IJACSA Volume 9 Issue 9 - thesai.org

IJACSA Volume 9 Issue 9

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

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Paper 1: A Proposal for A High Availability Architecture for VoIP Telephone Systems based on Open Source Software

Abstract: The inherent needs of organizations to improve and amplify their technological platform entail large expenses with the goal to enhance their performance. Hence, they have to contemplate mechanisms of optimization and the improvement of their operational infrastructure. In this direction arises the need to guarantee the correct operation and non-degradation of the services provided by the platform during the periods with a significant load of work. This type of scenario is perfectly applicable to the field of VoIP technologies, where users generate elevated loads of work on critical points of the infrastructure, during the process of interaction with their peers. In this research work, we propose a solution for high availability, with the goal of maintaining the continuity of the operation of communication environments based on the SIP protocol in high load. We validate our proposal through numerous experiments. Also, we compare our solution with other classical VoIP scenarios and show the advantages of a high availability and fault tolerance architecture for organizations.

Author 1: Alejandro Martin

Author 2: Eric Gamess

Author 3: Dedaniel Urribarri

Author 4: Jesús Gómez

Keywords: Cluster; high availability; load balancer; VoIP; SIP; kamailio; corosync; asterisk; SIPp

Paper 2: Aesthetics Versus Readability of Source Code

Abstract: The relationship between programming style and program readability has never been examined empirically, although the association has substantial importance for both pedagogical and industry best practices. This paper studies a fractal, relativistic measure of programming style called the beauty factor or “beauty” and puts forward two new hypotheses of beauty. First, code with increasing beauty tends to be more readable. Second, beauty measures a unique property in code called aesthetic value distinct from readability. These hypotheses are tested on a corpus of 53,000 lines of open source system codes written by experienced Linux programmers. Statistical correlation analysis is used on 11 different beauty factors versus eight different readability models (i.e., 88 experiments total). As the primary finding, the data show the maximum absolute statistically significant correlation is |p|=0.59 whereas the absolute median correlation is |p|=0.33. In other words, at least 65% of statistically significant variations in beauty cannot be explained by variations in readability; approximately 90% of statistically significant variations in beauty cannot be explained typically by variations in readability. These results lend support to both hypotheses. The data further shows indentation is more reliably correlated with readability than mnemonics or comments and GNU style is more correlated with readability than K&R, BSD, or Linux styles.

Author 1: Ron Coleman

Keywords: Programming style; fractal geometry; readability

Paper 3: User-Defined Financial Functions for MS SQL Server

Abstract: The paper deals with mathematical preparation and subsequent programming of various types of financial functions with using of Transact-SQL in Database Management System MS SQL Server. Financial functions are used to automate calculations in the area of Financial Economics. In MS SQL Server, any financial functions are not offered for financial data processing, how such as in program MS Excel. We emphasize that we have used a different calculation methods to create financial formulas, not those used in Excel. If users want to work with some special functions, there is a possibility to prepare User-Defined Functions (UDFs). The use of UDFs will make it easier to work on financial calculations in large databases.

Author 1: Jolana Gubalova

Author 2: Petra Medvedova

Keywords: Financial economics; user-defined functions; financial functions; database management system; structure query language; transact-SQL

Paper 4: An Amplitude Modulation of Cerebral Rhythms based Method in a Motor Task BCI Paradigm

Abstract: Quantitative evaluation based on amplitude modulation analysis of electroencephalographic signals is proposed for a brain computer interface paradigm. The method allows characterization of the interaction effects of different frequency bands in the electroencephalographic rhythms during motor tasks. A new index was proposed and computed to be a measure of the amplitude modulation. Built on this index, features vector are established for training different classification algorithms. Signals recorded from 50 subjects revealed important differences in amplitude modulations between motor tasks. Most notably, Theta modulation of the Theta and Alpha rhythms proved to be reliable discriminant features between different mental tasks.

Author 1: Oana Diana Eva

Author 2: Anca Mihaela Lazar

Keywords: Brain computer interface; motor tasks; electroencephalographic signal; amplitude modulation analysis; classifiers

Paper 5: Financial Literacy of SME Managers’ on Access to Finance and Performance: The Mediating Role of Financial Service Utilization

Abstract: Considering financial literacy as a central factor for consumer demand for financial services, we analyze its impact on access and actual use of financial services and its ultimate consequential reflections on SMEs performance in developing economies. By recognizing the important distinction between access and actual use of financial services this study uses the partial least square-structural equation modelling (PLS-SEM) to estimate the conceptual model. The study reveal significant positive impact of financial literacy to financial access and performance of the firm. It was also discovered that there is significant positive direct impact of access to financial services into actual use of financial services and positive significant effect of the use of financial service on firm performance. The firm use of financial services has a significant mediating role on firm access to financial services-firm performance relationship. The implications of these findings offers foretastes on the need to deepen and widen the scope of SMEs managers’ financial literacy for effective financial management and financial financing decisions. We argue distinct contributions of access and actual use of financial services construct on firm performance has to be given attention in attempt to avoid generalizing the phenomenon.

Author 1: Juma Buhimila Mabula

Author 2: Han Dong Ping

Keywords: Financial literacy; use of financial services; access to financial services; firm performance

Paper 6: Method of Graph Mining based on the Topological Anomaly Matrix and its Application for Discovering the Structural Peculiarities of Complex Networks

Abstract: The article introduces the mathematical concept of the topological anomaly matrix providing the foundation for the qualitative assessment of the topological organization underlying the large-scale complex networks. The basic idea of the proposed concept consists in translating the distributions of the individual vertex-level characteristics (such as the degree, closeness, and betweenness centrality) into the integrative properties of the overall graph. The article analyzes the lower bounds imposed on the items of the topological anomaly matrix and obtains the new fundamental results enriching the graph theory. With a view to improving the interpretability of these results, the article introduces and proves the theorem regarding the smoothness of the closeness centrality distribution over the graph’s vertices. By performing the series of experiments, the article illustrates the application of the proposed matrix for evaluating the topology of the real-world power grid network and its post-attack damage.

Author 1: Artem Potebnia

Keywords: Topological anomaly matrix; complex network; graph topology; closeness centrality; betweenness centrality; power grid

Paper 7: Comparison of Intelligent Methods of SOC Estimation for Battery of Photovoltaic System

Abstract: It is essential to estimate the state of charge (SOC) of lead-acid batteries to improve the stability and reliability of photovoltaic systems. In this paper, we propose SOC estimation methods for a lead-acid battery using a feed-forward neural network (FFNN) and a recurrent neural network (RNN) with a gradient descent (GD), a levenberg–marquardt (LM), and a scaled conjugate gradient (SCG). Additionally, an adaptive neuro-fuzzy inference system (ANFIS) with a hybrid method was proposed. The voltage and current are used as input data of neural networks to estimate the battery SOC. Experimental results show that the RNN with LM has the best performance for the mean squared error, but the ANFIS has the highest convergence speed.

Author 1: Tae-Hyun Cho

Author 2: Hye-Rin Hwang

Author 3: Jong-Hyun Lee

Author 4: In-Soo Lee

Keywords: Lead-acid battery; SOC; FFNN; RNN; ANFIS; gradient descent; levenberg-marquardt; scaled conjugate gradient

Paper 8: Novel Mechanism of Classifying the Brain Tumor for Identifying its Critical State

Abstract: Classification of brain tumor is one of the most challenging tasks in the clinical and radiological research. Upon investigating the existing research contribution, we find that still there is wide open scope of addressing classification problem pertaining to brain tumor. Therefore, this manuscript presents a simple mechanism of classifying the brain tumor in order to categorize its state of criticality. The proposed system applies a multi-level preprocessing to enhance the input image followed by image thresholding for feature extraction and decomposition using wavelet transform. The extracted features are further subjected to process of dimensional reduction that maintains a balance between good number of enriched feature and less size of redundant feature using statistical approach. Further, a supervised learning approach is implemented that further optimizes the classification process. The study outcome is further benchmarked with different process of classification to show the efficient computational environment of proposed system.

Author 1: Deepthi Murthy T S

Author 2: Sadashivappa G

Author 3: Ravi Shankar D

Keywords: Brain tumor; classification; categorization; clustering; identification; segmentation; MRI; Brain X-Ray

Paper 9: Quality Flag of GOSAT/FTS Products Taking into Account Estimation Reliability

Abstract: Quality and cloud flags of GOSAT/FTS: Fourier Transform Spectrometer onboard Greenhouse gasses Observation Satellite products taking into account cirrus clouds and thick aerosols are considered and proposed. Influence due to cirrus and thick aerosol on estimation of column CO2 and CH4 with GOSAT/FTS data is clarified. Relatively large estimation errors are observed in column CO2 and CH4 retrievals with FTS data in some atmospheric conditions. In order to find such cases, retrieval results and quality/cloud flags in the GOSAT/FTS data products are checked. Through the investigation, it is found that relatively large error is caused by convergence problem due to cirrus clouds and thick aerosols. In the proposed paper, some of the cases of which relatively large estimation error is occurred at the Saga TCCON (The Total Carbon Column Observing Network) site are investigated. Also, a comparative study is conducted between standard products provided by NASA/JPL and the Levenberg-Marquardt based least square method of column CO2 and CH4 retrieval. It is suggested that some improvements of estimation accuracy of column CO2 and CH4 retrieval with GOSAT/FTS data can be expected.

Author 1: Kohei Arai

Author 2: Takashi Higuchi

Author 3: Hiroshi Okumura

Author 4: Hirofumi Ohyama

Author 5: Shuji Kawakami

Author 6: Kei Shiomi

Keywords: Levenberg-Marquardt; FTS; GOSAT; aerosol; cirrus cloud

Paper 10: An Ensemble Approach to Big Data Security (Cyber Security)

Abstract: In the past, information safety was centered on event correlation designed for observing and spotting previously identified attacks. Due to the dynamic nature of multidimensional cyber-attacks, these models are no more acceptable. Specifically, these attacks use different strategies and procedures to find their way into and out of an organization. Traditional methods have reached their limit and thus new approaches are needed to find a solution for arising issues and challenges for big data security. To understand the current problem, we critically reviewed the literature related to big data security and the solutions proposed by the scientific community. In this paper, an ensemble approach for big data cybersecurity is proposed. To evaluate our approach, the given benchmark data is fed to three different classifiers namely to a k-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP) and the output of the single classifiers were compared to ensemble approach of the three classifiers. The reported results show that the ensemble approach for big data cybersecurity performs better than the single classifiers.

Author 1: Manzoor Ahmed Hashmani

Author 2: Syed Muslim Jameel

Author 3: Aidarus M. Ibrahim

Author 4: Maryam Zaffar

Author 5: Kamran Raza

Keywords: Big data; cyber security; benign, malicious; ensemble approach; Support Vector Machine (SVM); Receiver Operating Characteristic (ROC); Features (F)

Paper 11: An Interactive Content Development for Depression Awareness among Tertiary Students

Abstract: “2D Animation: Depression among Tertiary Students” is a novel interactive content development that gives information of depression to public. It consists of seven modules of depression which are Introduction, Statistics, Types, Symptoms, Causes, Treatments and Video of Depression Information. The objectives of this project are to study the causes and effects of depression among tertiary-level students in Malaysia, to design and develop a 2D animation in raising awareness about depression to the viewers and to investigate the effectiveness of depression animation to the users. The methodology used for this project is Multimedia Production Process which consists of three stages which are pre-production, production and post-production. The testing result shows that the interactive content for depression animation is accepted and effective to the public in order to have a better understanding and awareness on depression among tertiary students.

Author 1: Sarni Suhaila Rahim

Author 2: Thum Wei Ching

Keywords: 2D animation; awareness; depression; interactive content; tertiary students

Paper 12: Model for Predicting Educational Domain Rate based on the Regional Level

Abstract: The geographic information system (GIS) is rapidly becoming the part of current technology trends. GIS can be used to identify the factors that become the reason for an individual to adopt a field or subject. We used GIS as a major tool with the other technologies to identify the key factors. This research has analyzed that mostly people used to migrate to other cities due to unavailability of resources in their own region. Collection of data was done with the help of Survey 123 through which we were able to collect location coordinates of participants. After that, Pilot study approach used to conduct this research. Results show` that mostly user preferred to move to other cities due to unavailability of programs in local institutes. The overall idea can be used to improvement of local institutes and this research can also be used for proper and efficient allocation of facilities and resources in a region, which in turn can save money and time.

Author 1: Arslan Tariq

Author 2: Iqra Tariq

Author 3: Aneela Abbas

Author 4: Muhammad Aadil Butt

Author 5: Maimoona Shahid

Author 6: Umair Muneer Butt

Keywords: Geographic information system; education; domain; technology; region

Paper 13: Crypt-Tag Authentication in NFC Implementation for Medicine Data Management

Abstract: This study focus on the implementation of expiry date detection for medicine using RFID in the health care industry. The motivation for doing this research is the process of searching for the expired medicine is a time consuming and lack of security features included in current NFC implementation. Therefore, the objective of this research is to study the RFID technology used for detecting medicine expiry product and to develop a new system that integrated NFC with authentication feature. Moreover, the problem of current data management for medicine still using manual or barcode system that lead to inconsistency, easy duplication and human error. Here, the NFC is chosen, due to smaller distance of signal coverage, since less interference and the time spending for sniffing activity by the hacker can be reduced. The system is developed using C#, SQLite, Visual Studio, NFC Tag and NFC reader (ACR122U-A9). Experiments have shown that the proposed system has produced medicine expiry date system and only authorized person in charge can monitor the medicine. The impact of the proposed system produces safer, greener and easier environment for better medicine data management. The significance of this study gives a medicine expiry date detection system for health care.

Author 1: Z. Zainal Abidin

Author 2: N. A. Zakaria

Author 3: N. Harum

Author 4: M. R. Baharon

Author 5: Ee-Song Hong

Author 6: Z. Abal Abas

Author 7: Z. Ayop

Author 8: N. A. Mat Ariff

Keywords: Expiry date notification; Radio-Frequency Identification (RFID); Near-Field Communication (NFC); internet of things insider threats; health care

Paper 14: Thinging Machine Applied to Information Leakage

Abstract: This paper introduces a case study that involves data leakage in a bank applying the so-called Thinging Machine (TM) model. The aim is twofold: (1) Presenting a systematic conceptual framework for the leakage problem that provides a foundation for the description and design of a data leakage system. (2) The aim in (1) is developed in the context of experimentation with the TM as a new methodology in modeling. The TM model is based on slicing the domain of interest (a part of the world) to reveal data leakage. The bank case study concentrates on leakage during internal operations of the bank. The leakage spots are exposed through surveying data territory throughout the bank. All streams of information flow are identified, thus points of possible leakage can be traced with appropriate evidence. The modeling of flow may uncover possible hidden points of leakage and provide a base for a comprehensive information flow policy. We conclude that a TM based on the Heideggerian notion of thinging can serve as a foundation for early stages of software development and as an alternative approach to the dominant object-orientation paradigm.

Author 1: Sabah S. Al-Fedaghi

Author 2: Mahmoud BehBehani

Keywords: Thinging; bank system; abstract machine; software development cycle; heidegger

Paper 15: Statistical-Based Trustful Access Control Framework for Smart Campuses

Abstract: The vision of the Internet of Things (IoT) is based on the idea of offering connectivity to every physical object (e.g., thermometers, banknotes, smart TVs, bicycles, etc.). This connectivity ensures that immediate information about these objects and their surroundings can be obtained and therefore decisions can be taken based on real-time information. This allows increased productivity and efficiency. One of the most important implementations of the IoT is the smart (or digital) cities where the information collected from the connected devices is used in, for instance, configuring energy systems, enhancing the traffic, controlling pollution or ensuring security. However, there is no guarantee that all objects will provide information because, for example, some may be out of service or have lost connectivity bearing in mind that many objects in an IoT network are characterized by their limited resources (e.g., battery life, computing, and connection capacity). Moreover, the decision in an IoT network is mostly based on the information provided by a subset of the objects rather than all of them. In addition, the obtained information can be contradictory for many reasons, such as a defect in the object or malicious interference either in the object itself or during the communication process. Therefore, it is necessary to provide a measure that reflects to what extent the decision in an IoT network is trustful. In this paper, an approach based on statistical science is proposed to measure the trustworthiness of information collected from heat sensors. An architecture and algorithm, based on the confidence interval measurement to reduce the time taken to verify and check the trustworthiness of network sensors or any other type of IoT device.

Author 1: AHMAD B. ALKHODRE

Keywords: Internet of things; trust management; confidence interval; confidentiality; smart cities

Paper 16: Singkat: A Keyword-Based URL Shortener and Click Tracker Package for Django Web Application

Abstract: In recent years, Python has been gaining popularity as web scripting/programming language. In this research we propose Singkat, an open source uniform resource locator (URL) shortener package with web user interface built using Python-based Django web framework. It can be deployed and customized in any web project based on Django framework. This makes sure that administrators can gain control over data in their own environment. Users can create shortened links using base62 values to generate pseudo random keyword. To minimize phishing and other abuses, only registered users can create shortened link using their chosen keyword, and it is possible to preview a link before accessing it. Registered users can also monitor each click and get useful information. We also ran some tests to measure Singkat’s performance and functionality.

Author 1: Gottfried Prasetyadi

Author 2: Utomo Tri Hantoro

Author 3: Achmad Benny Mutiara

Keywords: Url shortener; click tracker; python; django framework; open source

Paper 17: A Quantum based Evolutionary Algorithm for Stock Index and Bitcoin Price Forecasting

Abstract: Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms.

Author 1: Usman Amjad

Author 2: Tahseen Ahmed Jilani

Author 3: Humera Tariq

Author 4: Amir Hussain

Keywords: Quantum evolutionary algorithm; fuzzy time series; nature inspired computing; fuzzy logic; crypto currency; bitcoin

Paper 18: Detection of Railroad Networks in SAR Images

Abstract: In this study, a railroad networks detection method for synthetic aperture radar (SAR) images is proposed. Proposed method consists of three steps. Firstly, railroad segments are detected. An existing line detector is modified by describing some rules for this process. Then segments are connected by utilizing perceptual grouping. Finally, a new line analysis algorithm is applied to determine real parts of railroad networks. A software is developed to achieve and evaluate proposed method. Completeness and correctness values which are obtained after different steps are computed to evaluate proposed method. Two different TerraSAR-X images are used in experiments and obtained results are discussed in detail.

Author 1: Safak Altay Açar

Author 2: Safak Bayir

Keywords: Remote sensing; synthetic aperture radar; railroad networks detection; perceptual grouping

Paper 19: Connectivity Restoration Techniques for Wireless Sensor and Actor Network (WSAN), A Review

Abstract: Wireless Sensor and actor networks (WSANs) are the most promising research area in the field of wireless communication. It consists of large number of small independent sensor and powerful actor nodes equipped with communication and computation capabilities. Actors gather sensor’s data and react collaboratively to attain application particular assignments. A powerful connected inter-actor network is required to coordinate its operations. Actor node may fail due to the battery depletion or any hardware failure and this failure may divide the network into disjoint segments. This problem can degrade the network performance but also reduce the efficiency and effectiveness of the network. To restore the network into its original state, the researchers have proposed many connectivity restoration techniques during last few years. This paper provides a brief review of the existing connectivity restoration techniques for WSANs with their advantages and limitations.

Author 1: Muhammad Kashif Saeed

Author 2: Mahmood ul Hassan

Author 3: Ansar Munir Shah

Author 4: Khalid Mahmood

Keywords: Wireless sensor networks; wireless sensor and actor networks; node failure; network partitioning; connectivity restoration; node movements

Paper 20: Design of Wearable Patch Antenna for Wireless Body Area Networks

Abstract: Wireless body area networks are being widely used due to the increase in the use of wireless networks and various electrical devices. A Wearable Patch antenna is used for enhancement of various applications for WBAN. In this paper, a low profile wearable microstrip patch antenna is designed and suggested for constant observation of human vital signs such as blood pressure, pulse rate and body temperature using wireless body area network (WBAN) technology. The operating frequency of the antenna is taken as 2.45 GHz which lies in industrial, scientific and medical (ISM) frequency band. Polyester textile fabric with a relative permittivity of 1.44 and thickness of 2.85 mm is used as a substrate material. The proposed antenna is designed to achieve better return loss, VSWR, gain and low value of specific absorption rate (SAR) as compare to other existing wearable antenna. The achieved antenna return loss at 2.45 GHz is about -10.52 dB and gain of 7.81 dB. The VSWR value achieved at 2.45 GHz is 1.84, which is good in terms of good impedance matching. Other antenna field parameters like 2D and 3D gain, radiation pattern, and SAR value have been calculated. High-Frequency Structure Simulator (HFSS) is used to design and simulate the proposed antenna.

Author 1: Saqib Hussain

Author 2: Saima Hafeez

Author 3: Sajjad Ali Memon

Author 4: Nasrullah Pirzada

Keywords: High-Frequency structure simulator (HFSS); return loss; voltage standing wave ratio (VSWR); gain; specific absorption rate (SAR)

Paper 21: Product Feature Ranking and Popularity Model based on Sentiment Comments

Abstract: This paper proposes the development of a model to determine feature popularity ranking for products in the market. Each feature that is reviewed by a customer has a relation to sentiment words present in the sentences within a customer review. Feature quantity of a product, derived from customer review dataset, cannot be used as a benchmark to determine customers’ preferences since each feature is influenced by sentiment words that give it either a positive or negative meaning. A positive meaning shows that the feature is liked by user; and a negative meaning shows that it is disliked by user. This study finds that sentiment assessments by users play an important role in determining feature popularity ranking; and they affect the feature of a product. Thus, this study proposes the development of a model that takes into account the importance of sentiment assessments present in each sentence within a customer review of a product feature. A case study has been conducted in proving that the developed model is able to produce a list of product feature popularity ranking. Results of this experimental model is also put into simple comparative analysis with a few models from previous studies.

Author 1: Siti Rohaidah Ahmad

Author 2: Azuraliza Abu Bakar

Author 3: Mohd Ridzwan Yaakub

Author 4: Nurhafizah Moziyana Mohd Yusop

Author 5: Muslihah Wook

Author 6: Arniyati Ahmad

Keywords: Product feature ranking; sentiment analysis; feature selection; sentiment word

Paper 22: Interest Reduction and PIT Minimization in Content Centric Networks

Abstract: Content Centric Networking aspires to a more efficient use of the Internet through in-path caching, multi-homing, and provisions for state maintenance and intelligent forwarding at the CCN routers. However, these benefits of CCN’s communication model come at the cost of large Pending Interest Table (PIT) sizes and Interest traffic overhead. Reducing PIT size is essential since larger memory sizes have an associated cost of slower access speeds, which would become a bottleneck in high speed networks. Similarly, Interest traffic may lead to upload capacity getting filled up which would be inefficient as well as problematic in case of traffics having bidirectional data transfers such as video conferencing. Our contribution in this paper is threefold. Firstly, we reduce PIT size by eliminating the need for maintaining PIT entries at all routers. We include the return path in the packets and maintain PIT entries at the egress routers only. Further, we use Persistent Interests (PIs), where one Interest suffices for retrieving multiple data segments, in order to reduce PIT entries at the egress routers as well as to reduce Interest overhead. This is especially useful for live and interactive traffic types where packet sizes are small leading to a large number of pipelined Interests at any given time. Lastly, since using PIs affects CCN’s original transport model, we address the affected aspects, namely congestion and flow control and multi path content retrieval. For our congestion scheme, we show that it achieves max-min fairness.

Author 1: Aadil Zia Khan

Keywords: Content centric networks; congestion control; scalability

Paper 23: Image Retrieval System based on Color Global and Local Features Combined with GLCM for Texture Features

Abstract: In CBIR (content-based image retrieval) features are extracted based on color, texture, and shape. There are many factors affecting the accuracy (precision) of retrieval such as number of features, type of features (local or global), color model, and distance measure. In this paper, a two phases approach to retrieve similar images from data set based on color and texture is proposed. In the first phase, global color histogram is utilized with HSV (hue, saturation, and value) color model and an automatic cropping technique is proposed to accelerate the process of features extraction and enhances the accuracy of retrieval. Joint histogram and GLCM (gray-level co-occurrence matric) are deployed in phase two. In this phase, color features and texture features are combined to enhance the accuracy of retrieval. Finally, a new way of using K-means as clustering algorithm is proposed to classify and retrieve images. Two experiments are conducted using WANG database. WANG database consists of 10 different classes each with 100 images. Results of comparing the proposed approach with the most relevant approaches are promising.

Author 1: Jehad Q. Alnihoud

Keywords: CBIR; color histogram; GLCM; K-means; WANG database

Paper 24: Assessing Trends of Existing Research Contribution Towards Internet-of-Things

Abstract: With the growing demands of system automation, technology integration, and non-human intervention technique, Internet-of-Things (IoT) has evolved as a boon and value-added services over pervasive computing. IoT comprises a highly complex system that integrates ubiquitous computing with low-powered data capturing devices via a gateway. Along with various forms of unimaginable advantages, IoT is also associated with a huge list of ongoing problems. The prime objective of the paper is to gauge the effectiveness of existing works of literature being carried out towards mitigating the issues of IoT. The paper illustrates the most frequently explored research topic and less regularly explored topic in IoT for providing a true picture of existing research trends. The paper also idealizes some of the research gaps that have been extracted after reviewing the existing literature.

Author 1: Bhagyashree Ambore

Author 2: Suresh L

Keywords: Bandwidth; cloud computing; energy; internet-of-things; security; sensor network

Paper 25: Text Clustering using Ensemble Clustering Technique

Abstract: Clustering is being used in different fields of research, including data mining, taxonomy, document retrieval, image segmentation, pattern classification. Text clustering is a technique through which text/ documents are divided into a particular number of groups, so that text within each group is related in contents. In this paper, the idea of ensemble text clustering of majority voting is defined. For this purpose, different clustering methods such as fuzzy c-means, k-means, agglomerative, Gustafson Kessel and k-medoid are used. After performing the pre-processing of the documents, inverse document frequency (IDF) has been achieved by the provided dataset. The achieved IDF is considered as input to the clustering algorithms. Dunn Index and Davies Bouldin Index have been calculated which are applied to analyze the usefulness of the proposed ensemble clustering. In this work, a dataset "Textclus" which contains four different classes, history, education, politician and art as a text is applied. Additionally, another dataset "20newsgroups" is also applied for analysis. The clustering quality measures have also been calculated from the proposed ensemble clustering results. The attained results show that the proposed ensemble clustering outperforms the other state of the art clustering techniques.

Author 1: Muhammad Mateen

Author 2: Junhao Wen

Author 3: Mehdi Hassan

Author 4: Sun Song

Keywords: Agglomerative; document clustering; ensemble clustering; gustafson kessel; inverse documents frequency; text clustering

Paper 26: Profile-Based Semantic Method using Heuristics for Web Search Personalization

Abstract: User profiles play a critical role in personalizing user search. It assists search systems in retrieving relevant information that is searched on the web considering the user needs. Researchers presented a vast number of profile-based approaches that aims to improve the effectiveness of information retrieval. However, these approaches are syntactic-based which fail to achieve the user satisfaction. By the means that the search results do not meet user preferences, due to the fact that the search is keyword-based rather than semantic-based. Exploiting user profiles with the application of semantic web technology into personalization might produce a step forward in future retrieval systems. By adopting profiling approach and using ontology base characteristics, a semantic-based method using heuristics and KNN algorithm is proposed. It engages searching ontology base domains horizontally and vertically to discover and extract the closest concept to the meaning of the query keyword. The extracted concept is used to expand the user query to personalize the search result and present the customized information for individuals.

Author 1: Hikmat A. M. Abdeljaber

Keywords: Semantic search method; user profile; heuristics; web search personalization; information retrieval

Paper 27: Deep Learning Algorithm for Cyberbullying Detection

Abstract: Cyberbullying is a crime where one person becomes the target of harassment and hate. Many cyberbullying detection approaches have been introduced, however, they were largely based on textual and user features. Most of the research found in the literature aimed at improving detection through introducing new features. However, as the number of features increases, the feature extraction and selection phases have become harder. On the other hand, no study has examined the meaning of words and semantics in cyberbullying. In order to bridge this gap, we propose a novel algorithms CNN-CB that eliminate the need for feature engineering and produce better prediction than traditional cyberbullying detection approaches. The proposed algorithm adapts the concept of word embedding where similar words have similar embedding. Therefore, bullying tweets will have similar representations and this will advance the detection. CNN-CB is based on convolutional neural network (CNN) and incorporates semantics through the use of word embedding. Experiments showed that CNN-CB algorithm outperform traditional content-based cyberbullying detection with an accuracy of 95%.

Author 1: Monirah Abdullah Al-Ajlan

Author 2: Mourad Ykhlef

Keywords: Cyberbullying; convolutional neural network; CNN; detection; deep learning

Paper 28: NADA: New Arabic Dataset for Text Classification

Abstract: In the recent years, Arabic Natural Language Processing, including Text summarization, Text simplification, Text Categorization and other Natural Language-related disciplines, are attracting more researchers. Appropriate resources for Arabic Text Categorization are becoming a big necessity for the development of this research. The few existing corpora are not ready for use, they require preprocessing and filtering operations. In addition, most of them are not organized based on standard classification methods which makes unbalanced classes and thus reduced the classification accuracy. This paper proposes a New Arabic Dataset (NADA) for Text Categorization purpose. This corpus is composed of two existing corpora OSAC and DAA. The new corpus is preprocessed and filtered using the recent state of the art methods. It is also organized based on Dewey decimal classification scheme and Synthetic Minority Over-Sampling Technique. The experiment results show that NADA is an efficient dataset ready for use in Arabic Text Categorization.

Author 1: Nada Alalyani

Author 2: Souad Larabi Marie-Sainte

Keywords: Data collection; arabic natural language processing; arabic text categorization; dewey decimal classification; synthetic minority over-sampling

Paper 29: An IoT based Warehouse Intrusion Detection (E-Perimeter) and Grain Tracking Model for Food Reserve Agency

Abstract: Zambia’s agricultural sector through Food Reserve Agency (FRA) while still underdeveloped faces many challenges that range from marketing, spoilage, infestations, and theft at site, spillage and storage among others. The methods used by FRA in their business processes are largely manual as there are no systems in place. In order to help curb these problems, this paper proposed and developed novel methods that can be used to sense real-time warehouse intrusion and grain tracking within the FRA circulation. The IoT based prototype model made use of the APC220 transceiver, GSM, GPRS, RFID, PIR and cloud storage. To curb theft of grain at storage points, the system used motion sensing through the use of PIR sensors, wireless radio communication module and the GSM/GPRS technologies such that when anyone comes in the range of PIR sensor, then the sensor will send a logic signal to the microcontroller. Lastly, the RFID combined with GSM and Arduino microcontroller responsible for grain tracking. From the results obtained in the experiment conducted it is believed that once this technology is adopted, theft will be reduced and grain management in the FRA satellite Depots dotted around the country will improve.

Author 1: Sipiwe Chihana

Author 2: Jackson Phiri

Author 3: Douglas Kunda

Keywords: Internet of things; motion sensing; RFID; cloud storage; GSM/GPRS

Paper 30: Communication System Design of Remote Areas using Openbts

Abstract: OpenBTS is a software-based GSM BTS, which allows GSM cell phone users to make phone calls or send SMS (short messages), without using a commercial service provider network. OpenBTS is known as the first open source implementation of the GSM industry standard protocol. The OpenBTS network is a network that is easy to implement and also inexpensive in maintenance and installation. Communication using a cell phone is not only needed in urban areas but remote areas currently require. But the problem is that not all remote areas get services from commercial cellular operators. By implementing mobile phone communication using OpenBTS, remote communication is very likely to be implemented. In this research communication design was delivered using GSM mobile phones using OpenBTS with telephone and SMS services.

Author 1: Winarno Sugeng

Author 2: Theta Dinnarwaty Putri

Keywords: OpenBTS; GSM; communication; remote areas

Paper 31: Self-organized Population Segmentation for Geosocial Network Neighborhood

Abstract: Geosocial network neighborhood application allows user to share information and communicate with other people within a virtual neighborhood or community. A large and crowded neighbourhood will degrade social quality within the community. Therefore, optimal population segmentation is an essential part in a geosocial network neighborhood, to specify access rights and privileges to resources, and increase social connectivity. In this paper, we propose an extension of the density-based clustering method to allow self-organized segmentation for neighbourhood boundaries in a geosocial network. The objective of this paper is two-fold: First, to improve the distance calculation in population segmentation in a geosocial network neighbourhood. Second, to implement self-organized population segmentation algorithms using threshold value and Dunbar number. The effectiveness of the proposed algorithms is evaluated via experimental scenarios using GPS data. The proposed algorithms show improvement in segmenting large group size of cluster into smaller group size of cluster to maintain the stability of social relationship in the neighbourhood.

Author 1: Low Shen Loong

Author 2: Syarulnaziah Anawar

Author 3: Zakiah Ayop

Author 4: Mohd Rizuan Baharon

Author 5: Erman Hamid

Keywords: Segmentation; geosocial network; virtual neighbourhood; density-based clustering; dunbar’s number

Paper 32: A Serious Game for Healthcare Industry: Information Security Awareness Training Program for Hospital Universiti Kebangsaan Malaysia

Abstract: This paper aims to develop an information security awareness training program for the healthcare industry to ensure the appropriate protection of electronic health systems. Serious games are primarily designed for training purposes rather than pure entertainment. Serious games are proven as an effective training approach for awareness programs. Serious games benefit learning as the games are fun to play and motivate learners to participate and interact with learning activities. Developing a serious game requires the revision of adequate guidelines that identify all characteristics to be incorporated in such games. Thus, this paper reviews serious game models that have been constructed as game development guidelines. To this end, a serious game is developed and implemented at a selected healthcare organization.

Author 1: Arash Ghazvini

Author 2: Zarina Shukur

Keywords: Serious game; information security; awareness training program

Paper 33: Hashtag Generator and Content Authenticator

Abstract: In the recent past, Online Marketing applications have been a focus of research. But still there are enormous challenges on the accuracy and authenticity of the content posted through social media. And if the social media business platforms are considered, majority of the users who try to add a market value to their own product face the problem of not getting enough attention from their target audience. The purpose of this research is to develop a safe and efficient trending hashtag generating application solution for social media business users which generates trending and relevant hashtags for user content in order to get a broad reach of target audience, automatically generates a meaningful caption to their relevant posts and guarantees the authenticity of the product at the same time. The user content is analyzed and filters the important keywords, generates a meaningful caption, suggest related trending keywords and generates trending hashtags to get the required reach for online marketers. Additionally, the marketing products’ content authentication is ensured. The application uses Natural Language Processing, Machine Learning, API technologies, Java and Python technologies. A unique database is assigned to users which contains rankings for each user. The target audience who engages in buying products get to know about the status of the sellers with respect to authenticity of the content. It is believed that the application provides a promising solution to existing audience reach problems of online marketers and buyers. The significance of this system is to help marketers and buyers to engage in online buying and selling with much effective, reliable and safer ways. This mitigate the vulnerability of bad social media marketing influences and helps to establish a safe and reliable online marketing practice to make both sellers and buyers happy. This paper provides a brief description on how to perform an organized online marketing discipline via the Trending Hashtag Generator & Image Authenticator application.

Author 1: Kavinga Yapa Abeywardana

Author 2: Ginige A.R.

Author 3: Herath N.

Author 4: Somarathne H.P.

Author 5: Thennakoon T.M.N.S.

Keywords: Hashtags; social media; NLP; machine learning; REST API; content authentication

Paper 34: EEG Signals based Brain Source Localization Approaches

Abstract: This article is focused on the overview of functionality of the neurons and investigation of the current research and algorithms used for brain source localization. The human brain is made up of active neurons and continuously generates electrical impulses on scalp surface. The neurons transmit the message through the dendrites called pyramidal cells. The active parts of the brain are addressed and measured by various neuroimaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG) etc. These techniques help to diagnose pathological, physiological, mental and functional abnormalities of the brain. EEG is a high temporal resolution and a low spatial resolution technique which yields the non-invasively potential difference measurements between pair of electrodes over the scalp. It is used in understanding behavior of brain which is further used to analyze various brain disorders. EEG brain source localization has remained an active area of research in neurophysiology since last couple of decades and still being investigated in terms of its processing time, resolution, localization error, free energy, integrated techniques and algorithms applied. In this paper, several approaches of forward problem, inverse problem and Bayesian framework have been explored to address the uncertainties and issues of localization of the neural activities incurring in the brain.

Author 1: Anwar Ali Gaho

Author 2: Sayed Hyder Abbas Musavi

Author 3: Munsif Ali Jatoi

Author 4: Muhammad Shafiq

Keywords: Electroencephalograph; brain source localization; forward problem; inverse problem; bayesian framework

Paper 35: An Efficient Protocol using Fuzzy Logic and Grids with Two-Dimensional Techniques for Saving Energy in WSN

Abstract: This work proposes an energy-saving protocol for wireless sensor networks (WSNs) using fuzzy logic and grids with two-dimensional techniques, namely, gravity and energy centers, to address the pressing issue of energy efficiency in WSNs. The optimal cluster head is chosen in two stages of the proposed protocol to prolong the network lifetime and reduce the energy consumption. The proposed protocol evaluated the cluster-head radius according to the residual energy and distance to the base station(BS) parameters of the sensor nodes. The proposed scheme shows better improvements than other related protocols as it extends the lifetime of Two Dimensional Technique Based On Center of Gravity and Energy Center (TDTCGE) protocol by 54\% and saves more energy. Fuzzy inference engine (Mamdani's rule) is used to elect the chance to be the best node. The results have been derived from matlab simulator which shows that the proposed protocol performs better than the TDTCGE protocol. Simulation results show also that our protocol offers a much better network lifetime and energy efficiency than other existing protocols.

Author 1: Emad M Ibbini

Author 2: Kweh Yeah Lun

Author 3: Mohamed Othman

Author 4: Zurina Mohd Hanapi

Author 5: Amir Abbas Baradaran

Keywords: Fuzzy logic; fuzzy inference engine; first node die; last node die; energy efficiency; lifetime

Paper 36: Design of Linear Phase High Pass FIR Filter using Weight Improved Particle Swarm Optimization

Abstract: The design of Finite Impulse Response (FIR) digital filter involves multi-parameter optimization, while the traditional gradient-based methods are not effective enough for precise design. The aim of this paper is to present a method of designing 24th order high pass FIR filter using an evolutionary heuristic search technique called Weight Improved Particle Swarm Optimization (WIPSO). A new function of the weight parameters is constructed for obtaining a better optimal solution with faster computation. The performance of the proposed algorithm is compared with two other search optimization algorithms namely standard Genetic Algorithm (GA) and conventional Particle Swarm Optimization (PSO). The simulation results show that the proposed WIPSO algorithm is better than GA and PSO in terms of the magnitude response accuracy and the convergence speed for the design of 24th order high pass FIR filter.

Author 1: Adel Jalal Yousif

Author 2: Ghazwan Jabbar Ahmed

Author 3: Ali Subhi Abbood

Keywords: Finite impulse response filter; evolutionary optimization; particle swarm optimization; fitness function; genetic algorithm; high pass filter; impulse response

Paper 37: The Designing of Adaptive Self-Assessment Activities in Second Language Learning using Massive Open Online Courses (MOOCs)

Abstract: Massive Open Online Courses (MOOCs) provides an effective learning platform with various high-quality educational materials accessible to learners from all over the world. In this paper, the types of learner characteristics in MOOCs second language learning are discussed. However, there are still problems and challenges including assessment. A quantitative research method approach has been utilized in this study. Results of the study are then used for implementing suitable adaptive self-assessment activities in MOOCs learning. Findings of this study are two folds: (1) The dimension of learner characteristics (learning styles and cognitive style) for improving student performance in MOOCs learning and (2) suitable self-assessment activities that consider learners requirement or adaptive to learner characteristics for improving MOOCs learning performance. Based on the findings, the data indicate that visual, active, thinking and intuitive learner is the proposed dimension used in this study. In this study, our aim is to propose adaptive self-assessment activities for improving MOOCs learning in the second language course. In the future study, students will be investigated about their engagement using MOOC assessment in the second language.

Author 1: Hasmaini Hashim

Author 2: Sazilah Salam

Author 3: Siti Nurul Mahfuzah Mohamad

Author 4: Nur Syafiatun Safwana Sazali

Keywords: MOOCs; adaptive self-assessment; learning styles; cognitive styles; second language

Paper 38: Consequences of Customer Engagement in Social Networking Sites : Employing Fuzzy Delphi Technique for Validation

Abstract: The consequences of the customer engagement in the Social Networking Sites (SNS) community have direct impact on the brand. This present research was conducted to examine the cohesive mechanisms for item verification on the most influential consequences of participating the brand community and joining the electronic Word-of-Mouth (eWOM) as the manifestation of the behavior of such communities. Using Fuzzy Delphi techniques, a total of 12 heterogeneous experts are involved in the verification process through a 7-point linguistic scale of the questionnaire survey. The results show good evidence of expert consensus by reaching 75% for each consequence of the engaged customers. On the SNS platform, further aspects of the inspected effects can be expanded to be studied on relevant domains. Practitioners will be more strategic in maintaining and fostering customer relationships, and consistently influencing new customers when interacting actively through SNS brand pages.

Author 1: Noraniza Md Jani

Author 2: Mohd Hafiz Zakaria

Author 3: Zulisman Maksom

Author 4: Md. Shariff M. Haniff

Author 5: Ramlan Mustapha

Keywords: Customer engagement; fuzzy delphi; SNS; consequences; brand page

Paper 39: Validating Antecedents of Customer Engagement in Social Networking Sites using Fuzzy Delphi Analysis

Abstract: The concept of online customer engagement is getting imperative in modern business due to the uncontrolled conversation via cyber-avenue. This study validates the antecedents of customer engagement conceptualized in Social Networking Sites (SNS) by benefitting the Fuzzy Delphi method. Through purposive sampling, a total of 12 experts from academics and practitioners have participated in the verification of items through 7-point linguistic scales of the questionnaire instrument. The findings show that invited experts have reached agreement on the elements shown within the framework through a 75% percent agreement for each construct. The analysis of this study has highlighted the implications of the relevant theories on the direction and the new dimensions of customer engagement concept especially in SNS to future researchers. Businesses are clearly able to gain stronger knowledge and information about their customer-related factors and their prospects at SNS.

Author 1: Noraniza Md Jani

Author 2: Mohd Hafiz Zakaria

Author 3: Zulisman Maksom

Author 4: Md. Shariff M. Haniff

Author 5: Ramlan Mustapha

Keywords: Customer engagement; antecedents; fuzzy delphi; SNS; online community

Paper 40: Effect of Fusion of Statistical and Texture Features on HSI based Leaf Images with Both Dorsal and Ventral Sides

Abstract: The present work involves statistically analyzing and studying the overall classification accuracy results using Hue channel images of different plant species using their dorsal and ventral sides, and then subjecting them to the process of feature extraction using first order statistical features and texture based features. These extracted features have been subjected to the classification process using KNN and Random Forest algorithms. Further, this work studies the fusion of two different kinds of features extracted for dorsal and ventral plant leaf images and studying the effect of fusion on the overall classification accuracy results. This work also delves into the feature selection task using random forest algorithm and studies the effect of reduced dataset with unique features on the overall classification accuracy results. The most important outcome of this investigation is that the ventral leaf images can be a suitable alternative for plant species classification using digital images and further, the fusion of features does improve the classification accuracy results.

Author 1: Poonam Saini

Author 2: Arun Kumar

Keywords: Dorsal; ventral; leaf classification; random forest; texture features; statistical features

Paper 41: Design of an Error Output Feedback Digital Delta Sigma Modulator with In–Stage Dithering for Spur–Free Output Spectrum

Abstract: Digital Delta Sigma Modulator (DDSM) is responsible for generation of spurious tones at the output of fractional n frequency synthesizer due their inherent periodicity. This results in an impure output spectrum of frequency synthesizer when they are used to generate the fractional numbers in the divider of Phase Locked Loop (PLL) based frequency synthesizer. This paper presents the design of Error – Output feedback modulator based third order Multi – stage noise Shaping (MASH) structure with lesser hardware and effective error compensation network to break the underlying periodicity of DDSM. The DDSM is also analyzed by using non-shaped, shaped and self – dithering mechanism to achieve a pure output spectrum and reduced quantization noise.

Author 1: Sohail Imran Saeed

Author 2: Khalid Mahmood

Author 3: Mehr e Munir

Keywords: Digital delta sigma modulator; fractional N – frequency synthesizer; phase locked loop; error feedback modulator; spur; dither; MASH; HK – MASH

Paper 42: Applied Artificial Intelligence in 3D-game (HYSTERIA) using UNREAL ENGINE4

Abstract: Game development industry spreading it roots at wider level. With the advancements in gaming technologies industries adopted latest trends for developing modern games. Artificial intelligence (AI) with programming provided countless support for latest technology adoption in game industry. This paper aims to highlight some major points of our research “Creation of third person shooter game in unreal engine 4”. We discussed how we can use one of the most powerful current generation game engines in an attempt to create our own game “Hysteria”. Endeavoring used to replicate the process of the major game production cycle .It is used by modern gaming industries. We attempted it to create an action adventure shooting game by creating its own original storyline. The game Hysteriaisplayedfromathirdperson perspective in which the player must go through multipleenvironmentsfightinghordesof enemies and try to reach the end of level. Depending on the difficulty level that the player sets, there will be the number of enemies and their fighting intensity. The game has been developed but running at initial stages; further enhancement will be required to give it a much professional impression so that in near future it could be successfully commercialized.

Author 1: Muhammad Muzammul

Author 2: Muhammad Awais

Author 3: Muhammad Umer ghani

Author 4: Muhammad Imran Manzoor

Author 5: Muhammad Kashif

Author 6: Muhammad Yahya Saeed

Keywords: Applied AI; UNREAL ENGIN 4; technology adoption

Paper 43: Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

Abstract: Classification plays a significant role in the diagnosis of any form of radiological images in the healthcare sector. After reviewing existing classification approaches carried out over chest radiographs, it was explored that existing techniques are highly restricted to perform binary classification that is not comprehensive for assisting in an effective diagnosis process of chest disease condition. This paper presents a novel approach to classifying chest x-rays on the basis of the practical disease condition. Harnessing the potential features of content-based image retrieval, the proposed system introduces a novel concept of attribute map that not only performs comprehensive classification but also makes the complete computational model extremely lightweight. The study outcome proved to offer better accuracy with the proposed non-iterative process in contrast to existing classifier design.

Author 1: Savitha S K

Author 2: N.C. Naveen

Keywords: Chest x-ray; classification; supervised learning; radiographs; accuracy

Paper 44: Analysis of End-to-End Packet Delay for Internet of Things in Wireless Communications

Abstract: Accurate and efficient estimators for End to End delay (E2EPD) plays a significant and critical role in Quality of Service (QoS) provisioning in Internet of Things (IoT) wireless communications. The purpose of this paper, on one hand, is to propose a novel real-time evaluation metrics, on the other hand, addresses the effects of varying packet payload (PP) size. These two objectives rely on the analysis of E2EPD for QoS provisioning in multi-hop wireless IoT networks through multiple hops count from source to destination. The results of this study show the critical effect of PP size, hops count and interface speed on the improving E2EPD use of applications requiring real-time IoT communications.

Author 1: Imane Maslouhi

Author 2: El Miloud Ar –reyouchi

Author 3: Kamal Ghoumid

Author 4: Kaoutar Baibai

Keywords: End to end delay; internet of things; multi hop; wireless communication

Paper 45: The Generation of a Stable Walking Trajectory of a Biped Robot based on the COG based-Gait Pattern and ZMP Constraint

Abstract: The research works contained in this paper are focused on the generation of a stable walking pattern of a biped robot and the study of its dynamic equilibrium while controlling the two following criteria; the centre of gravity COG and the zero-moment point ZMP. The stability was controlled where the biped have to avoid collision with obstacle. The kinematic constraints were also taken into consideration during the walking of the biped robot. In fact, the generation of the walking patterns is composed of several stages. First, we used the Kajita method for the generation of the COG trajectory, based on the linear inverted pendulum LIPM during the simple support phase SSP and linear pendulum model LPM during double support phase DSP. After that, we used two 4thspline function to generate the swing foot trajectory during the SSP and we used exact formulate for the foot trajectory during DSP. Finally, Newton's algorithm was performed (at the level of the inverse geometric model), in order to calculate the different joints according to the desired trajectories of the hip and the feet. Ground reaction forces were also determined from the dynamic model to satisfy the kinematic constraints on both feet of the biped. The generation of walking is done for two different speeds. To study the biped balance, ZMP generation algorithm was performed during the different walking phases and the results obtained for the two cases were compared.

Author 1: Arbia Ayari

Author 2: Jilani Knani

Keywords: Biped robot; COG; ZMP; stability; LIPM; LPM; walking gait

Paper 46: Enhancing the Secured Software Framework using Vulnerability Patterns and Flow Diagrams

Abstract: This article describes the process of simplifying the software security classification. The inputs of this process include a reference model from previous researcher and existing Common Vulnerabilities and Exposure (CVE) database. An interesting aim is to find out how we can make the secured software framework implementable in practice. In order to answer this question, some inquiries were set out regarding reference model and meta-process for classification to be a workable measurement system. The outputs of the process are the results discussion of experimental result and expert’s validation. The experimental result use the existing CVE database which serves as an analysis when a) the framework is applied on three mix datasets, and b) when the framework is applied on two focus datasets. The first explains the result when the framework is applied on the CVE data randomly which consist mix of vendors and the latter is applied on the CVE data randomly but on selective vendors. The metric used in this assessment are precision and recall rate. The result shows there is a strong indicator that the framework can produce acceptable output accuracy. Apart from that, several experts’ views were discussed to show the correctness and eliminate the ambiguity of classification rules and to prove the whole framework process.

Author 1: Nor Hafeizah Hassan

Author 2: Nazrulazhar Bahaman

Author 3: Burairah Hussin

Author 4: Shahrin Sahib

Keywords: Software secured framework; security classification; software security; common vulnerabilities and exposures

Paper 47: Intrusion Detection System with Correlation Engine and Vulnerability Assessment

Abstract: The proposed Intrusion Detection System (IDS) which is implemented with modern technologies to address certain prevailing problems in existing intrusion detection systems’ is capable of giving an advanced output to the security analyst. Even though the network of an organization has been secured internally as well as externally the intruders find ways to penetrate the network. With the system that is proposed activities of those intruders can be identified with a higher probability even if managed to bypass security controls of the network. The goal of this project is to give a reliable output to the system users where all the alerts are more accurate and correlated using HIDS alerts and NIDS alerts which is similar to the modern SIEM concept. The system will perform as a centralized IDS by getting inputs from both HIDS and NIDS which gives data regarding the activities of hosts and network traffic. With those implementations, the system is capable of monitoring host activities, monitoring network traffic with existing tools and give a correlated output which is more accurate, advanced and reliable prioritizing the possible attacks by using machine learning techniques and rule-based correlation techniques. With all these capabilities final product is a fully automated Intrusion Detection System which gives correlated alerts as outputs with a less rate of false positives compared to the existing systems.

Author 1: D.W.Y.O. Waidyarathna

Author 2: W.V.A.C.Nayantha

Author 3: W.M.T.C.Wijesinghe

Author 4: Kavinga Yapa Abeywardena

Keywords: Intrusion detection system (IDS); intrusion detection message exchange format (IDMEF); network intrusion detection system (NIDS); host intrusion detection system (HIDS); security information and event management (siem); correlation; machine learning

Paper 48: An Expert Comparison of Accreditation Support Tools for the Undergraduate Computing Programs

Abstract: Realizing continuous quality improvement within educational programs is a challenging task. However, there exist various assessment tools and models that help in this regard. This paper explores the features and capabilities of three major international accreditation support tools and compares their strengths and weaknesses. The investigated tools include EvalTools, CLOSO, and WEAVEonline. Two education quality experts performed a thorough comparison of the three tools across a range of criteria including coverage of the continuous quality improvement cycle, usability of the system, learning curve of faculty, data entry, data protection and privacy, among others. The paper highlights the advantages offered by each tool and identifies the gaps in respect to the continuous quality improvement cycle.

Author 1: Abdallah Namoun

Author 2: Ahmad Taleb

Author 3: Mohamed Benaida

Keywords: Component; accreditation support tools; continuous quality improvement; undergraduate programs; assessment; student outcomes; software

Paper 49: Development of a Novel Approach to Search Resources in IoT

Abstract: Internet of Things (IoT) referred to interconnected the world of things like physical devices, cars, sensors, home appliances, actuators and machines embedded with software at any time, any location. The increasing number of IoT devices facing challenges which are registration, integration, describing sensor, interoperability, semantics, security, discovery and searching. The current systems are suitable for limited number of devices. Our ecosystem change day by day which means we have billions and trillions of devices connecting to the Internet in future. One major challenge in current system is searching of suitable Smart Things from a millions or even billions number of devices in IoT. For the purpose of searching and indexing, some discovery methods and techniques are discussed and compared. Those techniques and methods are studied and find out the limitations and issued of the current system. Another challenge to searching the Smart Things is a variety of description models for describing the Smart Things. In this piece of work, a novel search engine is proposed to search the Smart Things with variety of description models. A web interface is implemented in this research with HTML, JSON and XML formats. The description models of Smart Things SensorML, SensorThings API and W3C JSON-LD are implemented in the current proposed system.

Author 1: Nisar Hussain

Author 2: Tayyaba Anees

Author 3: AzeemUllah

Keywords: IoT; IoT resources; search engine for IoT; SensorM

Paper 50: Automatic Pavement Cracks Detection using Image Processing Techniques and Neural Network

Abstract: Feature extraction methods and subsequent neural network performances were used in this research to impose proper assessment for distressed roads for a case study area in the North of Jordan. Object recognition method was used to extract roads cracks from airborne images acquired by drones. After images has been thresholded and the noise removed, digital image processing algorithms were applied to detect the presence of different crack types in the surface of pavement. In addition to that, the process was capable to automatically determine the length and the orientation of the cracks which were used as input for a neural network pattern recognition function designed for this purpose. Artificial Neural Network was used, tested and verified for cracks extraction. Different patterns and numbers of hidden layers were also investigated. The results revealed that using image processing techniques and neural network could detect pavement cracks with high accuracy.

Author 1: Nawras Shatnawi

Keywords: Artificial neural network (ANN); feature extraction; image processing; pavement crack

Paper 51: Study of Routing Protocols on CBR and VBR Applications in VANET Scenario

Abstract: Vehicular Adhoc Networks (VANETs) are special type of Mobile Adhoc networks (MANETs) where node movement is in pre ordered fashion but with high velocity in comparison to MANETs where nodes move in random manner. Due to high mobility of nodes, reliable data streaming in vehicular networks is a complex and challenging task. Moreover, transmission of data is difficult because of varying requirements of different applications in terms of various resources like time, energy and bandwidth. This paper gives an overview of performance evaluation of four types of routing protocols on CBR and VBR applications. This paper emphasizes on packet delivery ratio, packet loss and packet loss ratio for CBR and VBR applications in different scenarios like varying node density, varying speed of nodes, pause times and packet size. The effectiveness of various routing protocols shows variation in different conditions. The performance evaluation of different applications in terms of Quality of service (QoS) parameters like packet delivery ratio, packet loss and packet loss ratio has been studied by varying different conditions of CBR traffic and VBR traffic which has gives an insight to improve packet delivery ratio which in turn can be utilized to improve performance of an application in future.

Author 1: Pooja Sharma

Keywords: VANETS; routing protocols; qualnet; traffic types introduction

Paper 52: A Survey on Smartphone-Based Accident Reporting and Guidance Systems

Abstract: Every day, around the world, a large percentage of people die from road accidents and falls. One of the reasons for a person's death during accidents is the unavailability of first aid, due to the delay in informing about the accident. Thus, in the case of incidents involving vehicles or falls, response time is crucial for the timely provision of emergency medical services. An effective approach intended to reduce the number of traffic-related deaths is: the use of a system for detecting and reporting the occurred accidents, as well as reducing the time between the occurrences of an accident and sending the first emergency respondents to the scene of the accident. This paper presents a recent study on mobile terminal solutions (smartphones) for detecting and preventing accidents (road, falls, bicycles) and systematic comparisons of existing solutions.

Author 1: Alexandra Fanca

Author 2: Adela Puscasiu

Author 3: Honoriu Valean

Author 4: Silviu Folea

Keywords: Smartphone; accident; detection algorithm; reporting accident; mobile application; sensors

Paper 53: Printed Arabic Script Recognition: A Survey

Abstract: Optical character recognition (OCR) is essential in various real-world applications, such as digitizing learning resources to assist visually impaired people and transforming printed resources into electronic media. However, the development of OCR for printed Arabic script is a challenging task. These challenges are due to the specific characteristics of Arabic script. Therefore, different methods have been proposed for developing Arabic OCR systems, and this paper aims to provide a comprehensive review of these methods. This paper also discusses relevant issues of printed Arabic OCR including the challenges of printed Arabic script and performance evaluation. It concludes with a discussion of the current status of printed Arabic OCR, analyzing the remaining problems in the field of printed Arabic OCR and providing several directions for future research.

Author 1: Mansoor Alghamdi

Author 2: William Teahan

Keywords: Optical character recognition; arabic printed OCR; arabic text recognition; arabic OCR survey; feature extraction; segmentation; classification

Paper 54: Intelligent Irrigation Management System

Abstract: It is widely known that water resources are decreasing around the world. Rapid urbanization, population growth, industries and the expansion of agriculture are increasing demand for freshwater. In most countries, including Algeria, irrigation is the largest consumer of water, with about 70% of all freshwater withdrawals being used for irrigation. Therefore, it can be said that solving the problem of water scarcity is based on the adjustment of irrigation. The aim of this paper is to shed light on the irrigation systems, how they can be applied, and what are their benefits. With the adoption of solar energy to feed the system; this energy source is strongly available in arid zones.

Author 1: Wafa Difallah

Author 2: Khelifa Benahmed

Author 3: Fateh Bounnama

Author 4: Belkacem Draoui

Author 5: Ahmed Saaidi

Keywords: Internet of things; irrigation; soil; solar; water; wireless sensor network; intelligent

Paper 55: Securing and Monitoring of Bandwidth Usage in Multi-Agents Denial of Service Environment

Abstract: The primary purpose of Denial of Service attack (DoS) is to cripple resources so that the resources are made unavailable to the legitimate users. Due to the inadequate monitoring of activities on the network, it has resulted into huge financial losses. Bandwidth which is one of the resources being used on the network, if not properly monitored could result into misused and attack. This paper proposes a real time system for securing and monitoring the amount of bandwidth consumed on the network using the multi-agent framework technology. It also keeps a record of internet protocol (IP) addresses visiting the network and may be used as a starting point for the aspect of response in providing a comprehensive solution to DoS attacks. The bandwidth is pre-entered and an agent is assigned to monitor bandwidth consumption rate against the set threshold. If the bandwidth is consumed above the bandwidth limit and time set, then a DoS attack is suspected taking into considerations the DoS attack framework. This framework can be used as a replicate of what happen in the network scenario environment.

Author 1: Ogunleye G. O.

Author 2: Fashoto S.G

Author 3: Mbunge Elliot

Author 4: Arekete S.A

Author 5: Ojewumi T.O.

Keywords: Bandwidth; mobile agent; multi-agents; DoS

Paper 56: Internet of Things and Healthcare Analytics for Better Healthcare Solution: Applications and Challenges

Abstract: The total number of population in the world will keep on increasing. This will eventually pose challenges towards quality of life for example issues related to healthcare. Hence, a proper solution needs to be devised in order to face the challenges. Internet of Things (IoT), which is one of the digital technologies, that is becoming a trend now can offer promising solution. This paper serves as a short communication in introducing IoT and its application in healthcare domain as well as the analytics combined with the technology. Some examples are presented according to the categories of the application. It must be noted that the analytics play an important role in making the IoT healthcare as a comprehensive solution. At the end of the paper, challenges in making this digital as an accessible solution is discussed.

Author 1: Zuraida Abal Abas

Author 2: Zaheera Zainal Abidin

Author 3: Ahmad Fadzli Nizam Abdul Rahman

Author 4: Hidayah Rahmalan

Author 5: Gede Pramudya

Author 6: Mohd Hakim Abdul Hamid

Keywords: Internet of things; analytics; healthcare; applications; challenges

Paper 57: Implementation of Forward Chaining and Certainty Factor Method on Android-Based Expert System of Tomato Diseases Identification

Abstract: Plant disease is one of the reasons that cause the destruction of plant. It affects plant productivity and quality. Most of the farmers made mistake in cope with this problem because of the lack of knowledge. Expert system is a solution that has been widely used for identifying disease. This paper presents an Android-based expert system to help identifying tomato diseases. Data used in this expert system consist of 16 data of tomato diseases, 53 data of symptoms, and 20 variety of rules. This paper implements forward chaining and certainty factor method. Forward chaining is used as a reasoning method to get the result of disease identification. Certainty factor is used as a calculation method to obtain accuracy degree of identification results. Testing has been done through two stages, internal and external. The result from internal testing shows that tomato expert system works properly and fit perfectly in various android devices. External testing is done by giving questionnaire to 44 respondents. The result of questionanaires shows that tomato expert system is categorized as “good” by them.

Author 1: Kurnia Muludi

Author 2: Radix Suharjo

Author 3: Admi Syarif

Author 4: Fitria Ramadhani

Keywords: Expert system; forward chaining; certainty factor; tomato diseases; android

Paper 58: Applying Floyd’s Inductive Assertions Method for Verification of Generalized Net Models Without Temporal Components

Abstract: Generalized Nets are extensions of Petri Nets. They are a suitable tool for describing real sequential and parallel processes in different areas. The implementation of correct Generalized Nets models is a task of great importance for the creation of a number of applications such as transportation management, e-business, medical systems, telephone networks, etc. The cost of an error in the models of some of these applications can be very high. The implementation of models of similar applications has to use formal approaches to prove that the developed models are correct. A foundation stone of software verification, which is suitable for verification of Generalized Nets models with transitions without temporal component, is Floyd’s inductive assertion method. This article presents a modification of Floyd’s inductive assertion method for verification of flowcharts, which allows Generalized Nets without temporal component to be verified. Using an illustrative example, we show that the offered adaptation is appropriate for the purpose of training university students in the Informatics and Computer Sciences in formal methods of verification.

Author 1: Magdalina Todorova

Author 2: Nora Angelova

Keywords: Floyd’s inductive assertions method; generalized nets; verification; formal methods; education

Paper 59: Location-aware Event Attendance System using QR Code and GPS Technology

Abstract: Attendance process in a university’s event is time consuming and tracking the attendance can be harder. In this paper, a smart event attendance system for a university using QR code and GPS technology is proposed with objective to speed up the process of taking students’ attendance and tracking full attendance. The method of developing the system is based on two views; user view which is the mobile application used by the students, and admin view which is the web administration system used by the event organizer. From the evaluation, students’ attendance can be traced from the GPS location combine with QR code. The results indicate that full attendance increases as the system validates attendance through users’ identification, location and timestamp during user login and logout. The proposed system contributes to high satisfaction among the users that claim that the mobile application helps to speed up the event registration process.

Author 1: Zakiah Ayop

Author 2: Chan Yee Lin

Author 3: Syarulnaziah Anawar

Author 4: Erman Hamid

Author 5: Muhammad Syahrul Azhar

Keywords: Event attendance system; quick response (QR) code; global positioning system (GPS); android mobile application

Paper 60: An Enhanced Malay Named Entity Recognition using Combination Approach for Crime Textual Data Analysis

Abstract: Named Entity Recognition (NER) is one of the tasks in the information extraction. NER is used for extracting and classifying words or entities that belong to the proper noun category in text data such as person's name, location, organization, date and others. As seen in today's generation, social media such as web pages, blogs, Facebook, Twitter, Instagram and online newspapers are among the major contributors to the generation of information. This paper presents an enhanced Malay Named Entity Recognition model using combination fuzzy c-means and K-Nearest Neighbours Algorithm method for crime analysis. The results showed that this combination method could improve the accuracy performance on entity recognition of crime data in Malay. The model is expected to provide a better method in the process of recognizing named entities for text analysis particularly in Malay.

Author 1: Siti Azirah Asmai

Author 2: Muhammad Sharilazlan Salleh

Author 3: Halizah Basiron

Author 4: Sabrina Ahmad

Keywords: Named entity recognition; information extraction; fuzzy c-means; k-nearest neighbors; malay language; crime data

Paper 61: Towards A Framework for Multilayer Computing of Survivability

Abstract: The notion of survivability has an important position in today enterprise systems and critical functions. This notion has been defined in different ways. However, lacking a comprehensive and multilayer model for computing the survivability quantitatively, is the major gap happened in researches of this field; a model that is tally general and applicable in various applications. This research tries to design a comprehensive, multilayer as well as general model for modeling and computing the survivability. Considering that the Markov property is true in our proposed model, we used the Markov model. Using the proposed three layer architecture and designing a Markov structure, we could have been able to compute the survivability initially for each of infrastructure components separately and regardless of their functional dependency to each other. The computations were generalized to consider component dependencies as well as the upper layers entering dependencies in Markov model and could compute the survivability of each vital function for the highest architectural layer based on the underlying layers. Finally, a common and ordinary structure of crisis management has been studied and its results analyzed. We could examine the abilities of our model to compute the survivability of the whole crisis management system successfully.

Author 1: Abolghasem Sadeghi

Author 2: Mohammad Reza Valavi

Author 3: Morteza Barari

Keywords: Network survivability; survivability quantification; survivability computation; system survivability

Paper 62: Developing A Model to Predict the Occurrence of the Cardio-Cerebrovascular Disease for the Korean Elderly using the Random Forests Algorithm

Abstract: This study aimed to develop a model for predicting the cardio-cerebrovascular disease of the South Korean elderly using the random forests technique. This study analyzed 2,111 respondents (879 males and 1,232 females), who were age 60 or older, out of total 7,761 respondents, who completed the Seoul Welfare Panel Study. The result variable was defined as the cardio-cerebrovascular disease (e.g., hypertension, cerebral infarction, hyperlipidemia, cardiac infarction, and angina). As a result of developing a random forest-based model, the major determinants of the cardio-cerebrovascular diseases of the South Korean elderly were mean monthly household income, the highest level of education, subjective health condition, subjective friendship, subjective family relationship, smoking, regular exercise, age, marital status, gender, depression experience, economic activity, and high-risk drinking. Among them, mean monthly household income was the most important predictor of the cardio-cerebrovascular disease. Based on the developed prediction model, it is needed to develop a systematic program for preventing the cardio-cerebrovascular disease of the Korean elderly.

Author 1: Haewon Byeon

Keywords: Prediction model; data mining; random forest; risk factors; cardio-cerebrovascular disease; stroke

Paper 63: Proposal for a Feature Automation Solution for an IMS-KMS-IoT Platform based on SDN

Abstract: The concept of the Internet of Things is a paradigm that is gaining more and more ground and soon the number of connected objects will be counted in billions. This will transform our lives and pose new challenges. To meet the challenges of objects several platforms have been proposed by previous work. Some are based on an IMS-IoT platform and others integrate IMS, IoT and SDN technologies. Our article proposes an architecture that integrates SDN into an IMS-IoT platform and the automation of the control layer (IMS-IoT platform) and the transport layer of the functional architecture in order to meet future requirements related to the configuration of these many objects, mobility and diversity of user terminals (smartphone, tablet, computers, etc.). The proposed architecture makes it possible to benefit from all the simplicity and efficiency provided by an SDN network but also the services offered by the IMS-IoT platform.

Author 1: Samba DIOUF

Author 2: Kéba GUEYE

Author 3: Samuel OUYA

Author 4: Gervais MENDY

Keywords: IMS; IoT; SDN; automation; KMS

Paper 64: A Novel Approach for Background Subtraction using Generalized Rayleigh Distribution

Abstract: Identification of the foreground objects in dynamic scenario video images is an exigent task, when compared to static scenes. In contrast to motionless images, video sequences offer more information concerning how items and circumstances change over time. Pixel based comparisons are carried out to categorize the foreground and the background based on frame difference methodology. In order to have more precise object identification, the threshold value is made static during both the cases, to improve the recognition accuracy, adaptive threshold values are estimated for both the methods. The current article also highlights a methodology using Generalized Rayleigh Distribution (GRD). Experimentation is conducted using benchmark video images and the derived outputs are evaluated using a quantitate approach.

Author 1: Pavan Kumar Tadiparthi

Author 2: Srinivas Yarramalle

Author 3: Nagesh Vadaparthi

Keywords: Background subtraction; segmentation; generalized rayleigh distribution (GRD); quantitative evaluation; image analysis

Paper 65: A Survey on using Neural Network based Algorithms for Hand Written Digit Recognition

Abstract: The detection and recognition of handwritten content is the process of converting non-intelligent information such as images into machine edit-able text. This research domain has become an active research area due to vast applications in a number of fields such as handwritten filing of forms or documents in banks, exam form filled by students, users’ authentication applications. Generally, the handwritten content recognition process consists of four steps: data preprocessing, segmentation, the feature¬ extraction and selection, application of supervised learning algorithms. In this paper, a detailed survey of existing techniques used for Hand Written Digit Recognition(HWDR) is carried out. This review is novel as it is focused on HWDR and also it only discusses the application of Neural Network (NN) and its modified algorithms. We discuss an overview of NN and different algorithms which have been adopted from NN. In addition, this research study presents a detailed survey of the use of NN and its variants for digit recognition. Each existing work, we elaborate its steps, novelty, use of dataset and advantages and limitations as well. Moreover, we present a Scientometric analysis of HWDR which presents top journals and sources of research content in this research domain. We also present research challenges and potential future work.

Author 1: Muhammad Ramzan

Author 2: Hikmat Ullah Khan

Author 3: Shahid Mehmood Awan

Author 4: Waseem Akhtar

Author 5: Mahwish Ilyas

Author 6: Ahsan Mahmood

Author 7: Ammara Zamir

Keywords: Neural network; digit recognition; segmentation; supervised learning; image classification; computer vision

Paper 66: Comparative Analysis of Support Vector Machine, Maximum Likelihood and Neural Network Classification on Multispectral Remote Sensing Data

Abstract: Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN.

Author 1: Asmala Ahmad

Author 2: Ummi Kalsom Mohd Hashim

Author 3: Othman Mohd

Author 4: Mohd Mawardy Abdullah

Author 5: Hamzah Sakidin

Author 6: Abd Wahid Rasib

Author 7: Suliadi Firdaus Sufahani

Keywords: Land cover; change detection; remote sensing; training set; supervised classification

Paper 67: Mobile Data Collector Routing Protocol Scheme for Scalable Dense Wireless Sensor Network to Optimize Node’s Life

Abstract: Wireless Sensor Networks (WSN) is a special kind of network communication architecture which has a very wide range of application and the cost-effectiveness of this architecture boosts its adaptability and usability. The erratic use of WSN, its rapid advancement has encouraged the research community to report several standing problems with WSN, among them is the concern of network life and node energy management in a dense network. This paper presents the experimental outcomes of using MDC multi-tier approach in dense network environments. Besides the node density, experiments also consider the inter-agricultural field hurdles that cause communication disturbance among the nodes that exist in ground level, or at some height above the farming field. The simulated experiment shows the noteworthy results, which comparatively enhance the network lifetime, efficiently utilizing individual node energy, and maximizing the content delivery.

Author 1: Farhan A. Siddiqui

Author 2: Jibran R. Khan

Author 3: Muhammad Saeed

Author 4: M. Arshad

Author 5: Nasir Touheed

Keywords: WSN; MDC; node density; energy efficient sensor networks; agriculture; robust networks

Paper 68: IMouse: Eyes Gesture Control System

Abstract: A high number of people, affected with neuro-locomotor disabilities or those paralyzed by injury cannot use computers for basic tasks such as sending or receiving messages, browsing the internet, watch their favorite TV show or movies. Through a previous research study, it was concluded that eyes are an excellent candidate for ubiquitous computing since they move anyway during interaction with computing machinery. Using this underlying information from eye movements could allow bringing the use of computers back to such patients. For this purpose, we propose an imouse gesture control system which is completely operated by human eyes only. The purpose of this work is to design an open-source generic eye-gesture control system that can effectively track eye-movements and enable the user to perform actions mapped to specific eye movements/gestures by using computer webcam. It detects the pupil from the user’s face and then tracks its movements. It needs to be accurate in real-time so that the user is able to use it like other every-day devices with comfort.

Author 1: Syed Muhammad Tahir Saleem

Author 2: Sammat Fareed

Author 3: Farzana Bibi

Author 4: Arsalan Khan

Author 5: Shahzad Gohar

Author 6: Hafiz Hamza Ashraf

Keywords: IMouse; eyes gesture control system; eye tracking systems; mouse cursor; eye mouse; webcam; eye movement

Paper 69: Calculation of Pressure Loss Coefficients in Combining Flows of a Solar Collector using Artificial Neural Networks

Abstract: The paper presents a novel technique for determination of loss coefficients due to pressure by use of artificial neural network (ANN) in tee junctions. Geometry and flow parameters are feed into ANN as the inputs for purpose of training the network. Efficacy of the network is demonstrated by comparison of the ANN and experimentally obtained pressure loss coefficients for combining flows in a Tee Junction. Reynolds numbers ranging from 200 to 14000 and discharge ratios varying from minimum to maximum flow for calculation of pressure loss coefficients have been used. Pressure loss coefficients calculated using ANN is compared to the models from literature used in junction flows. The results achieved after the application of ANN agrees reasonably to the experimental values.

Author 1: Shahzad Yousaf

Author 2: Imran Shafi

Author 3: Jamil Ahmad

Keywords: Artificial neural network; pressure loss coefficients for solar collector; combining flow

Paper 70: Interface of an Automatic Recognition System for Dysarthric Speech

Abstract: This paper addresses the realization of a Human/Machine (H/M) interface including a system for automatic recognition of the Continuous Pathological Speech (ARSCPS) and several communication tools in order to help frail people with speech problems (Dysarthric speech) to access services providing by new technologies of information and communication (TIC) while making it easier for the doctors to achieve a first diagnosis on the patient’s disease. In addition, an ARSCPS has been improved and developed for normal and pathology voice while establishing a link with our graphic interface which is based on the box tools Hidden Markov Model Toolkit (HTK), in addition to the Hidden Models of Markov (HMM). In our work we used different techniques of feature extraction for the speech recognition system in order to improve the dysarthric speech intelligibility while developing an ARSCPS which can perform well for pathological and normal speakers. These techniques are based on the coefficients of ETSI standard Mel Frequency Cepstral Coefficient Front End (ETSI MFCC FE V2.0); Perceptual Linear Prediction coefficients (PLP); Mel Frequency Cepstral Coefficients (MFCC) and the recently proposed Power Normalized Cepstral Coefficients (PNCC) have been used as a basis for comparison. In this context we used the Nemours database which contains 11 speakers that represents dysarthric speech and 11 speakers that represents normal speech.

Author 1: Brahim- Fares Zaidi

Author 2: Malika Boudraa

Author 3: Sid-Ahmed Selouani

Author 4: Djamel Addou

Keywords: Automatic Recognition System of Continuous Pathological Speech (ARSCPS); ETSI standard Mel frequency Cepstral Coefficient Front End (ETSI MFCC FE V2.0); Hidden Markov Model Toolkit (HTK); Hidden Models of Markov (HMM); Human/Machine (H/M); Technologies of

Paper 71: Personalized E-Learning Recommender System using Multimedia Data

Abstract: Due to the huge amounts of online learning materials, e-learning environments are becoming very popular as means of delivering lectures. One of the most common e-learning challenges is how to recommend quality learning materials to the students. Personalized e-learning recommender systems help to reduce information overload, which tailor learning material to meet individual student’s learning needs. This research focuses on using various recommendation and data mining techniques for personalized learning in e-learning environment.

Author 1: Hayder Murad

Author 2: Linda Yang

Keywords: E-Learning; recommender system; data mining

Paper 72: Level of Confidence in Software Effort Estimation by an Intelligent Fuzzy – Neuro - Genetic Approach

Abstract: Organizations are struggling to deliver the expected software functionality and quality in scheduled time and prescribed budget. Despite availability of numerous advanced effort estimation techniques overestimation and underestimation occur on a vast scale and results in project failures and significant loss to the organization. The paper proposes machine learning based approach to calculate the optimized effort and level of confidence. Genetically trained neural network evaluates the optimum effort for given COCOMO II variables. The level of confidence is evaluated by fuzzy logic and indicates the percentage that the predicted effort will not exceed the limits.

Author 1: Poonam Rijwani

Author 2: Sonal Jain

Keywords: COCOMO II; artificial neural networks; genetic algorithm; fuzzy logic

Paper 73: A Controlled Environment Model for Dealing with Smart Phone Addiction

Abstract: Smart phones are commonly used in most parts of the world and it is difficult to find a society that is not affected by the smart phone culture. But the usage of smart phone is crossing the limit of being used as a facility towards high level of abnormal dependency on the phone. This dependency can reach to the point where we have no longer control on the over-use and hence the negative impacts it can cause to our lives. The worst situation is that people do not even consider that this dependency is actually a type of addiction and we need to find some solutions to deal with it. In this research paper, we identify symptoms that show the existence of smart phone addiction and demonstrate that this addiction has an effect on the quality and even quantity of people’ lives and it can ultimately affect the whole society. We propose solutions to deal with smart phone addiction and propose the design of a smart phone application to reduce the level of abnormal dependency on smart phones.

Author 1: Irfan Uddin

Author 2: Adeel Baig

Author 3: Abid Ali Minhas

Keywords: Smart phone addiction; Abnormal use of smart phone; Healthy society; Dealing with smart phone addiction

Paper 74: On the Distinction of Subjectivity and Objectivity of Emotions in Texts

Abstract: Emotion classification in texts is an instance of the text classification problem. It therefore could apply some existing text classifiers by considering each emotion as a label of the text. However, most of recent works does not differentiate the subjectivity and objectivity of the same emotion in the text. This paper firstly builds some datasets whose labels are emotion, in which the subject and object of the same emotion are considered as two separated labels. Secondly, this paper evaluates some existing classifiers via some scenarios on the built datasets. The results are then discussed on some difficulties of these kinds of problem.

Author 1: Manh Hung Nguyen

Keywords: Text classification; Emotion classification; subjec-tive emotion; objective emotion

Paper 75: First Out First Served Algorithm for Mobile Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) have recently gained tremendous attention as they cover a vast range of applications requiring an important number of sensor nodes deployed in the area of interest to measure physiological types of data and send it back to the base station for further analysis and treatment. Many routing protocols have been proposed to perform data routing towards the destination in accordance with energy consumption,end-to-end delay and throughput. In this paper,the First Out First Served algorithm for cluster based routing in Mobile Wireless Sensor Networks was presented. The algorithm aims to increase packet reception within the cluster in a highly constrained environment.The results prove the efficiency of the proposed algorithm in increasing the reception of data packets by the cluster head and enhancing the Radio_Coef ficient Diff parameter of the network.

Author 1: Anouar Bouirden

Author 2: Maryam El Azhari

Author 3: Ahmed Toumanari

Author 4: Ahmed Aharoune

Keywords: MWSNs; WSN; Packet reception; energy consump-tion; convex area; Mobility Manager

Paper 76: Developing Disease Classification System based on Keyword Extraction and Supervised Learning

Abstract: The Evidence-Based Medicine (EBM) is emerged as the helpful practice for medical practitioners to make decisions with available shreds of evidence along with their professional ex-pertise. In EBM, the medical practitioners suggest the medication on the basis of underlying information of patients descriptions and medical records (mostly available in textual form). This paper presents a novel and efficient method for predicting the correct disease. Since these type of tasks are generally accounted as the multi-class classifying problem, therefore, a large number of records are needed, so a large number of records will be entertained in higher n-dimensional space. Our system, as proposed in this paper, will utilise the key-phrases extraction techniques to scoop out the meaningful information to reduce the size of textual dimension, and, the suite of machine learning algorithms for classifying the diseases efficiently. We have tested the proposed approach on 6 different diseases i.e. Asthma, Hypertension, Diabetes, Fever, Abdominal issues, and Heart problems over the dataset of 690 patients. With key-phrases tested in the range [3,7] features, SVM has shown the highest (93.34%, 95%) F1-score and accuracy.

Author 1: Muhammad Suffian

Author 2: Muhammad Yaseen Khan

Author 3: Shuakat Wasi

Keywords: Natural language processing; Machine Learning; Multi-Class Classification; Patient descriptions; Keyword Extraction

Paper 77: Deep Learning based Object Distance Measurement Method for Binocular Stereo Vision Blind Area

Abstract: Visual field occlusion is one of the causes of urban traffic accidents in the process of reversing. In order to meet the requirements of vehicle safety and intelligence, a method of target distance measurement based on deep learning and binocular vision is proposed. The method first establishes binocular stereo vision model and calibrates intrinsic extrinsic and extrinsic parameters, uses Faster R-CNN algorithm to identify and locate obstacle objects in the image, then substitutes the obtained matching points into a calibrated binocular stereo model for spatial coordinates of the target object. Finally, the obstacle distance is calculated by the formula. In different positions, take pictures of obstacles from different angles to conduct physical tests. Experimental results show that this method can effectively achieve obstacle object identification and positioning, and improve the adverse effect of visual field blindness on driving safety.

Author 1: Jiaxu Zhang

Author 2: Shaolin Hu

Author 3: Haoqiang Shi

Keywords: deep learning; computer vision; binocular stereo vision; intelligent transportation

Paper 78: Light but Effective Encryption Technique based on Dynamic Substitution and Effective Masking

Abstract: Cryptography and cryptanalysis are in ever-lasting struggle. As the encryption techniques advance, the cryptanalysis techniques advance as well. To properly face the great danger of the cryptanalysis techniques, we should diligently look for more effective encryption techniques. These techniques must properly handle any weaknesses that may be exploited by hacking tools. We address this problem by proposing an innovative encryption technique. Our technique has unique features that make it different from the other standard encryption methods. Our method advocates the use of dynamic substitution and tricky manipulation operations that introduce tremendous confusion and diffusion to ciphertext. All this is augmented with an effective key expansion that not only allows for implicit embedment of the key in all of the encryption steps but also produces very different versions of this key. Experiments with our proof-of-concept prototype showed that our method is effective and passes very important security tests.

Author 1: Muhammed Jassem Al-Muhammed

Keywords: Encryption techniques; dynamic substitution; key expansion; directive based manipulation; block masking

Paper 79: Duplicates Detection Within Incomplete Data Sets Using Blocking and Dynamic Sorting Key Methods

Abstract: In database records duplicate detection, blocking method is commonly used to reduce the number of comparisons between the candidate record pairs. The main procedure in this method requires selecting attributes that will be used as sorting keys. Selection accuracy is essential in clustering candidates records that are likely matched in the same block. Nevertheless, the presence of missing values affects the creation of sorting keys and this is particularly undesirable if it involves the attributes that are used as the sorting keys. This is because, consequently, records that are supposed to be included in the duplicate detection procedure will be excluded from being examined. Thus, in this paper, we propose a method that can deal with the impact of missing values by using a dynamic sorting key. Dynamic sorting is an extension of blocking method that essentially works on two functions namely uniqueness calculation function (UF) (to choose unique attributes) and completeness function (CF) (to search for missing values). We experimented a particular blocking method called as sorted neighborhood with a dynamic sorting key on a restaurant data set (that consists of duplicate records) obtained from earlier research in order to evaluate the method’s accuracy and speed. Hypothetical missing values were applied to testing data set used in the experiment, where we compare the results of duplicate detection with (and without) dynamic sorting key. The result shows that, even though missing values are present, there is a promising improvement in the partitioning of duplicate records in the same block.

Author 1: Abdulrazzak Ali

Author 2: Nurul A. Emran

Author 3: Siti A. Asmai

Author 4: Awsan Thabet

Keywords: Duplicate detection; Incomplete Data Set; Blocking Methods; Sorting key; Attribute Selection

Paper 80: An Agent Cellular Residential Mobility Model : From Functional and Conceptual View

Abstract: Residential mobility is of great challenge to sustainable cities. Developing computer models based simulation could be a powerful tool to support informing urban decisions especially with the fact that half of the world’s population now lives in cities. The present paper presents our detailed model of residential mobility which use an alliance Multi-agent systems and Cellular automata (MAS-CA) approach. Conventional Urban modelling approaches will be presented firstly with a distinct light sheded on the alliance CA-MAS approach. The model will be then exposed in its two functional and conceptual views. At the end, results of a scenario of growth population is simulated and discussed. Results of the simulation shows significant conformity with the underlying model hypothesis.

Author 1: Elarbi Elalaouy

Author 2: Khadija Rhoulami

Author 3: Moulay Driss Rahmani

Keywords: Residential mobility; land use change; computer model; multi-agent systems; cellular automata

Paper 81: Urdu Sentiment Analysis

Abstract: Internet is the most significant source of getting up thoughts, surveys for a product, and reviews for any type of service or activity. A Bulky amount of reviews are produced on daily basis on the cyberspace about online products and objects. For example, many individuals share their remarks, reviews and feelings in their own language utilizing social media networks such as twitter and so on. Considering their colossal Quantity and size, it is exceedingly knotty to look at with and interpret specified surveys. Sentiment Analysis (SA) aims at extracting people’s opinion, felling and thought from their reviews in social websites. SA has recently gained significant consideration, however the vast majority of the resources and frameworks constructed so far are tailored to English as well as English like Western languages. The requirement for designing frameworks for different dialects is expanding, particularly as blogging and micro-blogging sites are becoming popular. This paper presents a comprehensive review of approaches of Urdu sentiment analysis and outlines of relevant gaps in the literature.

Author 1: Khairullah Khan

Author 2: Wahab Khan

Author 3: Atta Ur Rahman

Author 4: Aurangzeb Khan

Author 5: Asfandyar Khan

Author 6: Ashraf Ullah Khan

Author 7: Bibi Saqia

Keywords: Urdu; sentiment analysis; social media; survey