. However, a damaged network with unknown information (which is mostly the case) requires a distributed networking establishment mechanism. Therefore, this paper proposes an algorithm to restore connectivity among the disconnected parts of the damaged network. Cognitive radio-based UAVs (CR-UAVs) fly into the affected area and try to connect the various parts of the damaged network using the pro-posed algorithm. The main objective of the proposed algorithm is to connect the different disconnected parts of the broken network with the fewest possible UAVs in the least possible time. The results of the MATLAB simulation illustrate the significance of the proposed algorithm in terms of the number of UAVs used and the total distance they fly.

Author 1: Najam Ul Hasan

Author 2: Prajoona Valsalan

Author 3: Umer Farooq

Author 4: Imran Baig

Keywords: Cognitive radio networks; spectrum allocation; sen-sor network

Paper 107: Arabic Word Recognition System for Historical Documents using Multiscale Representation Method

Abstract: In the last decades, huge efforts have been made to develop automated handwriting recognition systems. The task of recognition usually involves several complex processes includ-ing image pre-processing, segmentation, features extracting and matching. This task usually gets harder by processing historical documents as they involve skews, document degradation and structure noise. Although, the success that has been achieved in English language, the recognition of handwritten Arabic still constitutes a major challenge for many reasons. The characteristic of Arabic language, as a Semitic language, differs from other languages (e.g., European languages) in several aspects such as complex structure, implicit characters, concatenation and, writing styles and direction. This work proposes a full recognition system for the task of word recognition from from Arabic historical documents. In the proposed system, a novel feature extraction method is presented to define robust features from Arabic words. Prior Feature extraction, each input image is pre-processed and segmented resulting in segmented words. After that, the features of each word/sub-word are defined based on Multiscale Convexity Concavity(MCC) analysis of contour word shape. For feature matching, a circular shift method is proposed to burn the computational cost instead of using traditional dynamic time warping (DTW) which exhibits high computational cost. Finally, the proposed algorithm has been evaluated under well-known dataset, namely, Ibn Sina, and showed high performance for historical documents with low computational cost.

Author 1: Said Elaiwat

Author 2: Marwan Abu-Zanona

Keywords: Word recognition; multiscale convexity concavity analysis; historical documents; dynamic time warping

Paper 108: An Improved CoSaMP Multiuser Detection for Uplink Grant Free NOMA System

Abstract: Non-Orthogonal Multiple Access (NOMA) is the most prominent technology that enhances massive connectivity and spectral efficiency in 5G cellular communication. It provides services to the multi-users in time, frequency, and code domain with significant power level. Message Passing Algorithm (MPA) detection in a multi-user uplink grant-free system requires user activity information at the receiver that makes it impractical. To circumvent this problem, (MPA) is combined with Compressed Sensing (CS) based detection which not only detects the user activity but also the signal data. However, the Compressive Sampling Matching pursuit (CoSaMP) algorithm uses Zero Forcing (ZF) detector to estimate the signal but its performance degrades with increment in Signal to Noise Ratio (SNR). Therefore, Minimum Mean Square Error (MMSE) detector in CoSaMP algorithm is deployed in this paper that enhances detection accuracy and BER performance. The simulation results validate that the proposed algorithm attains better performance than MPA and conventional CoSaMP algorithm in high SNR.

Author 1: Saifullah Adnan

Author 2: Yuli Fu

Author 3: Jameel Ahmed Bhutto

Author 4: Junejo Naveed Ur Rehman

Author 5: Raja Asif Wagan

Author 6: Abbas Ghulam

Keywords: MMSE; multi-user detection; CoSaMP; NOMA; MPA; SNR