IJACSA Volume 10 Issue 1 - thesai.org

IJACSA Volume 10 Issue 1

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.

View Full Issue

Paper 1: A Dynamic Partitioning Algorithm for Sip Detection using a Bottle-Attachable IMU Sensor

Abstract: Hydration tracking technologies are a promising tool for improving health outcomes across a variety of populations. As a non-wearable solution that is reconfigurable across containers, bottle-attachable inertial measurement unit (IMU) sensors offer numerous advantages versus alternative tracking approaches. This paper proposes a novel dynamic temporal partitioning and classification algorithm for spotting drinks within the streaming data generated by such sensors. By exploiting the distinguishing characteristics of the container’s estimated inclination during drinking, the algorithm identifies candidate drink intervals for subsequent classification using a Threshold-Merge-Discard framework. The proposed approach is benchmarked against a slight variation of a previously introduced sliding window classifier for a series of experiments replicating the intended use case of the device. The new algorithm is shown to increase the true-positive detection rate by 23.7%, while reducing the number of required classification operations by more than an order of magnitude.

Author 1: Henry Griffith

Author 2: Yan Shi

Author 3: Subir Biswas

Keywords: Hydration management; online activity classification; dynamic time windowing; inertial measurement unit sensors

Paper 2: Linking Context to Data Warehouse Design

Abstract: Data warehouses are now widely used for analysis and decision support purposes. The availability of software solutions, which are more and more user-friendly and easy to manipulate has made it possible to extend their use to end users who are not specialists in the field of business intelligence. The purpose of this article is to provide an approach that assists non-expert users in the data warehouse design process and integrates their contextual data. As well as to provide a method that assists non-expert users in data warehouse design process while incorporating their contextual data. Our proposal consists of a context model and a comprehensive Data Warehouse construction method that attaches the context to data warehouses and uses it to produce customized data marts adapted to the decision makers context.

Author 1: Aadil Bouchra

Author 2: Kzaz Larbi

Author 3: Ait Wakrime Abderrahim

Author 4: Sekkaki Abderrahim

Keywords: Business intelligence; data warehouse; context; data mart

Paper 3: Many-Objective Cooperative Co-evolutionary Linear Genetic Programming Applied to the Automatic Microcontroller Program Generation

Abstract: In this article, a methodology for the generation of programs in assembly language for microcontroller-based systems is proposed, applying a many-objective cooperative co-evolutionary linear genetic programming based on the decomposition of a program into segments, which evolve simultaneously, collaborating with each other in the process. The starting point for the program generation is a table of input/output examples. Two methods of fitness evaluation are also proposed. When the objective is to find a binary combination, the authors propose fitness evaluation with an exhaustive search for the output of each bit of the binary combination in the genetic program. On the other hand, when the objective is to generate specific variations of the logical values in the pins of the microcontroller’s port, the authors propose calculating the fitness, comparing the timing diagrams generated by the genetic program with the desired timing diagrams. The methodology was tested in the generation of drivers for the 4x4 matrix keyboard and character LCD module devices. The experimental results demonstrate that for certain tasks, the use of the proposed method allows for the generation of programs capable of competing with programs written by human programmers.

Author 1: Wildor Ferrel Serruto

Author 2: Luis Alfaro

Keywords: Many-objective optimization; cooperative coevolution; linear genetic programming; program synthesis; microcontroller-based systems

Paper 4: Cookies and Sessions: A Study of what they are, how they can be Stolen and a Discussion on Security

Abstract: Cookies and sessions are common and vital to a person’s experience on the Internet. The use of cookies was originally used to overcome a memoryless protocol while using a tiny amount of the system’s resources. Cookies make for a cohesive experience when shopping online, enjoying customized content, and even receiving personalized advertisements when casually surfing the Web. However, by design, cookies lack security. Our research begins by giving a background of cookies and sessions. It then introduces what session hijacking is, and a lab was constructed to test and show how a cookie can be stolen and replayed to gain authenticated access. Finally, the paper presents various countermeasures for common attacks and tools checking for authentication cookies vulnerabilities.

Author 1: Young B. Choi

Author 2: Yin L. Loo

Author 3: Kenneth LaCroix

Keywords: AED; ARP spoofing; cookies; CSP; CSRF; HSTS; man-in-the-middle attack; newton; session hijack; web session; XSS

Paper 5: Categorical Grammars for Processes Modeling

Abstract: The diversity and heterogeneity of real-world systems makes it impossible to naturally model them only with existing modeling languages. For this reason, models are often constructed using domain specific modeling languages as metamodels, which must themselves be specified by meta-metamodels. In this paper we present a new approach, based on the category theory, to specify metamodels. A grammar for modeling processes (PN, CSP, EPC, etc.) syntactically defines processes and then presents a set of reaction rules that model the behavior of the system. We will see that the categorical sketch is sufficiently expressive to be able to support the constructions needed to visually define the syntax of a graphical modeling language. The category theory also provides appropriate structures to model the behavioral rules of a real system.

Author 1: Daniel-Cristian Craciunean

Keywords: Process modeling; metamodel; modeling grammars; categorical grammars; category theory; categorical sketch

Paper 6: Ant Colony Optimization of Interval Type-2 Fuzzy C-Means with Subtractive Clustering and Multi-Round Sampling for Large Data

Abstract: Fuzzy C-Means (FCM) is widely accepted as a clustering technique. However, it cannot often manage different uncertainties associated with data. Interval Type-2 Fuzzy C-Means (IT2FCM) is an improvement over FCM since it can model and minimize the effect of uncertainty efficiently. However, IT2FCM for large data often gets trapped in local optima and fails to find optimal cluster centers. To overcome this challenge an Ant Colony-based Optimization (ACO) is proposed. Another challenge encountered is determining the number of clusters to perform clustering. Subtractive clustering (SC) is an efficient technique to estimate appropriate number of clusters. Though for large datasets the convergence rate of ACO and SC becomes high and thus, it becomes challenging to cluster data and evaluate correct number of clusters. To encounter the challenges of large dataset, Multi-Round Sampling (MRS) technique is proposed. IT2FCM-ACO with SC and MRS technique performs clustering on subsets of data and determines suitable cluster centers and cluster number. The obtained clusters are then extended to the entire dataset. This eliminates the need for IT2FCM to work on the complete dataset. Thus, the objective of this paper is to optimize IT2FCM using ACO algorithm and to estimate the optimal number of clusters using SC while employing MRS to handle the challenges of voluminous data. Results obtained from several clustering evaluation measures shows the improved performance of IT2FCM-ACO-MRS compared to ITFCM-ACO and IT2FCM. Speed up for different sample size of dataset is computed and is found that IT2FCM-ACO-MRS is ≈1–5 times faster than IT2FCM and IT2FCM-ACO for medium datasets whereas for large datasets it is reported to be ≈ 30–150 times faster.

Author 1: Sana Qaiyum

Author 2: Izzatdin Aziz

Author 3: Jafreezal Jaafar

Author 4: Adam Kai Leung Wong

Keywords: Interval type-2 fuzzy c-means; ant colony optimization; subtractive clustering; multi-round sampling

Paper 7: Learning Deep Transferability for Several Agricultural Classification Problems

Abstract: This paper addresses several critical agricultural classification problems, e.g. grain discoloration and medicinal plants identification and classification, in Vietnam via combining the idea of knowledge transferability and state-of-the-art deep convolutional neural networks. Grain discoloration disease of rice is an emerging threat to rice harvest in Vietnam as well as all over the world and it acquires specific attention as it results in qualitative loss of harvested crop. Medicinal plants are an important element of indigenous medical systems. These resources are usually regarded as a part of culture’s traditional knowledge. Accurate classification is preliminary to any kind of intervention and recommendation of services. Hence, leveraging technology in automatic classification of these problems has become essential. Unfortunately, building and training a machine learning model from scratch is next to impossible due to the lack of hardware infrastructure and finance support. It painfully restricts the requirements of rapid solutions to deal with the demand. For this purpose, the authors have exploited the idea of transfer learning which is the improvement of learning in a new prediction task through the transferability of knowledge from a related prediction task that has already been learned. By utilizing state-of-the-art deep networks re-trained upon our collected data, our extensive experiments show that the proposed combination performs perfectly and achieves the classification accuracy of 98.7% and 98.5% on our collected datasets within the acceptable training time on a normal laptop. A mobile application is also deployed to facilitate further integrated recommendation and services.

Author 1: Nghia Duong-Trung

Author 2: Luyl-Da Quach

Author 3: Chi-Ngon Nguyen

Keywords: Medicinal Plant Classification; Grain Discoloration Classification; Transfer Learning; Deep Learning

Paper 8: Applying FireFly Algorithm to Solve the Problem of Balancing Curricula

Abstract: The problem of assigning a balanced academic curriculum to academic periods of a curriculum, that is, the balancing curricula, represents a traditional challenge for every educational institution which look for a match among students and professors. This article proposes a solution for the balancing curricula problem using an optimization technique based on the attraction of fireflies (FA) meta-heuristic. We perform a set of test and real instances to measure the performance of our solution proposal just looking to deliver a system that will simplify the process of designing a curricular network in higher education institutions. The obtained results show that our solution achieves a fairly fast convergence and finds the optimum known in most of the tests carried out.

Author 1: Jose Miguel Rubio

Author 2: Cristian L. Vidal-Silva

Author 3: Ricardo Soto

Author 4: Erika Madariaga

Author 5: Franklin Johnson

Author 6: Luis Carter

Keywords: Balanced Academic Curriculum; Attraction of Fire-flies Meta-heuristic; Optimization

Paper 9: Towards the Algorithmic Detection of Artistic Style

Abstract: The artistic style of a painting can be sensed by the average observer, but algorithmically detecting a painting’s style is a difficult problem. We propose a novel method for detecting the artistic style of a painting that is motivated by the neural-style algorithm of Gatys et. al. and is competitive with other recent algorithmic approaches to artistic style detection.

Author 1: Jeremiah W. Johnson

Keywords: Artificial intelligence; neural networks; style trans-fer; representation learning; deep learning; computer vision; ma-chine learning

Paper 10: Blockchain: Securing Internet of Medical Things (IoMT)

Abstract: The internet of medical things (IoMT) is playing a substantial role in improving the health and providing medical facilities to people around the globe. With the exponential growth, IoMT is having a huge influence in our everyday life style. Instead of going to the hospital, patient clinical related data is remotely observed and processed in a real time data system and then is transferred to the third party for future use such as the cloud. IoMT is intensive data domain with a continuous growing rate which means that we must secure a large amount of sensitive data without being tampered. Blockchain is a temper proved digital ledger which provides us peer-to-peer communication. Blockchain enables communication between non-trusting members without any intermediary. In this paper we first discuss the technology behind Blockchain then propose IoMT based security architecture employing Blockchain to ensure the security of data transmission between connected nodes.

Author 1: Nimra Dilawar

Author 2: Muhammad Rizwan

Author 3: Fahad Ahmad

Author 4: Saima Akram

Keywords: Blockchain; IoMT; peer-to-peer; security; proof of work (PoW)

Paper 11: Novel ABCD Formula to Diagnose and Feature Ranking of Melanoma

Abstract: A prototype of skin cancer detection system for melanoma diagnoses in early stages is very important. In this paper, a novel technique is proposed for Skin malignant growth identification based on feature parameters, color shading histogram, to improve the diagnosis method by optimizing the ABCD formula. Features are extracted like Shape, Statistical, GLCM texture, Color, Wavelet transform, Texture. Once the features are extracted we found the most prominent features by assigning a rank. We have calculated parameters such as sensitivity, specificity, accuracy for checking the imperceptibility and robustness of the proposed approach. Also, Correlation analysis is made between traditional and proposed TDS equation using Karl Pearson’s method.

Author 1: Reshma M

Author 2: B. Priestly Shan

Keywords: Karl Pearson’s method; gray level co-occurrence matrix (GLCM); wavelet transform; melanoma; dermoscopy

Paper 12: Minimizing Information Asymmetry Interference using Optimal Channel Assignment Strategy in Wireless Mesh Networks

Abstract: Multi-radio multi-channel wireless mesh networks (MRMC-WMNs) in recent years are considered as the prioritized choice for users due to its low cost and reliability. MRMC-WMNs is recently been deployed widely across the world but still these kinds of networks face interference problems among WMN links. One of the well-known interference issue is information asymmetry (IA). In case of information asymmetry interference the source mesh nodes of different mesh links cannot sense each other before transmitting data on the same frequency channel. This non-coordination leads to data collision and packet loss of data flow and hence degrades the network capacity. To maximize the MRMC-WMN capacity and minimize IA interference, various schemes for optimal channel assignment have been proposed already. In this research a novel and near-optimal channel assignment model called Information Asymmetry Minimization (IAM) model is proposed based on integer linear programming. The proposed IAM model optimally assigns orthogonal or non-overlapping channels from IEEE 802.11b technology to various MRMC-WMN links. Through extensive simulations we show that our proposed model gives 28.31% network aggregate network capacity improvement over the existing channel assignment model.

Author 1: Gohar Rahman

Author 2: Chuah Chai Wen

Author 3: Sadiq Shah

Author 4: Misbah Daud

Keywords: Wireless mesh network; information asymmetry interference; channel assignment; integer linear programming; coordinated interference

Paper 13: Social Network Analysis of Twitter to Identify Issuer of Topic using PageRank

Abstract: Twitter as widest micro-blogging and social media proves a billion of tweets from many users. Each tweet carry its own topic, and the tweet itself is can be retweeted by other user. Social network analysis is needed to reach the original issuer of a topic. Representing topic-specific Twitter network can be done to get the main issuer of the topic with graph based ranking algorithm. One of the algorithm is PageRank, which rank each node based on number of in-degree of that node, and inversely proportional to out-degree of the other nodes that point to that node. In proposed methodology, network graph is built from Twitter where user acts as node and tweet-retweet relation as directed edge. User who retweet the tweet points to original user who tweet. From the formed graph, each node’s PageRank is calculated as well as other node properties like centrality, degree, and followers and average time retweeted. The result shows that PageRank score of node is directly proportional to closeness centrality and in-degree of node. However, the ranking with PageRank, closeness centrality, and in-degree ranking yield different ranking result.

Author 1: Sigit Priyanta

Author 2: I Nyoman Prayana Trisna

Keywords: Twitter ranking; social network analysis; graph-based algorithm; PageRank; graph centrality

Paper 14: Efficient Gabor-Based Recognition for Handwritten Arabic-Indic Digits

Abstract: In daily life, the need of automatically digitizing paper documentations and recognizing textual images is still present with existing and potential upcoming rooms for improvements, especially for languages like Arabic, which is unlike English as an instance, has more complex context and not been extensively supported by research in a such domain. As yet, the available online offline optical character recognition (OCR) systems have utilized functional techniques and achieved high performance mainly on machine printed data images. However, in case of handwritten script, the recognition task becomes highly unconstrained and much more challenging. Amongst a large verity of recognizable multi-lingual characters, handwritten digit recognition is a considerably useful task for different purposes and countless applications. In this research, the focus is on Arabic (known today as Indic or Indian) digit recognition using different proposed Gabor-based approaches in several combinations with different classification methods. The proposed approaches are trained and tested using 91120 digit samples of two independent standard databases (Arabic-Handwritten-Digits and AHDBase), allowing performance variability assessments and comparisons not only between the different combinations of features and classifiers but also between different datasets. The proposed Arabic-Indic digit recognition system achieves high recognition rates reach up to 99.87%. This research practically shows that one of the proposed approaches with significant dimensionality reduced features remains attaining a high recognition rate with low complexity time, which can be hence recommended further for online digit recognition systems.

Author 1: Emad Sami Jaha

Keywords: Digit recognition; Gabor filters; OCR; k-nearest neighbor; artificial neural networks

Paper 15: Requirements Prioritization and using Iteration Model for Successful Implementation of Requirements

Abstract: Requirements prioritization is ranking of software requirements in particular order. Prioritize requirements are easy to manage and implement while un-prioritized requirements are costly and consume much time as total estimation time of project can exceed. Because all requirements are depended on each other so total estimation time exceed when requirements wait for pre-requisite requirements. Priority of requirement also increases when other requirements wait for it but assigning low priority to needed requirements will delaying the project. Iteration model is software engineering (SE) process model in which all requirements are not developed at one time but are developed in phases. Only sufficient information or sub-requirements of particular user requirement (UR) can be needed for other user requirements (URs) so by implementing only the sufficient requirements in first phase will reduce waiting time. Hence total estimation time of the project will also reduce. In this research work, iteration model approach is used during prioritization to reduce total estimation time of project and to assure timely delivery of project. From the results it is concluded that not all sub-requirements of particular UR get same priority, but there are only few requirements that are important and should be given more priority.

Author 1: Muhammad Yaseen

Author 2: Noraini Ibrahim

Author 3: Aida Mustapha

Keywords: Requirements prioritization; iteration model; user requirements; spanning trees; directed acyclic graph

Paper 16: Individual Readiness for Change in the Pre-Implementation Phase of Campus Enterprise Resource Planning (ERP) Project in Malaysian Public University

Abstract: In recent years, the current globalization has revolutionized transformed the landscape and ecosystem of the institution of higher education were demanding that the university transition from legacy system to Enterprise Resource Planning (ERP) system on enhancing university competitiveness. This shift requires the entire organization to be ready for change as early as the pre-implementation phase to ensure the successful implementation of ERP and resistance among staff is reduced. Past studies related to readiness for change are more focused on the ERP implementation phase for Human Resources, Finance and Manufacturing. However, studies on the individual readiness for change (IRFC) among public university staff in the pre-implementation phase are limited especially in Malaysian. Therefore, this study aims to analyze the IRFC factor among public university staff by combining the theoretical and empirical results of the study. Data analysis was obtained from a questionnaire from 117 public university staff who were in the pre-implementation phase of the Campus ERP project. The findings show that appropriateness, management support, change-specific efficacy and personal valence as contributing IRFC public university staff in on pre-implementation phase of Campus ERP project. Besides that, there are 24 items representing that four factors in measuring IRFC. In the future, studies can be done in a variety of perception such as students and other ERP systems such as Human Resource System and Financial System which are also a core system for the university. Additionally, this study leads for further study in implementation and post-implementation phase of the Campus ERP project.

Author 1: Adiel Harun

Author 2: Zulkefli Mansor

Keywords: Campus ERP; ERP pre-implementation phase; individual readiness for change; IRFC

Paper 17: Data Categorization and Model Weighting Approach for Language Model Adaptation in Statistical Machine Translation

Abstract: Language model encapsulates semantic, syntactic and pragmatic information about specific task. Intelligent systems especially natural language processing systems can show different results in terms of performance and precision when moving among genres and domains. Therefore researchers have explored different language model adaptation strategies in order to overcome effectiveness issue. There are two main categories in language model adaptation techniques. The first category includes the techniques that based on the data selection where task-oriented corpus can be extracted and used to train and generate models for specific translations. While the second category focuses on developing a weighting criterion to assign the test data to specific model corpus. The purpose of this research is to introduce language model adaptation approach that combines both categories (data selection and weighting criterion) of language model adaptation. This approach applies data selection for specific-task translations by dividing the corpus into smaller and topic-related corpora using clustering process. We investigate the effect of different approaches for clustering the bilingual data on the language model adaptation process in terms of translation quality using the Europarl corpus WMT07 that includes bilingual data for English-Spanish, English-German and English-French. A mixture of language models should assign any given data to the right language model to be used in the translation process using a specific weighting criterion. The proposed language model adaptation has achieved better translation quality compare to the baseline model in Statistical Machine Translation (SMT).

Author 1: Mohammed AbuHamad

Author 2: Masnizah Mohd

Keywords: Language model adaptation; Statistical machine translation; clustering

Paper 18: Development of Fire Fighting Robot (QRob)

Abstract: Fire incident is a disaster that can potentially cause the loss of life, property damage and permanent disability to the affected victim. They can also suffer from prolonged psychological and trauma. Fire fighters are primarily tasked to handle fire incidents, but they are often exposed to higher risks when extinguishing fire, especially in hazardous environments such as in nuclear power plant, petroleum refineries and gas tanks. They are also faced with other difficulties, particularly if fire occurs in narrow and restricted places, as it is necessary to explore the ruins of buildings and obstacles to extinguish the fire and save the victim. With high barriers and risks in fire extinguishment operations, technological innovations can be utilized to assist firefighting. Therefore, this paper presents the development of a firefighting robot dubbed QRob that can extinguish fire without the need for fire fighters to be exposed to unnecessary danger. QRob is designed to be compact in size than other conventional fire-fighting robot in order to ease small location entry for deeper reach of extinguishing fire in narrow space. QRob is also equipped with an ultrasonic sensor to avoid it from hitting any obstacle and surrounding objects, while a flame sensor is attached for fire detection. This resulted in QRob demonstrating capabilities of identifying fire locations automatically and ability to extinguish fire remotely at particular distance. QRob is programmed to find the fire location and stop at maximum distance of 40 cm from the fire. A human operator can monitor the robot by using camera which connects to a smartphone or remote devices.

Author 1: Mohd Aliff

Author 2: Nor Samsiah Sani

Author 3: MI Yusof

Author 4: Azavitra Zainal

Keywords: Firefighting robot; compact size robot; ultrasonic sensor; flame sensor; remote control

Paper 19: Performance Investigation of VoIP Over Mobile WiMAX Networks through OPNET Simulation

Abstract: Worldwide Interoperability for Microwave Access (WiMAX) is regarded as a promising technology that can provide wireless communication because of its advantages which include, high-speed data rates, high coverage and low cost of development and maintenance. WiMAX also supports the performance of Voice over Internet Protocol (VoIP), which is expected to replace conventional circuit switched voice services. VoIP requires to accurately design of QoS configurations over WiMAX networks. This paper focuses on studying and analyzing the performance of VoIP over WiMAX mobile networks. WiMAX network and VoIP technology such as mobility, WiMAX service classes, number of nodes and VoIP codecs are studied and analyzed. WiMAX network is simulated in a different manner using the simulation program known as OPNET Modeler. Simulation results established that the service layer Unsolicited Grand Service (UGS) is more appropriate for VoIP service because it has the best standard and performance. It was also observed that the least delay and highest value of customer satisfaction rate of services is demonstrated by the G.723.1 best coding. It also maintains the minimum consumption of capacity.

Author 1: Ilyas Khudhair Yalwi Dubi

Author 2: Ravie Chandren Muniyandi

Keywords: Voice over Internet Protocol (VoIP); R-score; Worldwide Interoperability of Microwave Access (WiMAX); quality of service (QoS); OPNET 14.5

Paper 20: Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning

Abstract: Human identity recognition and protection of information security are current global concerns in this age of increasing information growth. Biometrics approach of defining identity is considered as one of the highly potential approaches due to its internal feature that is difficult to be artificially recreated, stolen and/or forgotten. The new recognition system based on finger vein is a unique method depending on physiological traits and parameters of the vein patterns for the human. Published works on finger vein identification have hitherto ignored the power of aggregating different types of features and classifiers in improving the performance of the biometric recognition system. In this paper, we developed a novel feature approach named as straight line approximator (SLA) for extending the feature space of vein pattern using a public data set SDUMLA-HMT comprising about 3,816 images of finger vein for 160 persons. Furthermore, we applied a set of extreme learning machine (ELM) and support vector machine (SVM) classifier in different kernels. Then, we used the combination rules to improve the performance of the system. The experiment result of the proposed method achieved an accuracy of 87% using (DS and GWAR) rules at rank 1, while the accuracy of DS rule 93% and GWAR rule 92% at rank 5.

Author 1: Roza Waleed Ali

Author 2: Junaidah Mohamed Kassim

Author 3: Siti Norul Huda Sheikh Abdullah

Keywords: Finger vein recognition; SLA; ELM; SVM; HOG; straight line approximate

Paper 21: OntoDI: The Methodology for Ontology Development on Data Integration

Abstract: The implementations of data integration in current days have many issues to be solved. Heterogeneity of data with non-standardization data, data conflicts between various data sources, data with a different representation, as well as semantic aspects problems are among the challenges and still open to research. Semantic data integration using ontology approach is considered as an appropriate solution to deal with semantic aspects problem in data integration. However, most methodologies for ontology development are developed to cover specific purpose and less suitable for common data integration implementation. This research offers an improved methodology for ontology development on data integration to deal with semantic aspects problem, called OntoDI. It is a continuation and improvement of the previous work about ontology development methods on agent system. OntoDI consists of three main parts, namely the pre-development, core-development and post-development, in which every part contains several phases. This paper describes the experiment of OntoDI in the electronic learning system domain. Using OntoDI, the development of ontology knowledge gives simpler phases, complete steps, and clear documentation for the ontology client. In addition, this ontology knowledge is also capable to overcome semantic aspect issues that happen in the sharing and integration process in education area.

Author 1: Arda Yunianta

Author 2: Ahmad Hoirul Basori

Author 3: Anton Satria Prabuwono

Author 4: Arif Bramantoro

Author 5: Irfan Syamsuddin

Author 6: Norazah Yusof

Author 7: Alaa Omran Almagrabi

Author 8: Khalid Alsubhi

Keywords: Data integration; methodology; ontology development; semantic issues; semantic approach

Paper 22: A New PHP Discoverer for Modisco

Abstract: MoDisco is an Eclipse Generative Modeling Technologies project (GMT Project) intended to make easier the design and building of model-based solutions that are dedicated to legacy systems Model-Driven Reverse Engineering (MDRE). It offers an open source, generic and extensible MDRE framework. Indeed, MDRE applies of Model-driven Engineering (MDE) principles to enhance traditional Reverse Engineering processes, and thus facilitate their understanding and manipulation. In the same context, the Architecture-Driven Modernization (ADM) is an OMG (Object Management Group) standard, which addresses the integration of MDA (Model-driven Architecture) and Reverse Engineering in the aim of understanding and evolving existing software assets. Thus, Modisco succeeded to stand out as the implementation reference in the MDRE and ADM field. Currently, Modisco handles only some technologies, such as Java and XML. Unfortunately, no adapted way to handle PHP (Hypertext Preprocessor) web-based projects by Modisco is available so far. This paper proposes a new model discovery tool intended for PHP language. This latter constitutes an extension for the Modisco framework that allows managing the applications assets written in PHP language. Thus, this work aims at enhancing the Modisco platform capabilities in managing more software development technologies.

Author 1: Abdelali Elmounadi

Author 2: Nawfal El Moukhi

Author 3: Naoual Berbiche

Author 4: Nacer Sefiani

Keywords: MDRE; ADM; modisco; model discovery; PHP

Paper 23: A Deep Learning Approach for Breast Cancer Mass Detection

Abstract: Breast cancer is the most widespread type of cancer among women. The diagnosis of breast cancer in its early stages is still a significant problem worldwide. The accurate classification and localization of breast mass help in the early detection of the disease, so in the last few years, a variety of CAD systems are developed to enhance breast cancer classification and localization accuracy, but most of them are fully based on handcrafted feature extraction techniques, which affect its efficiency. Currently, deep learning approaches are able to automatically learn a set of high-level features and consequently, they are achieving remarkable results in object classification and detection tasks. In this paper, the pre-trained ResNet-50 architecture and the Class Activation Map (CAM) technique are employed in breast cancer classification and localization respectively. CAM technique exploits the Convolutional Neural Network (CNN) classifiers with Global Average Pooling (GAP) layer for object localization without any supervised information about its location. According to the experimental results, the proposed approach achieved 96% Area under the Receiver Operating Characteristics (ROC) curve in the classification with 99.8% sensitivity and 82.1% specificity. Furthermore, it is able to localize 93.67% of the masses at an average of 0.122 false positives per image on the Digital Database for Screening Mammography (DDSM) data-set. It is worth noting that the pre-trained CNN is able automatically to learn the most discriminative features in the mammogram, and then fulfills superior results in breast cancer classification (normal or mass). Additionally, CAM exhibits the concrete relation between the mass located in the mammogram and the discriminative features learned by the CNN.

Author 1: Wael E. Fathy

Author 2: Amr S. Ghoneim

Keywords: Convolutional Neural Networks (CNNs); breast cancer; Global Average Pooling (GAP); mass classification and localization; Class Activation Map (CAM); Receiver Operating Characteristics Curve (ROC); Deep Learning; Computer Aided Detection And Diagnosis (CAD)

Paper 24: Optimized K-Means Clustering Model based on Gap Statistic

Abstract: Big data has become famous to process, store and manage massive volumes of data. Clustering is an essential phase in big data analysis for many real-life application areas uses clustering methodology for result analysis. The data clustered sets have become a challenging issue in the field of big data analytics. Among all clustering algorithm, the K-means algorithm is the most widely used unsupervised clustering approach as seen from past. The K-means algorithm is the best adapted for deciding similarities between objects based on distance measures with small datasets. Existing clustering algorithms require scalable solutions to manage large datasets. However, for a particular domain-specific problem the initial selection of K is still a significant concern. In this paper, an optimized clustering approach presented which is calculated the optimal number of clusters (k) for specific domain problems. The proposed approach is an optimal solution based on the cluster performance measure analysis based on gab statistic. By observation, the experimental results prove that the proposed model can efficiently enhance the speed of the clustering process and accuracy by reducing the computational complexity of the standard k-means algorithm which achieves 76.3%.

Author 1: Amira M. El-Mandouh

Author 2: Laila A. Abd-Elmegid

Author 3: Hamdi A. Mahmoud

Author 4: Mohamed H. Haggag

Keywords: Big data; mapreduce; k-means; gap statistic

Paper 25: A Trapezoidal Cross-Section Stacked Gate FinFET with Gate Extension for Improved Gate Control

Abstract: An improved trapezoidal pile gate bulk FinFET device is implemented with an extension in the gate for enhancing the performance. The novelty in the design is trapezoidal cross-section FinFET with stacked metal gate along with extension on both sides. Such improved device structure with additional process cost exhibits significant enhancement in the performance metrics specially in terms of leakage current behavior. The simulation study proves the suitability of the device for low power applications with improved on/off current ratio, subthreshold swing (SS), drain induced barrier lowering (DIBL), Gate Induced Drain Leakage (GIDL) uniform distribution of electron charge density along the channel and effects of Augur recombination within the channel.

Author 1: Sangeeta Mangesh

Author 2: Pradeep Chopra

Author 3: Krishan K. Saini

Keywords: Drain induced barrier lowering (DIBL); gate induced drain leakage (GIDL); subthreshold swing (SS); silicon on-insulator (SOI)

Paper 26: English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory

Abstract: The availability of a machine translation to translate from English-to-Arabic with high accuracy is not available because of the difficult morphology of the Arabic Language. A hybrid machine translation system between Example Based machine translation technique and Translation memory was introduced in this paper. Two datasets have been used in the experiments that were constructed by using internal medicine publications and Worldwide Arabic Medical Translation Guide Common Medical Terms sorted by Arabic. To examine the accuracy of the system constructed four experiments were made using Example Based Machine Translation system in the first, Google Translate in the second and Example Based with Google translate in the third and the fourth is the system proposed using Example Based with Translation memory. The system constructed achieved 77.17 score for the first dataset and 63.85 score for the second which were the highest score using BLEU score.

Author 1: Rana Ehab

Author 2: Eslam Amer

Author 3: Mahmoud Gadallah

Keywords: Hybrid machine translation system; translation memory; internal medicine publications; google translate; BLEU

Paper 27: Economical Motivation and Benefits of using Load Shedding in Energy Management Systems

Abstract: With declining fossil fuel consumption and rising energy demand for renewable energy, the need for integration of these highly predictable sources into the electricity system increases. At the same time, there is a rise in the price of energy, which increases the willingness of consumers to change their breed in order to reduce the costs, or at least to keep them in an acceptable level. One of the options for optimizing energy savings on the consumer side is to use the principle of demand response. This principle enables the consumer, for example, to have the necessary information to optimize the consumption of electricity so as to minimize it when the energy price is high. In view of the constantly changing conditions in the electricity system, the need for optimization is to be implemented automatically, without the necessity of users of the system. This paper main focus is the formulation and optimization of Demand Side Management using the quasi-quadratic problem (MIQP). The result of such optimization is the use of individual devices that take into account the cost of electricity, the working cycle of the installation, the requirements of the user, the systems And limitations and other input information. The method proposed which, after implementation into the individual member - the energy manager - will ensure the optimal utilization of appliances and other Set up by the witches of a clever house.

Author 1: Walid Emar

Author 2: Ghazi Suhail Al-Barami

Keywords: Demand side management; load shedding; energy management system; energy consumption

Paper 28: Radial basis Function Neural Network for Predicting Flow Bottom Hole Pressure

Abstract: The ability to monitor the flow bottom hole pressure in pumping oil wells provides important information regarding both reservoir and artificial lift performance. This paper proposes an iterative approach to optimize the spread constant and root mean square error goal of the radial basis function neural network. In addition, the optimized network is utilized to estimate this oil well pressure. Simulated experiments and qualitative comparisons with the most related techniques such as feedforward neural networks, neuro-fuzzy system, and the empirical model have been conducted. The achieved results show that the proposed technique gives better performance in estimating the flow of bottom hole pressure. Compared with the other developed techniques, an improvement of 7.14% in the root mean square error and 3.57% in the standard deviation of relative error has been achieved. Moreover, 90% and 95% accuracy of the proposed network are attained by 99.6% and 96.9% of test data, respectively.

Author 1: Medhat H A Awadalla

Keywords: Radial basis function neural network; neuro-fuzzy system; feedforward neural networks; empirical model

Paper 29: Stress Detection of the Employees Working in Software Houses using Fuzzy Inference

Abstract: In the modern era where the use of computer systems in software houses is mandatory and in various organizations has increased, it has given rise to the level of stress of employees working for hours at the system as well. Employees working in software houses are prone to have increased stress and anxiety level. It is important to detect the stress level of the employees so that various solutions can be applied in the working environment to get a better output. This paper would be beneficial for detecting the stress level of employees working on the computer using various inputs i.e. heart rate, pupil contraction, facial expressions, skin temperature, blood pressure, age and number of hours working on the computer. This research would indicate the raised level of stress of employees and this indication can be used to increase the yield of the quality of work and satisfaction of employees working in a particular organization. According to the levels of stress, within the working environment, during break hours various steps can be taken as a solution and applied during break hours of employees to ensure maximum satisfaction and the improved quality of work.

Author 1: Rabia Abid

Author 2: Nageen Saleem

Author 3: Hafiza Ammaraa Khalid

Author 4: Fahad Ahmad

Author 5: Muhammad Rizwan

Author 6: Jaweria Manzoor

Author 7: Kashaf Junaid

Keywords: Stress; fuzzy inference system; stress detection; software house

Paper 30: Repository System for Geospatial Software Development and Integration

Abstract: The integration of geospatial software components has recently received considerable attention due to the need for rapid growth of GIS application and development environments. However, finding appropriate source code components that can be incorporated into a system under development requires considerable verification to ensure the source code can work correctly. This paper therefore describes the design of a repository system that employs a new specification language, namely SpecJ2, to address the challenges involved in integrating and operating software components. SpecJ2 was designed to represent the architectural attributes of source code components and to abstract their complexity by applying the notion of separation of concerns, a key consideration when designing software systems. The results of the experiment showed that SpecJ2 is capable of defining the different architectural attributes of source code components and can facilitate their integration and interaction at run-time. Thus, SpecJ2 can classify software components according to their identified types.

Author 1: Basem Y Alkazemi

Keywords: Open-Source software; geographic information system; repository system; specification language; components integration

Paper 31: An Enhanced Concept based Approach for User Centered Health Information Retrieval to Address Presentation Issues

Abstract: The diversity of health information seekers signifies the enormous variety of information needs by numerous users. The existing health information retrieval systems failed to address the information needs of both medical expert and laymen patients. This study focused on designing an enhanced information retrieval approach using the concept based approach that would address the information needs of both medical experts and laymen patients. We evaluated and compared the performance of the proposed enhanced concept based approach with the existing approaches namely: concept based approach (CBA), query likelihood model (QLM) and latent semantic indexing (LSI) approach using Diagnosia 7, Medical Subject Heading (MeSH), Khresmoi Project 6 and Genetic Home Reference datasets. The experimental results obtained shows that the proposed enhanced concept based approach manage to score similarity scores of 1.0 (100%) in respect to maxSim values for all the runs in all the four datasets and idf weighting values of between 3.82 – 3.86 for all the runs in all the four datasets. While the existing approaches (CBA, QLM, LSI) scored the maxSim scores of 0.5 (50%) for all their runs in all the four dataset and idf weighting values of between 1.40 – 1.47 for all the four dataset, as a result of their inability to generate and display medical search results in both medical experts and layman’s forms. These results shows that the proposed enhanced concept based approach is the best approach suited to be used in addressing presentation issues.

Author 1: Ibrahim Umar Kontagora

Author 2: Isredza Rahmi A. Hamid

Author 3: Nurul Aswa Omar

Keywords: Enhanced concept based approach; existing concept based approach; medical discharge reports; medical expert form; layman’s form

Paper 32: An Efficient Scheme for Detection and Prevention of Black Hole Attacks in AODV-Based MANETs

Abstract: Mobile ad hoc network (MANET) is a set of independent mobile nodes, which connect to each other over a wireless channel without any centralized infrastructure, nor integrated security. MANET is a weak target to many Denial of Service (DOS) attacks, which seriously harms its functionality and connectivity. A black hole attack is a type of DOS attack, where the malevolent node tries to get all the data packets from a source node by sending fabricated fake route reply (RREP) packet, falsely pretending that it possesses the shortest path towards the destination node, and then drops all the packets it receives. In this paper, the AODV (Ad-hoc on-demand distance vector) routing protocol is improved by incorporating an efficient and simple mechanism to mitigate black hole attacks. Mechanism to detect black hole attacks from MANET (MDBM) uses fake route request (RREQ) packets with an unreal destination address in order to detect black hole nodes prior to the actual routing process. Simulation experiment conducted has verified the performance of the proposed detection and prevention scheme. The results demonstrated that the proposed mechanism performed well in terms of Packet Delivery Ratio, End-to-End Delay and Throughput under black hole attack.

Author 1: Muhammad Salman Pathan

Author 2: Jingsha He

Author 3: Nafei Zhu

Author 4: Zulfiqar Ali Zardari

Author 5: Muhammad Qasim Memon

Author 6: Aneeka Azmat

Keywords: Mobile ad hoc network; denial of service; black hole; fake route request packet; AD-hoc on-demand distance vector

Paper 33: Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning

Abstract: This research focuses on evaluating whether a website is legitimate or phishing. Our research contributes to improving the accuracy of phishing website detection. Hence, a feature selection algorithm is employed and integrated with an ensemble learning methodology, which is based on majority voting, and compared with different classification models including Random forest, Logistic Regression, Prediction model etc. Our research demonstrates that current phishing detection technologies have an accuracy rate between 70% and 92.52%. The experimental results prove that the accuracy rate of our proposed model can yield up to 95%, which is higher than the current technologies for phishing website detection. Moreover, the learning models used during the experiment indicate that our proposed model has a promising accuracy rate.

Author 1: Alyssa Anne Ubing

Author 2: Syukrina Kamilia Binti Jasmi

Author 3: Azween Abdullah

Author 4: NZ Jhanjhi

Author 5: Mahadevan Supramaniam

Keywords: Phishing; feature selection; classification models; random forest; prediction model; logistic regression

Paper 34: Detection of Visual Positive Sentiment using PCNN

Abstract: Many people all over the world use online social networks to express their feeling and sharing their experience, and the easiest way from their perspective is using images and videos to do so. This paper shows the utilization of two techniques (Viola et al algorithm and Pulse coupled Neural Network) in visual sentiment analysis using a hand-labeled dataset. The proposed system, which uses the PCNN with NN classifier, achieves 96% right classification, whereas Viola algorithm achieves 94% for the same dataset.

Author 1: Samar H. Ahmed

Author 2: Emad Nabil

Author 3: Amr A. Badr

Keywords: Visual sentiment analysis; pulse coupled neural network (PCNN); viola et al. algorithm

Paper 35: Rab-KAMS: A Reproducible Knowledge Management System with Visualization for Preserving Rabbit Farming and Production Knowledge

Abstract: The sudden rise in rural-to-urban migration has been a key challenge threatening food security and most especially the survival of Rabbit Farming and Production (RFP) in Sub-Saharan Africa. Currently, significant knowledge of RFP is going into extinction as evident by the drastic fall in commercial rabbit farming and production indices. Hence, the need for a system to proactively preserve RFP knowledge for future potential farmers cannot be overemphasized. To this end, knowledge archiving and management are key concepts of ensuring long-term digital storage of conceptual blueprints and specifications of systems, methods and frameworks with capacity for future updates while making such information readily accessible to relevant stakeholders on demand. Therefore, a reproducible Rabbit production’ Knowledge Archiving and Management System (Rab-KAMS) is developed in this paper. A 3-staged approach was adopted to develop the Rab-KAMS. This include a knowledge gathering and conceptualization stage; a knowledge revision stage to validate the authenticity and relevance of the gathered knowledge for its intended purpose and a prototype design stage adopting the use of unified modelling language conceptual workflows, ontology graphs and frame system. For seamless accessibility and ubiquitous purposes, the design was implemented into a mobile application having interactive end-users’ interfaces developed using XML and Java in Android 3.0.2 Studio development environment while adopting the V-shaped software development model. The qualitative evaluation results obtained for Rab-KAMS based on users’ rating and reviews indicate a high level of acceptability and reliability by the users. It also indicates that relevant RFP knowledge were correctly captured and provided in a user-friendly manner. The developed Rab-KAMS could offer seamless acquisition, representation, organization and mining of new and existing verified knowledge about RFP and in turn contributing to food security.

Author 1: Temitayo Matthew Fagbola

Author 2: Surendra Colin Thakur

Author 3: Oludayo Olugbara

Keywords: Knowledge_archiving; knowledge_management; mobile design; starUML; protégé-OWL; rabbit production; reproducibility; ubiquitous ontology

Paper 36: Innovative Automatic Discrimination Multimedia Documents for Indexing using Hybrid GMM-SVM Method

Abstract: In this paper, a new parameterization method sound discrimination of multimedia documents based on entropy phase is presented to facilitate indexing audio documents and speed up their searches in digital libraries or the retrieval of audio documents in the network, to detect speakers in purely judicial purposes and translate films into a specific language. There are four procedures of an indexing method are developed to solve these problems which are based on (parameterization, training, modeling and classification). In first step new temporal characteristics and descriptors are extracted. However, the GMM and SVM classifiers are associated with the other procedures. The MATLAB environment is the basis of the simulation of the proposed algorithm whose system performance is evaluated from a database consisting of music containing several segments of speech.

Author 1: Debabi Turkia

Author 2: Bousselmi Souha

Author 3: Cherif Adnen

Keywords: Audio indexing; classification; GMM; SVM; entropy; Speech-music discrimination

Paper 37: Towards a Gateway-based Context-Aware and Self-Adaptive Security Management Model for IoT-based eHealth Systems

Abstract: IoT-based systems have considerable dynamic behavior and heterogeneous technology participants. The corresponding threats and security operations are also complex to handle. Traditional security solutions may not be appropriate and effective in such ecosystems as they recognize and assess a limited context, they work well only with high-end and specific computing platforms, and implement manual response mechanisms. We have identified the security objectives of a potential IoT-eHealth system and have proposed a security model that can efficiently achieve them. The proposed model is a context-aware and self-adaptive security management model for IoT, in eHealth perspective that will monitor, analyze, and respond to a multitude of security contexts autonomously. As these operations are planned at the gateway level, the model exploits the advantages of computing in the Fog Layer. Moreover, the proposed model offers flexibility and open connectivity to allow any smart device or thing to be managed irrespective of their native design. We have also explained how our model can establish and serve the essential security objectives of an IoT-based environment.

Author 1: Waqas Aman

Author 2: Firdous Kausar

Keywords: Internet of things; security; self-adaptation; context awareness; ehealth

Paper 38: Securing Cognitive Radio Vehicular Ad Hoc Network with Fog Node based Distributed Blockchain Cloud Architecture

Abstract: Cognitive radio, ad hoc networks' applications are continuously increasing in wireless communication globally. In vehicles' environment, cognitive radio technology with mobile ad hoc networks (MANETs) enables vehicles to monitor the available channels and to effectively function in these frequencies through sharing ongoing information with drivers and different frameworks to enhance traffic safety on roads. To fulfill the computational storage resources’ limitations of a specific vehicle, Vehicular Cloud Computing (VCC) is used by merging VANET with cloud computing. Cloud computing requires high security and protection because authenticate users and attackers have the same rights in VCC. The security is enhanced in CRVANETs, but the distributed nature of cloud unlocks a door for dissimilar attacks, such as trust modal, data security, connection fault and query tracking attacks. This paper proposes an effective and secured blockchain scheme-based distributed cloud architecture in place of conventional cloud architecture to secure the drivers’ privacy with low cost and on-demand sensing procedure in CRVANETs ecosystem.

Author 1: Sara Nadeem

Author 2: Muhammad Rizwan

Author 3: Fahad Ahmad

Author 4: Jaweria Manzoor

Keywords: Cognitive radio vehicular ad-hoc network (CRVANET); cloud computing; blockchain; security; software defined networking (SDN); edge computing

Paper 39: Explore the Major Characteristics of Learning Management Systems and their Impact on e-Learning Success

Abstract: Today, there are many educational institutions and organizations around the world, especially the universities have adopted the e-learning and learning management system concepts because they want to enhance and support their educational process since the number of students who would like to attend universities and educational institutions is increasing. This paper has many objectives, the first one is comparing between different types of most popular learning management system (LMS) software such as Moodle and Blackboard based on their uniqueness features. The second objective is presenting the learning management systems and their benefits in e-learning. Finally, this research paper presents a proposed model, which consists of six independent variables (application and integration, communication, assessment, content, cost, and security), and one dependent variable which is e-learning success. A questionnaire has been developed and distributed to 450 respondents, and then data was collected from 418 valid questionnaires. The result showed that there is a statistically significant impact of learning management system major characteristics on e-learning success.

Author 1: Mohammad Shkoukani

Keywords: Learning management system; e-learning; educational process; Moodle; educational institutions

Paper 40: The Coin Passcode: A Shoulder-Surfing Proof Graphical Password Authentication Model for Mobile Devices

Abstract: Swiftness, simplicity, and security is crucial for mobile device authentication. Currently, most mobile devices are protected by a six pin numerical passcode authentication layer which is extremely vulnerable to Shoulder-Surfing attacks and Spyware attacks. This paper proposes a multi-elemental graphical password authentication model for mobile devices that are resistant to shoulder surfing attacks and spyware attacks. The proposed Coin Passcode model simplifies the complex user interface issues that previous graphical password models have, which work as a swift passcode security mechanism for mobile devices. The Coin Passcode model also has a high memorability rate compared to the existing numerical and alphanumerical passwords, as psychology studies suggest that human are better at remembering graphics than words. The results shows that the Coin Passcode is able to overcome the current shoulder-surfing and spyware attack vulnerability that existing mobile application numerical passcode authentication layers suffer from.

Author 1: Teoh joo Fong

Author 2: Azween Abdullah

Author 3: NZ Jhanjhi

Author 4: Mahadevan Supramaniam

Keywords: Mobile graphical password; multi-elemental passcode; shoulder-surfing proof passcode; mobile authentication model

Paper 41: Analysis and Maximizing Energy Harvesting from RF Signals using T-Shaped Microstrip Patch Antenna

Abstract: The advancement of the modern world requires catering the power crisis. New methodologies for energy harvesting were considered, but their succession in a different environment is still to explore. This paper deals with antenna designing to harvest energy from radio signals. The rummage of energy from surrounding sources is considered a harvesting of energy and it would be an alternative approach for low energy utilization. As comparatively well-known sources are considered for energy harvesting; such as wind and solar, radio frequency signal can provide continues supply of energy harvesting. Alternatively, we are getting the maximum usable energy resources which are challenging the amplitude of arriving signal, which is considered very low and the requirements for operating available antennas are proportionally higher. Using the Microstrip patch antenna limited the energy resources, because it is low profile, easy to configure, simple in design at the lowest rate. Furthermore, the combined the configuration and proposed antenna design provide the maximum energy efficiency. More simulation iterations are performed to maximize the gain of ISM band of 2.4 GHz. The operating frequency of microstrip patch antenna is 2.4GHz, which provides the gain of 7.2dB, return loss -20dB and the directivity of 7.44dB. The achieved result of source voltage is 900 mv after rectification the output voltage 2.5v. The results are efficient and suitable to overcome litter bit power crisis.

Author 1: Muhammad Salman Iqbal

Author 2: Tariq Jameel Khanzada

Author 3: Faisal .A Dahri

Author 4: Asif Ali

Author 5: Mukhtiar Ali

Author 6: Abdul Wahab Khokhar

Keywords: Microstrip patch antenna; radio frequency; energy harvesting; ISM band; gain; return loss and directivity

Paper 42: A Defected Ground based Fractal Antenna for C and S Band Applications

Abstract: Retracted: After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IJACSA`s Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

Author 1: Muhammad Noman Riaz

Author 2: Attaullah Buriro

Author 3: Athar Mahboob

Keywords: Antenna gain; voltage standing wave ratio (VSWR); coefficient of reflection; antenna radiation pattern; defected ground

Paper 43: Modeling and Control by Multi-Model Approach of the Greenhouse Dynamical System with Multiple Time-delays

Abstract: This paper presents the Internal Multi-Model Control IMMC for a multivariable discrete-time system with variable multiple delays. This work focus on the Greenhouse climate model as a multivariable time-delay system. In fact, the Greenhouse technology is an interesting subject for sustainable crop production in the regions of disadvantageous climatic conditions. In addition, high summer temperature is an important setback for successful greenhouse crop production throughout year. The main intent of this work is to present a new control of Greenhouse during summer months using the Internal Multi-Model approach. First, the plant and the model are discredited with the bilinear approximation and then they will be controlled with an Internal Multi-Model Control. The chosen system is modeled only in the summer season case. The simulation results prove the robustness of this Internal Multi-Model Control to preserve stability system despite the incertitude of the chosen model and the extern disturbances.

Author 1: Marwa Hannachi

Author 2: Ikbel Bencheikh Ahmed

Author 3: Dhaou Soudani

Keywords: Variable time-delay; multivariable systems; greenhouse system; renewable energy; multi-model approach; commutation’s technique; internal model control; discrete-time case; stability; disturbances; robustness

Paper 44: Educational Data Classification Framework for Community Pedagogical Content Management using Data Mining

Abstract: Recent years witness the significant surge in awareness and exploitation of social media especially community Question and Answer (Q&A) websites by academicians and professionals. These sites are, large repositories of vast data, pawing ways to new avenues for research through applications of data mining and data analysis by investigation of trending topics and the topics of most attention of users. Educational Data Mining (EDM) techniques can be used to unveil potential of Community Q&A websites. Conventional Educational Data Mining approaches are concerned with generation of data through systematic ways and mined it for knowledge discovery to improve educational processes. This paper gives a novel idea to explore already generated data through millions of users having variety of expertise in their particular domains across a common platform like StackOverFlow (SO), a community Q&A website where users post questions and receive answers about particular problems. This study presents an EDM framework to classify community data into Software Engineering subjects. The framework classifies the SO posts according to the academic courses along with their best solutions to accommodate learners. Moreover, it gives teachers, instructors, educators and other EDM stakeholders an insight to pay more attention and focus on commonly occurring subject related problems and to design and manage of their courses delivery and teaching accordingly. The data mining framework performs preprocessing of data using NLP techniques and apply machine learning algorithms to classify data. Amongst all, SVM gives better performs with 72.06% accuracy. Evaluation measures like precision, recall and F-1 score also used to evaluate the best performing classifier.

Author 1: Husnain Mushtaq

Author 2: Imran Siddique

Author 3: Dr. Babur Hayat Malik

Author 4: Muhammad Ahmed

Author 5: Umair Muneer Butt

Author 6: Rana M. Tahir Ghafoor

Author 7: Hafiz Zubair

Author 8: Umer Farooq

Keywords: Text mining; educational data mining; social learning; course design and delivery; technology supported learning; crowdsourced educational data mining

Paper 45: Connection Time Estimation between Nodes in VDTN

Abstract: Vehicular delay tolerant network (VDTN) is a widely used communication standard for the scenarios where no end to end path is available between nodes. Data is sent from one node to another node using routing protocols of VDTN. These routing protocols use different decision metrics. Based on these metrics, it is chosen whether to send data to connected node or find another suitable candidate. These metrices are Time to live (TTL), geographical information, destination utility, relay utility, meeting prediction, total and remaining buffer size and many other. Different routing protocols use a different combination of metrics. In this paper, a metric called “estimation-time” is introduced. The “estimation-time” is assessed at the encounter of two nodes. Nodes may decide based on that whether to send data or not. This metric can be used in routing decisions. The simulations results are above 88% which proves “estimation-time” metric is calculated correctly.

Author 1: Adnan Ali

Author 2: Muhammad Shakil

Author 3: Hamaad Rafique

Author 4: Sehrish Munawar Cheema

Keywords: Vehicular delay-tolerant network; delay tolerant network; smart transportation

Paper 46: Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Abstract: Sentiment analysis is a process of detecting and classifying sentiments into positive, negative or neutral. Most sentiment analysis research focus on English lexicon vocabularies. However, Malay is still under-resourced. Research of sentiment analysis in Malaysia social media is challenging due to mixed language usage of English and Malay. The objective of this study was to develop a cross-lingual sentiment analysis using lexicon based approach. Two lexicons of languages are combined in the system, then, the Twitter data were collected and the results were determined using graph. The results showed that the classifier was able to determine the sentiments. This study is significant for companies and governments to understand people’s opinion on social network especially in Malay speaking regions.

Author 1: Nur Imanina Zabha

Author 2: Zakiah Ayop

Author 3: Syarulnaziah Anawar

Author 4: Erman Hamid

Author 5: Zaheera Zainal Abidin

Keywords: Opinion Mining; Sentiment Analysis; Lexicon-based Approach; Cross-lingual

Paper 47: A Qualitative Analysis to Evaluate Key Characteristics of Web Mining based e-Commerce Applications

Abstract: E-Commerce applications are playing vital role by providing competitive advantage over business peers. It is important to get interesting patterns from e-commerce transactions to analyze customer experience, customer likelihood. For this, web mining based e-commerce applications are being developed for various e-businesses. There are different characteristics like user interface and interactivity, which can make these applications more efficient and effective. Well-defined criteria are needed to prioritize key characteristics of these applications. The primary intention of this work is to identify and prioritize the key characteristics and their impact on designing these applications. This paper provides a qualitative survey based evaluation and prioritization of key characteristics.

Author 1: Sohail Tariq

Author 2: Ramzan Talib

Author 3: Muhammad Kashif Hanif

Author 4: Muhammad Umar Sarwar

Author 5: Hafiz Muhammad Rashid

Author 6: Muhammad Zaman Khalid

Keywords: Web Mining; e-Commerce Applications; User Interface; Interactivity

Paper 48: A Survey of Malware Detection Techniques based on Machine Learning

Abstract: Diverse malware programs are set up daily focusing on attacking computer systems without the knowledge of their users. While some authors of these programs intend to steal secret information, others try quietly to prove their competence and aptitude. The traditional signature-based static technique is primarily used by anti-malware programs in order to counter these malicious codes. Although this technique excels at blocking known malware, it can never intercept new ones. The dynamic technique, which is often based on running the executable on a virtual environment, may be introduced by a number of anti-malware programs. The major drawbacks of this technique are the long period of scanning and the high consumption of resources. Nowadays, recent programs may utilize a third technique. It is the heuristic technique based on machine learning, which has proven its success in several areas based on the processing of huge amounts of data. In this paper we provide a survey of available researches utilizing this latter technique to counter cyber-attacks. We explore the different training phases of machine learning classifiers for malware detection. The first phase is the extraction of features from the input files according to previously chosen feature types. The second phase is the rejection of less important features and the selection of the most important ones which better represent the data contained in the input files. The last phase is the injection of the selected features in a chosen machine learning classifier, so that it can learn to distinguish between benign and malicious files, and give accurate predictions when confronted to previously unseen files. The paper ends with a critical comparison between the studied approaches according to their performance in malware detection.

Author 1: Hoda El Merabet

Author 2: Abderrahmane Hajraoui

Keywords: Malware; anti-malware; machine learning; feature extraction; feature selection; random forest; SVM; neural networks; classification

Paper 49: An Adaptive Heart Disease Behavior-Based Prediction System

Abstract: Heart disease prediction is a complex process that is influenced by several factors, including the combination of attributes leading to the possibility of heart disease and availability of these attributes in the database, an accurate selection of these attributes and determining the priority and impact of each of them on the prediction model, and finally selecting the appropriate classification technique to build the model. Most of the previous studies have used some heart disease symptoms as major risk factors to build a heart disease prediction system leading to inaccurate prediction results. The main objective of this study is to build an Adaptive Heart Disease Behavior-Based Prediction System (AHDBP) based on risk factors and behaviors that may lead to heart disease. Different classification algorithms will be deployed to get the most accurate results. 18 attributes were used to build the prediction system. The accuracy of the classification techniques was as follows: Decision Tree 90.34%, Naive Bayes 91.54%, and Neural Networks 94.91%. Neural networks can predict heart disease better than other techniques. The Chi square method has also been applied to determine the difference between the expected and the observed results, and the proposed system proved its accuracy at 86.54%.

Author 1: O. E. Emam

Author 2: A. Abdo

Author 3: Mona. M. Mahmoud

Keywords: Chest pain; risk factors; coronary; cholesterol; neural networks; decision tree; naive Bayes

Paper 50: A Novel Architecture for Information Security using Division and Pixel Matching Techniques

Abstract: The computer users have to safeguard the information which they are handling. An information hiding algorithm has to make sure that such information is undecipherable since it may have some sensitive information. This paper proposes a steganography method that conceals the message behind the image by providing the security when compared to the other existing methods. In this system, the information to be hidden is encrypted by an advanced cryptography technique. For that, initially, the data is divided by the method of arithmetic division. The information is hold on within the style of the divisor, the quotient & the remainder. The secret key is also encrypted and holds on several pixels. Then, the pixel matching algorithm is used to hide the information of the secret image in the carrier image. By this system, the embedding time is reduced when compared to different existing algorithms. In this method, different types of images are used for testing the proposed algorithm. By using this method, the peak signal to noise magnitude relation obtained is more for all the pixels present in the image.

Author 1: Abdulrahman Abdullah Alghamdi

Keywords: Information security; steganography; pixel pattern matching; key segmentation; division method

Paper 51: Method for Uncertainty Evaluation of Vicarious Calibration of Spaceborne Visible to Near Infrared Radiometers

Abstract: A method for uncertainty evaluation of vicarious calibration for solar reflection channels (visible to near infrared) of spaceborne radiometers is proposed. Reflectance based at sensor radiance estimation method for solar reflection channels of radiometers onboard remote sensing satellites is also proposed. One of examples for vicarious calibration of LISA: Line Imager Space Application onboard LISAT: LAPAN-IPB Satellite is described. Through the preliminary analysis, it is found that the proposed uncertainty evaluation method is appropriate. Also, it is found that percent difference between DN: Digital Number derived radiance and estimated TOA: Top of the Atmosphere radiance (at sensor radiance) ranges from 3.5 to 9.6 %. It is also found that the percent difference at shorter wavelength (Blue) is greater than that of longer wavelength (Near Infrared: NIR). In comparison to those facts to those of Terra/ASTER/VNIR, it is natural and reasonable.

Author 1: Kohei Arai

Author 2: Wahyudi Hasbi

Author 3: A Hadi Syafrudin

Author 4: Patria Rachman Hakim

Author 5: Sartika Salaswati

Author 6: Lilik Budi Prasetyo

Author 7: Yudi Setiawan

Keywords: Field experiment; vicarious calibration; image quality evaluation

Paper 52: Automated Knowledge Acquisition Framework for Supply Chain Management based on Hybridization of Case based Reasoning and Intelligent Agent

Abstract: Throughout the past few years, there has been notable research effort directed towards developing automated knowledge acquisition (KA) in order to automate knowledge acquisition in Supply Chain Management (SCM) applications. Several methods utilized for the automation of supply chain management involved Intelligent Agent (IA) and Case-Based Reasoning (CBR). This paper used both approaches to bring about automated knowledge acquisition in order to assist decision-making in SCM applications. With the arrival of a new case, prior cases are retrieved from the database and the potential solutions are laid down. After the completion of acquisition, case and solution outcome are analyzed and evaluated according to function similarity. Finally, after evaluating the new case along with the problem details and the chosen solution, the case is retained in the database for issues that will arise in future applications.

Author 1: Mohammad Zayed Almuiet

Author 2: Maryam Mohamad Al-zawahra

Keywords: Knowledge acquisition; supply chain management; supply chain knowledge; case-based reasoning; intelligent agent component

Paper 53: Analysis of Airport Network in Pakistan Utilizing Complex Network Approach

Abstract: Field of complex network covers different social, technological, biological, scientific collaborative work, communication networks and many others. Among these networks, transportation network is an important indicator to measure the economic growth in any country. In this study different dynamics of Airport Network in Pakistan are analyzed by the complex network methodology. Dataset of air transportation has been collected from Civil Aviation Authority of Pakistan (CAA) and formatted to accomplish the complex network requirements. The network is formed to observe its different properties and compare these with their topological counterparts. In this, network nodes are represented by Airports of Pakistan while flights between them within a week are considered as edges. The behavior of degree distribution is observed as preferential attachment of nodes, which represented that few nodes are controlling overall network which emphasizes that Airport Network in Pakistan (ANP) follows power law. Clustering coefficient displayed the network as a clustered network. Result of short average path length highlights that Airport Network in Pakistan is small-world network. Study also signified the average nearest neighbour degree node, which explained that ANP exhibited disassortative mixing in nature which states that high degree nodes (airports) tend to connect to low degree nodes (airports). Interestingly, is has been observed that it is not necessary that the most connected node is also the most central node in degree centralities.

Author 1: Hafiz Abid Mahmood Malik

Author 2: Nadeem Mahmood

Author 3: Mir Hammal Usman

Author 4: Kashif Rziwan

Author 5: Faiza Abid

Keywords: Transportation network; Airport network analysis; Complex network; Scale-free network

Paper 54: The Development of Geographic Information System using Participatory GIS Concept of Spatial Management

Abstract: Spatial management of Bandung Regency area has been regulated on Regional Regulation (PERDA), which is PERDA Bandung Regency Number 27 of 2016. Recently there are no facilities that can be used as a dissemination media of information about The Regional Layout Planning (RTRW) so that it easily accessed by the community who will utilize the space in Bandung Regency area. The information dissemination of The Regional Layout Planning is very important to avoid the mistake in the use of the area by the community. The use of Participatory GIS is conducted based on the purpose of producing an appropriate spatial plan in accordance with the established rules. The implementation of participatory GIS concept on the geographic information system of regional spatial allows all communities to participate in making decisions on the use of an area.

Author 1: Nizar Rabbi Radliya

Author 2: Rauf Fauzan

Author 3: Hani Irmayanti

Keywords: Participatory GIS; regional spatial planning; geographic information system

Paper 55: Generating a Highlight Moments Summary Video of Apolitical Event using Ontological Analysis on Social Media Speech Sentiment

Abstract: Numerous viewers choose to watch political or presidential debates highlights via TV or internet, rather than seeing the whole debate nowadays, which requires a lot of time. However, the task of making a debate summary, which can be considered neutral and does not give out a negative nor a positive image of the speaker, has never been an easy one, due to personal or political beliefs bias of the video maker. This study came up with a solution that generates highlights of a political event, based on twitter social network flow. Twitter streaming API is used to detect an event's tweets stream using specific hashtags, and detect on a timescale the extreme changes of volume of tweets, which will determine the highlight moments of our video summary at first, then a process is set up based on a group of ontologies that analyze each tweet of these moments to calculate the percentage of each sentiment’s positivity, then classify those moments by category (positive, negative or neutral).

Author 1: Abid Mehdi

Author 2: Benayad Nsiri

Author 3: Yassine Serhane

Author 4: Miyara Mounia

Keywords: Debate summary; API; hashtags; twitter; highlights moment; ontologies; sentiment analysis

Paper 56: Simulation Results for a Daily Activity Chain Optimization Method based on Ant Colony Algorithm with Time Windows

Abstract: In this paper, a new approach is presented based on ant colony algorithm with time windows in order to optimize daily activity chains with flexible mobility solutions. This flexibility is realized by temporal and spatial change of activities achieved by travellers during one day. With the injection of flexibility concept of time and locations, the requirements for such a transport system are high. However, our method has shown promising results by decreasing 10 to 20% the total travel time of travellers based on combining and comparing different transport modes including the private transport as well as the public transport and by choosing the optimal set of activities using our method.

Author 1: Imad SABBANI

Author 2: Bouattane Omar

Author 3: Domokos Eszetergar-Kiss

Keywords: Component; ant colony optimization; daily activity chain; travel salesman problem; simulation

Paper 57: EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Abstract: Since several work requires continuous alertness like efficient driving, learning, etc. efficient measurement of the alertness states through neural activity is a crucial challenge for the researchers. This work reports a practical method to investigate the alertness state from electroencephalography (EEG) of the human brain. Here, we have proposed a novel idea to monitor the brain alertness from EEG signal that can discriminate the alertness state comparing resting state with a simple statistical threshold. We have investigated two different types of mental tasks: alphabet counting & virtual driving to monitor their alertness level. The EEG signals are acquired from several participants regarding alphabet counting and virtual motor driving tasks. A 9-channel wireless EEG system has been used to acquire their EEG signals from frontal, central, and parietal lobe of the brain. With suitable preprocessing, signal dimensions are reduced by principal component analysis and the features of the signals are extracted by the discrete wavelet transformation method. Using the features, alertness states are classified using the artificial neural network. Additionally, the relative power of responsible frequency band to alertness is analyzed with statistical inference. We have found that the beta relative power increases at a significant level due to alertness which is good enough to differentiate the alertness state from the control state. It is also found that the increment of beta relative power for virtual driving is much greater than the alphabet counting mental alertness. We hope that this work will be very helpful to monitor constant alertness for efficient driving and learning.

Author 1: Md. Asadur Rahman

Author 2: Md. Mamun or Rashid

Author 3: Farzana Khanam

Author 4: Mohammad Khurshed Alam

Author 5: Mohiuddin Ahmad

Keywords: Alertness monitoring; Electroencephalography (EEG); Principal Component Analysis (PCA); Analysis of Variance (ANOVA); Discrete Wavelet Transformation (DWT); Band Relative Power; Artificial Neural Network (ANN)

Paper 58: Three Dimensional Agricultural Land Modeling using Unmanned Aerial System (UAS)

Abstract: Nowadays, the unmanned aerial vehicles (UAVs) drones are mostly used in civil and military fields for security and monitoring purposes. They are also involved in the development of electronics communications and navigation systems. The UAVs are the aerial vehicles with a built-in power system having capability of controlling by a remote control system or leads to fly automatically. Rapid increase in their use due to sensors mobility in its small size that becomes the UAVs to fly at lower altitude and their significant contributions to the image processing studies, where the photogrammetric surveys in small scale areas are given importance for landslide and erosion monitoring. This paper is going to consider agriculture activities like detecting crop diseases, finding crop patterns and conduct small scale agriculture policies for study and research. In our study, the UAV drone is used for the image data collection purpose and structure form motion (SfM), algorithmic approach is utilized for producing the volumetric structure or 3-D structure of images. These 3-dimensional structures are further used for building information modeling systems and performing different operations like image classification, enhancement and segmentation. Our approach highlights better and efficient results than others agriculture images approaches captured by UAVs at high altitude.

Author 1: Faisal Mahmood

Author 2: Khizar Abbas

Author 3: Asif Raza

Author 4: Muhammad Awais Khan

Author 5: Prince Waqas Khan

Keywords: Image processing; structure from motion (SFM); unmanned aerial system (UAS); unmanned aerial vehicles (UAVs); camera calibration; change detection

Paper 59: An Efficient Algorithm for Enumerating all Minimal Paths of a Graph

Abstract: The enumeration of all minimal paths between a terminal pair of a given graph is widely used in a lot of applications such as network reliability assessment. In this paper, we present a new and efficient algorithm to generate all minimal paths in a graph G(V, E). The algorithm proposed builds the set of minimal paths gradually, starting from the source nodes. We present two versions of our algorithm; the first version determines all feasible paths between a pair of terminals in a directed graph without cycle, and this version runs in linear time O(|V| + |E|). The second version determines all minimal paths in a general graph (directed and undirected graph). In order to show the process and the effectiveness of our method, an illustrative example is presented for each case.

Author 1: Khalid Housni

Keywords: Minimal path; network reliability; linked path struc-ture; recursive algorithm

Paper 60: Help Tetraplegic People by Means of a Computational Neuronal Control System

Abstract: In the present document we present an Interface called BrainMouse where its main task is to help people with motor disabilities specially tetraplegic or quadriplegic people that they can move the mouse of the computer by means of blinking or any neural response. The Interface uses the data obtained from a neuronal system which is responsible for taking reliable readings of the electrical signals generated in the human brain, through non-intrusive neuronal interfaces. The recorded data is used by the BrainMouse Interface so that the mouse can perform functions such as an up, down, left lateral, right lateral, left click, right click and double click. Thus, this interface has all the options that a conventional mouse would have.

Author 1: Jaime Moreno

Author 2: Oswaldo Morales

Author 3: Ricardo Tejeida

Author 4: America Gonzalez

Author 5: Dario Rodriguez

Keywords: Computational applications; Computer Human In-teraction; Neuronal Interactions; Tetraplegic or Quadriplegic Peo-ple; Neural Response

Paper 61: Auto-Scaling Approach for Cloud based Mobile Learning Applications

Abstract: In the last decade, mobile learning applications have attracted a significant amount of attention. Huge investments have been made to develop educational applications that can be implemented on mobile devices. However, mobile learning applications have some limitations, such as storage space and battery life. Cloud computing provides a new idea to solve some limitations of mobile learning applications. However, there are other limitations, like scalability, that must be solved before mobile cloud learning can become completely operational. There are two main problems with scalability. The first occurs when the application server’s performance declines due to an increase in the number of requests, which affects usability. The second is that a decrease in the number of requests makes most application servers idle and therefore wastes money. These two problems can be avoided or minimized by provisioning auto-scaling techniques that permit the acquisition and release of resources dynamically to accommodate demand. In this paper, we propose an intelligent neuro-fuzzy reinforcement learning approach to solve the scalability problem in mobile cloud learning applications, and evaluate the proposed approach against some of the existing approaches via MATLAB. The large state space and long training time required to find the optimal policy are the main problems of reinforcement learning. We use fuzzy Q-learning to solve the large state space problem by grouping similar variables in the same state; there is then no need to use large look-up tables. The use of parallel learning agents reduces the training time needed to determine optimal policies. The experimental results prove that the proposed approach is able to increase learning speed and reduce the training time needed to determine optimal policies.

Author 1: Amani Nasser Almutlaq

Author 2: Dr. Yassine Daadaa

Keywords: Auto-scaling; reinforcement learning; fuzzy Q-learning

Paper 62: Implementation, Verification and Validation of an OpenRISC-1200 Soft-core Processor on FPGA

Abstract: An embedded system is a dedicated computer system in which hardware and software are combined to per-form some specific tasks. Recent advancements in the Field Programmable Gate Array (FPGA) technology make it possible to implement the complete embedded system on a single FPGA chip. The fundamental component of an embedded system is a microprocessor. Soft-core processors are written in hardware description languages and functionally equivalent to an ordinary microprocessor. These soft-core processors are synthesized and implemented on the FPGA devices. In this paper, the OpenRISC 1200 processor is used, which is a 32-bit soft-core processor and written in the Verilog HDL. Xilinx ISE tools perform synthesis, design implementation and configure/program the FPGA. For verification and debugging purpose, a software toolchain from GNU is configured and installed. The software is written in C and Assembly languages. The communication between the host computer and FPGA board is carried out through the serial RS- 232 port.

Author 1: Abdul Rafay Khatri

Keywords: FPGA Design; HDLs; Hw-Sw Co-design; Open-RISC 1200; Soft-core processors

Paper 63: Developing an Adaptive Language Model for Bahasa Indonesia

Abstract: A language model is one of the important compo-nents in a speech recognition system. It is commonly developed using a statistical method called n-gram. However, a standard n-gram cannot be used for general domains with so many am-biguous semantics of sentences. This paper focuses on developing an adaptive n-gram language model for Bahasa Indonesia. First, a text corpus of ten million distinct sentences is crawled from hundreds of websites of news, magazines, personal blogs, and writing forums. The text corpus is then used to construct an adaptive language model using Latent Dirichlet Allocation (LDA) with Collapsed Gibbs Sampling (CGS) training method. Compare to the standard n-gram, the adaptive language model gives a better performance in the word selection to produce the best sentence.

Author 1: Satria Nur Hidayatullah

Author 2: Suyanto

Keywords: Adaptive Language Model; Bahasa Indonesia; Col-lapsed Gibbs Sampling; Latent Dirichlet Allocation; text corpus

Paper 64: Community Detection in Dynamic Social Networks: A Multi-Agent System based on Electric Field

Abstract: In recent years, several approaches have been proposed in order to detect communities in social networks. Most of them suffer from the recurrent problems: no detection of overlapping communities, exponential running time, no detection of all possible communities transformations, don’t consider the properties of social members, inability to deal with large scale networks, etc. Multi-agent systems are very suitable for modeling the phenomena in which various autonomous entities in inter-actions able to evolve in a dynamic environment. Considering the advantages of multi-agent simulations for social networks, in the present study, an incremental multi-agent system based on electric field is proposed. In this approach, a group of autonomous agents work together to discover the dynamic communities. Indeed, an agent is associated to each detected community. To update its community according to the dynamic of its members, each agent creates an electric field around it. It applies an attractive force to add very connected and similar members and neighboring communities. In the same time, it applies a repulsive force to reject some members and to get away from other communities. These forces are based on the structural and attributes similarity. To study the performance of this approach, set of different experiments is performed. The obtained results show the efficiency of the proposed model that was able to overcome all mentioned problems.

Author 1: E. A Abdulkreem

Author 2: H. Zardi

Author 3: H. Karamti

Keywords: Community detection; dynamic social networks; net-work evolution; multi-agent system; electric field; attractive force; repulsive force; attributes similarity; overlapping communities

Paper 65: Image Co-Segmentation via Examples Guidance

Abstract: Given a collection of images which contains objects from the same category, the co-segmentation methods aim at simultaneously segmenting such common objects in each image. Most of existing co-segmentation approaches rely on comput-ing similarities inter-regions representing foregrounds in these images. However, region similarity measurement is challenging due to the large appearance variations among objects in the same category. In addition, for real-world images which have cluttered backgrounds, the existing co-segmentation approaches miss sufficient robustness to extract the common object from the background. In this paper, we propose a new co-segmentation method which takes advantage of the reliable segmentation of few selected images, in order to guide the segmentation of the remaining images in the collection. A random sample of images is first selected from the image collection. Then, the selected images are segmented using an interactive segmentation method. These segmentation results are used to construct positive/negative samples of the targeted common object and background regions respectively. Finally, these samples are propagated to the remain-ing images in the collection through computing both local and global consistency. The experiments on the iCoseg and MSRC datasets demonstrate the performance and robustness of the proposed method.

Author 1: Rachida Es-Salhi

Author 2: Imane Daoudi

Author 3: Hamid El Ouardi

Keywords: Co-segmentation; image segmentation; segmentation propagation; MRF based segmentation

Paper 66: Detection of Infected Leaves and Botanical Diseases using Curvelet Transform

Abstract: The study of plants is known as botany and for any botanist it is a daily routine work to examine various plants in their research lab. This research efforts an image processing-based algorithm for extracting the region of interest (ROI) from plant leaf in order to classify the specie and to recognize the particular botanical disease as well. Moreover, this paper addresses the implementation of curvelet transform on subdivided leaf images in order to compute the related information and train the support vector machine (SVM) classifier to execute better results. Furthermore, the paper presents a comparative analysis of existing and proposed algorithm for species and botanical diseases recognition over the dataset of leaves. The proposed multi-dimensional curvelet transform based algorithm provides relatively greater accuracy of 93.5% with leaves dataset.

Author 1: Nazish Tunio

Author 2: Abdul Latif Memon

Author 3: Faheem Yar Khuhawar

Author 4: Ghulam Mustafa Abro

Keywords: Region Of Interest (ROI); Support Vector Machine (SVM); feature extraction; curvelet transform; alternata; anthrac-nose; blightness

Paper 67: BioPay: Your Fingerprint is Your Credit Card

Abstract: In recent years, credit and debit cards have become a very convenient method of payment. The growing use of card payments, hereafter referred to as credit cards, is evident in the daily use with many applications, such as withdrawing money from an Automated Teller Machine (ATM) and making payments in a store. Online payment has been very common these days, where the transaction is made across a great distance, allowing for online shopping. This has increased chance of credit cards experiencing a risk of cybersecurity attacks, particularly if the transaction amount is big enough. Another problem that arises is the potential fraud should a thief try to impersonate the credit card owner’s identity. To overcome these obstacles, we propose a BioPay scheme that uses the fingerprint biotoken to replace the current plastic credit card. The BioPay scheme uses the biometric data (fingerprint), revocable fingerprint biotokens (Biotope), and Bipartite token to provide high authentication, non-repudiation, security and privacy for all payment transactions including money withdrawal from an ATM. The BioPay scheme collects biometric data (i.e. fingerprint) from users and embeds four-digit authentication numbers inside the encoding biometric data (i.e. fingerprint), finally distributing them over clouds. In the payment/withdrawal process, a user provides his/her fingerprint to complete the transaction. BioPay scheme insures that the transaction process performs on an encrypted form to provide security and privacy for the customer’s bank information. Our experiment shows that BioPay has comparable accuracy and significant performance gain for performing the transaction process.

Author 1: Fahad Alsolami

Keywords: Fingerprint; credit/debit card; cybersecurity

Paper 68: Reviewing Diagnosis Solutions for Valid Product Configurations in the Automated Analysis of Feature Models

Abstract: A Feature Model (FM) is an information model to represent commonalities and variabilities for all the products of a Software Product Line (SPL). The complexity and large-scale of real feature models makes their manual analysis for determining the product configurations validity a tedious or even infeasible task. Efficient solutions for the diagnosis of errors in the Automated Analysis of Feature Models (AAFM) already exist such as FMDiag and FlexDiag. Thus, this work describes the fundamental basis for both diagnosis algorithms to apply the first of them on the validity of FM product configurations. The results highlight the applicability and efficiency of FMDiag and invite us to look for additional applications in the AAFM scenarios.

Author 1: Cristian L. Vidal-Silva

Keywords: AAFM; feature model; valid product; valid config-uration; FMDiag; FlexDiag

Paper 69: Biometric Recognition using Area under Curve Analysis of Electrocardiogram

Abstract: In this paper, we introduce a human recognition system that utilizes lead I electrocardiogram (ECG). It proposes an efficient method for ECG analysis that corrects the signal and extract all major features of its waveform. FIR equiripple high pass filter is used for denoising ECG signal. R peak is detected using Haar wavelet transform. A novel class of features called as area under curve are computed from dominant fiducials of ECG waveform along with other known class of features such as interval features, amplitude features and angle features. The feasibility of an electrocardiogram as a new biometric is tested on selected features that reports the authentication performance 99.49% on QT database, 98.96% on PTB database and 98.48%on MIT-BIH arrhythmia database. The results obtained from the proposed approach surpasses the other conventional methods of biometric applications.

Author 1: Anita Pal

Author 2: Yogendra Narain Singh

Keywords: Electrocardiogram; biometric; area under curve features

Paper 70: Identification and Formal Representation of Change Operations in LOINC Evolution

Abstract: LOINC (Logical Observation Identifiers Names and Codes) is one of the standardized health ontologies that is widely used by practitioners in the health sector. Like other ontologies in health field, LOINC evolves. This research focuses on representing formally the conceptual changes in LOINC. Four steps are taken to achieve this goal. First, the release of LOINC is studied to get an overview of the changes in LOINC. Secondly, the change operations that occur in LOINC are classified. Third, the changes are represented formally by considering the need to keep the history of changes in concepts. Finally, a few algorithms are developed to identify changes that occur between two releases of LOINC. The evaluation shows that the algorithms are able to identify change operations in LOINC with 100% of success rate. With a formal representation of changes that occur in LOINC, it is expected that adjustments to applications that use LOINC can be performed more straightforward. The history of reference to concepts in LOINC can also be traced back so that information about the changes on the reference can be obtained easily.

Author 1: Anny Kartika Sari

Keywords: LOINC; ontology; evolution; change operation; formal representation; health

Paper 71: Challenges of Medical Records Interoperability in Developing Countries: A Case Study of the University Teaching Hospital in Zambia

Abstract: The University Teaching Hospital (UTH) is an integral national referral Hospital made up of eight departments. Standardized systems and semantic interoperability is key for successful flow of patient information from one department to another and from section to section within a department. Lack of a SNOMED CT E.H.R System in surgery departments causes inefficient scheduling of surgical procedures, insufficient and inaccurate pertinent patient historical information, misconceptions and error arising from ambiguities in terminology usage. The result is unhealthy clinician working environment leading to high death rates among patients. Baseline Survey was conducted using questionnaire to establish the major drawbacks of the current manual system in use at the department. Record inspection was done followed by roundtable discussion with stakeholder. Convenient sampling was used, out of 40 respondents 72.5% had computers in their section, 27.5% did not have, 60% were using partial electronic records and paper based, 37.5% were using manual system, 2.5% reported that they were using electronic record system. The result reviewed more than 50% of the medical practitioner ranging from nurses to surgeon reported to be dissatisfied with the current system. In addition, record inspection was conducted by going to each section of the department to understand the business process and the form and format of data storage; this exercise reviewed redundancy in the capture, storage and management of patient records due to the fact that in every section where a patient pass, while undergoing diagnosis procedure, basic details are collected afresh for the same patient. This situation has brought about unnecessary duplication of work. The other drawback is the storage of patient records arising from lack of storage space. Record which are ten years old are destroyed to create space for new ones. This destruction of records robs researchers of the much-needed data for trends analysis and patient disease history. Because of these draw backs, it is very apparent that a standardized E.H.R is implemented.

Author 1: Danny Leza

Author 2: Jackson Phiri

Keywords: HER; surgery; ICT; paper based; adoption

Paper 72: LQR Robust Control for Active and Reactive Power Tracking of a DFIG based WECS

Abstract: This research work sets forward a new formulation of Linear Quadratic Regulator problem (LQR) applied to a Wind Energy Conversion System (WECS). A new necessary and sufficient condition of Lyapunov asymptotic stability is also established. The problem is mathematically described in form of Linear Matrix Inequalities (LMIs). The considered WECS is based on a Doubly Fed Induction Generator (DFIG). An appropriate Linear Parameter Varying (LPV) model is designed. This model stands for a realistic representation of the randomly time varying wind velocity. Stability and robustness of the controller over the admissible values of time varying parameter are investigated. The newly lifted Lyapunov condition gives less conservative conditions for LMI approach in case of parameter-dependent Lyapunov functions PDLF. The considered PDLF has the same variation dynamics as the system matrix. The intrinsic objective for our research is to offer more freedom degrees to the control problem and to improve the efficiency of the controller in case of uncertainties or parametric variations. The performances of the proposed theorems are validated to achieve active and reactive powers tracking of the WECS over the admissible range of wind speeds. The interesting features of the proposed solution are the simpler implementation and the larger robustness margin. It also has the advantage of providing a linear control to the considered nonlinear system without resorting to linearization. The LMIs implementation is performed on Yalmip Matlab toolbox. The proposed controller is verified on a Matlab Simulink emulator. This work presents an extension of the LQR control problem to LPV systems.

Author 1: Sana Salhi

Author 2: Salah Salhi

Keywords: LQR robust tracking; LPV system; lyapunov stability; LMI; DFIG based wind energy conversion systems; optimal control

Paper 73: Investigating Technologies in Decision based Internet of Things, Internet of Everythings and Cloud Computing for Smart City

Abstract: The idea of a Smart City features the need to upgrade quality, interconnection and execution of different urban administrations with the utilization of data and correspondence advances (ICT). Smart City advances cloud-based and Internet of Things (IoT) based administrations in which certifiable user interface utilize PDAs, sensors and RFIDs. Distributed computing and IoT are by and by two most essential ICT models that are forming the up and coming age of registering. Cloud computing speaks to the new technique for conveying equipment and programming assets to the clients, Internet of Things (IoT) is at present a standout amongst the most well-known ICT ideal models. In the meantime, the IoT idea imagines another age of gadgets (sensors, both virtual and physical) that are associated with the Internet and give diverse administrations to esteem included applications. Focus of this study attention on the integration of Cloud, IoT and IoE technologies for smart city services as well as a review has been made so that we can develop a better smart city that will utilize IoT, IoE in order to provide a better platform for smart city. This paper tends to the joined area of cloud computing, IoT and IoE for any smart city application organization.

Author 1: Babur Hayat Malik

Author 2: Zunaira Zainab

Author 3: Husnain Mushtaq

Author 4: Amina Yousaf

Author 5: Sohaib Latif

Author 6: Hafiz Zubair

Author 7: Sayyam Malik

Author 8: Palwesha Sehar

Keywords: IoT; IOET; technologies; cloud computing; WSN

Paper 74: Development of a Two Factor Authentication for Vehicle Parking Space Control based on Automatic Number Plate Recognition and Radio Frequency Identification

Abstract: This paper proposed a two factor authentication for vehicle access controls using Automatic Number Plate Recognition (ANPR) and Radio Frequency Identification system (RFID) for the University of Zambia (UNZA) vehicle access points. The University of Zambia is experiencing increasing challenge of car parking space and vehicle access controls to and within campus premises. The survey that was conducted reviewed that members of staff found difficulties finding parking spaces due to intrusion. The survey also reviewed that vehicles have been stolen within campus parking areas without detection. An access control system using integrated ANPR and RFID technologies was developed to provide five authentication states that met different vehicle access point’s requirement. It was built with ‘ORed’ and ‘ANDed’, logic settings to achieve five different states of authentication levels, each suited for a particular access point. The ANRP system used the vehicle number plate to authenticate the vehicle through the use of the camera. On the other hand, the RFID system used the drivers’ card/tag through the RFID card reader to authenticate the user. Daily transaction records were sent to the security center where information would easily be retrieved. Illegal access to restricted areas, threats of theft of motor vehicles and failed transaction recording system was amicably solved by this proposal.

Author 1: Friday Chisowa Chazanga

Author 2: Jackson Phiri

Author 3: Sebastian Namukolo

Keywords: RFID; ANPR; Vehicle access control; two-factor authentication

Paper 75: Multi Factor Authentication for Student and Staff Access Control

Abstract: This paper proposes a model to improve security, by controlling who accesses the University of Zambia Campus, Student Hostels and Offices. The proposed model combines Barcode, RFID, and Biometrics Technology to automatically identify Students and Staff. A component to track visitors’ physical location and movements in real time is also included to ensure visitors go to authorized places. A baseline study based on International Standard Organisation 27002 standard was conducted to measure the level of security at UNZA. This result shows that UNZA has uncontrolled access into the campus environment, student hostels and offices. The results from this study were used to develop the proposed model. When the RFID reader installed at any of the entrances detects an RFID tag number, the system requests for a fingerprint scan and scans the database for a match. If both RFID card and fingerprint belong to a registered Student or Staff, the entrance door or Turnstile is released open and access is granted otherwise access is denied. In case of the visitor the National ID number is tied to the RFID tag number. The visitors’ RFID tag has a GPS module fixed to it. Once the visitor is granted access their movements and physical location are tracked in real time.

Author 1: Consuela Simukali

Author 2: Jackson Phiri

Author 3: Stephen Namukolo

Keywords: Security and access control; authentication; RFID; ISO 27002; barcode technologies

Paper 76: Software Product Line Test List Generation based on Harmony Search Algorithm with Constraints Support

Abstract: In software product line (SPL), selecting product's features to be tested is an essential issue to enable the manufactories to release new products earlier than others. Practically, it is impossible to test all the products’ features (i.e. exhaustive testing). Evidence has shown that several SPL strategies have been proposed to generate the test list for testing purpose. Nevertheless, all the existing strategies failed to produce an optimum test list for all cases. Thus, the current study is aimed to develop a new SPL test list generation strategy based on Harmony Search (HS) algorithm, namely SPL-HS. SPL-HS generates a minimum number of test cases that cover all of the features that are required to be tested based on the required interaction degree (t). The results demonstrate that the performance of SPL-HS is able to compete with the existing SPL strategies for generating test list size.

Author 1: AbdulRahman A. Alsewari

Author 2: Muhammad N. Kabir

Author 3: Kamal Z. Zamli

Author 4: Khalid S. Alaofi

Keywords: Harmony search; computational intelligence; combinatorial testing problem

Paper 77: Implementation and Comparison of Text-Based Image Retrieval Schemes

Abstract: Search engines, i.e., Google, Yahoo pro-vide various libraries and API’s to assist programmers and researchers in easier and efficient access to their collected data. When a user generates a search query, the dedicated Application Programming Interface (API) returns the JavaScript Object Notation (JSON) file which contains the desired data. Scraping techniques help image descriptors to separate the image’s URL and web host’s URL in different documents for easier implementation of different algorithms. The aim of this paper is to propose a novel approach to effectively filter out the desired image(s) from the retrieved data. More specifically, this work primarily focuses on applying simple yet efficient techniques to achieve accurate image retrieval. We compare two algorithms, i.e., Cosine similarity and Sequence Matcher, to obtain the accuracy with a minimum of irrelevance. Obtained results prove Cosine similarity more accurate than its counterpart in finding the maximum relevant image(s).

Author 1: Syed Ali Jafar Zaidi

Author 2: Attaullah Buriro

Author 3: Mohammad Riaz

Author 4: Athar Mahboob

Author 5: Mohammad Noman Riaz

Keywords: Image retrieval; image filtering; cosine similarity; sequence matching

Paper 78: EMMCS: An Edge Monitoring Framework for Multi-Cloud Environments using SNMP

Abstract: Multi-cloud computing is no different than other Cloud computing (CC) models when it comes to providing users with self-services IT resources. For instance, a company can use services of one specific cloud Service Provider (CSP) for its business, as it can use more than one CSP either to get the best of each without any vendor lock-in. However, the situation is different regarding monitoring a multi-cloud environment. In fact, CSPs provide in-house monitoring tools that are natively compatible with their environment but lack support for other CSP's environments, which is problematic for any company that wants to use more than a CSP. In addition, third party cloud monitoring tools often use agents installed on each monitored virtual machine (VM) to collect monitoring data and send them to a central monitoring server that is hosted on premise or on a Cloud, which increases bottlenecks and latency while transmitting data or processing it. Therefore, this paper presents a monitoring framework for multi-cloud environments that implements edge computing and RESTFul microservices for a high efficiency monitoring and scalability. In fact, the monitoring framework “EMMCS” uses SNMP agents to collect metrics, and performs all monitoring tasks at the edge of each cloud to enhance network transmission and data processing at the central monitoring server level. The implementation of the framework is tested on different public cloud environments, namely Amazon AWS and Microsoft Azure to show the efficiency of the proposed approach.

Author 1: Saad Khoudali

Author 2: Karim Benzidane

Author 3: Abderrahim Sekkaki

Keywords: Simple network management protocol; multi-cloud monitoring; edge computing; edge monitoring; microservices; cloud computing