IJACSA Volume 8 Issue 7 - thesai.org

IJACSA Volume 8 Issue 7

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

View Full Issue

Paper 1: A Better Comparison Summary of Credit Scoring Classification

Abstract: The credit scoring aim is to classify the customer credit as defaulter or non-defaulter. The credit risk analysis is more effective with further boosting and smoothing of the parameters of models. The objective of this paper is to explore the credit score classification models with an imputation technique and without imputation technique. However, data availability is low in case of without imputation because of missing values depletion from the large dataset. On the other hand, imputation based dataset classification accuracy with linear method of ANN is better than other models. The comparison of models with boosting and smoothing shows that error rate is better metric than area under curve (AUC) ratio. It is concluded that artificial neural network (ANN) is better alternative than decision tree and logistic regression when data availability is high in dataset.

Author 1: Sharjeel Imtiaz

Author 2: Allan J. Brimicombe

Keywords: Credit score data mining; classification; artifical neural network; imputation

Paper 2: MobisenseCar: A Mobile Crowd-Based Architecture for Data Acquisition and Processing in Vehicle-Based Sensing

Abstract: The use of wireless technology via smartphone allows designing smartphone applications based on OBD-II for increasing environment sensing. However, uploading of vehicle’s diagnostics data via car driver’s tethered smart phone attests a long Internet latency when a large number of concurrent users use the remote mobile crowdsensing server application simultaneously, which increases the communication cost. The large volume of data would also challenge the traditional data processing framework. This paper studies design functionalities of mobile crowdsensing architecture applied to vehicle-based sensing for handling a huge amount of sensor data collected by those vehicle-based sensors equipped with a smart device connected to the OBD-II interface. The proposed MobiSenseCar uses Node.js, a web server architecture based on single-thread event loop approach and Apache Hive platform responsible for analyzing vehicle’s engine data. The Node.JS is 40% faster than the traditional web server side features thread-based approach. Experiment results show that MapReduce algorithm is highly scalable and optimized for distributed computing. With this mobile crowdsensing architecture it was possible to monitor information of car’s diagnostic system condition in real time, improving driving ability and protect the environment by reducing vehicle emissions.

Author 1: Lionel Nkenyereye

Author 2: Jong Wook Jang

Keywords: Mobile crowdsensing; data processing; web services; hadoop; hiveQL; OBD-II

Paper 3: Impedance Matching of a Microstrip Antenna

Abstract: Microstrip patch antennas play a very significant role in communication systems. In recent years, the study to improve their performances has made great progression, and different methods have been proposed to optimize their characteristics such as the gain, the bandwidth, the impedance matching and the resonance frequency. This paper presents a new method that allows to ameliorate the impedance matching, thus to increase the gain of a rectangular microstrip antenna. This method is based on the adaptation technique using a simple “L” matching network. The originality of this work is the exploitation of the principle of causality that permits to detect the problems of reflected waves and to obtain the suitable placement of components that constitute the matching circuit.

Author 1: Sameh Khmailia

Author 2: Hichem Taghouti

Author 3: Sabri Jmal

Author 4: Abdelkader Mami

Keywords: Impedance matching; microstrip antenna; “L” matching network; bond graph model; principle of causality; wave matrix; scattering matrix; transmission and reflection characteristics

Paper 4: Development of A Clinically-Oriented Expert System for Differentiating Melanocytic from Non-melanocytic Skin Lesions

Abstract: Differentiating melanocytic from non-melanocytic (MnM) skin lesions is the first and important step required by clinical experts to automatically diagnosis pigmented skin lesions (PSLs). In this paper, a new clinically-oriented expert system (COE-Deep) is presented for automatic classification of MnM skin lesions through deep-learning algorithms without focusing on pre- or post-processing steps. For the development of COE-Deep system, the convolutional neural network (CNN) model is employed to extract the prominent features from region-of-interest (ROI) skin images. Afterward, these features are further purified through stack-based autoencoders (SAE) and classified by a softmax linear classifier into categories of melanocytic and non-melanocytic skin lesions. The performance of COE-Deep system is evaluated based on 5200 clinical images dataset obtained from different public and private resources. The significance of COE-Deep system is statistical measured in terms of sensitivity (SE), specificity (SP), accuracy (ACC) and area under the receiver operating curve (AUC) based on 10-fold cross validation test. On average, the 90% of SE, 93% of SP, 91.5% of ACC and 0.92 of AUC values are obtained. It noticed that the results of the COE-Deep system are statistically significant. These experimental results indicate that the proposed COE-Deep system is better than state-of-the-art systems. Hence, the COE-Deep system is able to assist dermatologists during the screening process of skin cancer.

Author 1: Qaisar Abbas

Keywords: Skin cancer; melanocytic; non-melanocytic; dermoscopy; deep learning; convolutional neural network; stack-based autoencoders

Paper 5: Feature Selection and Extraction Framework for DNA Methylation in Cancer

Abstract: Feature selection methods for cancer classification are aimed to overcome the high dimensionality of the biomedical data which is a challenging task. Most of the feature selection methods based on DNA methylation are time consuming during testing phase to identify the best pertinent features subset that are relevant to accurate prediction. However, the hybridization between feature selection and extraction methods will bring a method that is far fast than only feature selection method. This paper proposes a framework based on both novel feature selection methods that employ statistical variation, standard deviation and entropy, along with extraction methods to predict cancer using three new features, namely, Hypomethylation, Midmethylation and Hypermethylation. These new features represent the average methylation density of the corresponding three regions. The three features are extracted from the selected features based on the analysis of the methylation behavior. The effectiveness of the proposed framework is evaluated by the breast cancer classification accuracy. The results give 98.85% accuracy using only three features out of 485,577 features. This result proves the capability of the proposed approach for breast cancer diagnosis and confirms that feature selection and extraction methods are critical for practical implementation.

Author 1: Abeer A. Raweh

Author 2: Mohammad Nassef

Author 3: Amr Badr

Keywords: DNA methylation, feature selection; feature extraction; cancer classification; epigenetics; biomarkers; hypomethylation; hypermethylation; methylation

Paper 6: Research Pathway towards MAC Protocol in Enhancing Network Performance in Wireless Sensor Network (WSN)

Abstract: The applications and utility of Wireless Sensor Network (WSN) have increased its pace in making an entry to the commercial market since the last five years. It has successfully established its association with Internet-of-Things (IoT) and other reconfigurable networks. However, in this advent of exponential progress in technology, WSN still suffers from elementary problems of energy efficiency, scalability, delay, and latency where Medium Access Control (MAC) protocols hold the primary responsibility. This paper reviewed the frequently used MAC protocols and studied their advantages and limitations followed by most recently carried out implementation work towards WSN performance enhancement. The paper finally outlines the unsolved problems from the existing research work and discussed briefly the research gap followed by a chalked out plan of tentative future work to address the research gap from existing review.

Author 1: Anitha K

Author 2: Usha S

Keywords: Delay; energy issues; latency; MAC protocol; scalability; Wireless Sensor Network (WSN)

Paper 7: Reduced-Latency and Area-Efficient Architecture for FPGA-Based Stochastic LDPC Decoders

Abstract: This paper introduces a new field programmable gate array (FPGA) based stochastic low-density parity-check (LDPC) decoding process, to implement fully parallel LDPC-decoders. The proposed technique is designed to optimize the FPGA logic utilisation and to decrease the decoding latency. In order to reduce the complexity, the variable node (VN) output saturated-counter is removed and each VN internal memory is mapped only in one slice distributed RAM. Furthermore, an efficient VN initialization, using the channel input probability, is performed to improve the decoder convergence, without requiring additional resources. The Xilinx FPGA implementation shows that the proposed decoding approach reaches high performance along with reduction of logic utilisation, even for short codes. As a result, for a (200, 100) regular codes, a 57% reduction of the average decoding cycles is attained with an important bit error rate improvement, at Eb/N0 = 5.5dB. Additionally, a significant hardware reduction is achieved.

Author 1: Ghania Zerari

Author 2: Abderrezak Guessoum

Keywords: Stochastic decoding; low-density parity-check (LDPC) decoder; field programmable gate array (FPGA)

Paper 8: Towards an SOA Architectural Model for AAL-Paas Design and Implimentation Challenges

Abstract: Ambient Assisted Living (AAL) systems main purpose is to improve the quality of life of special groups of people, including the elderly and people with physical disabilities. Driven by the critical ongoing changes in all modern, industrialized countries, there is a huge interest in IT-based equipment and services these days, to facilitate daily tasks and extend the independency time for these groups. Thence, AAL systems can benefit from the huge advances of both intelligent systems and communication technologies as promising growing research fields. The implementation of such complicated yet vital system should be established on solid bases relying on a standard architecture to satisfy and respond to the needs of heterogeneous stakeholders. This article proposes a Service Oriented Architecture model for Ambient Assisted Living Platform as a Service based on Wireless Sensors Network, it starts by presenting a classification of ambient assisted living services. Secondly, it describes some user and environmental challenges that have an impact on the service qualities. The discussion of architectural trends for AAL systems is included, and the description of challenges in designing and implementing of an effective one. Finally, this paper introduces a new vision of prototypical AAL systems architecture.

Author 1: El murabet Amina

Author 2: Abtoy Anouar

Author 3: Abdellah Touhafi

Author 4: Abderahim Tahiri

Keywords: Ambient Assisted Living (AAL); Ambient Assisted Living Platform as a Service (AAL-PaaS); Service Oriented Architecture (SOA); Wireless Sensors Network (WSN)

Paper 9: Core Levels Algorithm for Optimization: Case of Microwave Models

Abstract: Metaheuristic algorithms are investigated and used by many researchers in different areas. It is crucial to find optimal solutions for all problems under study especially for the ones which require sensitive optimization. Especially, for real case problems, solution quality and convergence speed of the algorithms are highly desired characteristics. In this paper, a new optimization algorithm called Core Levels Algorithm (COLA) based on the use of metaheuristics is proposed and analyzed. In the algorithm, two core levels are applied recursively to create new offsprings from the parent vectors which provides a desired balance on the exploration and exploitation characteristics. The algorithm’s performance is first studied on some well-known benchmark functions and then compared with previously proposed efficient evolutionary algorithms. The experimental results showed that even at the early stages of optimization, obtained values are very close or exactly the same as the optimum values of the analyzed functions. Then, the performance of COLA is investigated on real case problems such as some selected microwave circuit designs. The results denoted that COLA produces stable results and provides high accuracy of optimization without high parameter dependency even for the real case problems.

Author 1: Ali Haydar

Author 2: Ezgi Deniz Ülker

Author 3: Kamil Dimililer

Author 4: Sadik Ülker

Keywords: Metaheuristic algorithms; evolutionary algorithms; microwave circuits, optimization

Paper 10: A Proposed Adaptive Scheme for Arabic Part-of Speech Tagging

Abstract: This paper presents an Arabic-compliant part-of-speech (POS) tagging scheme based on using atomic tag markers that are grouped together using brackets. This scheme promotes the speedy production of annotations while preserving the richness of resultant annotations. The proposed scheme is comprised of two main elements, a new tokenization approach and a custom tool that enables the semi-automatic implementation of this scheme. The proposed model can serve in many scenarios where the user is in a need for better Arabic support and more control over the Part-of-Speech tagging process. This scheme was used to annotate sample narratives and it demonstrated capability and adaptability while addressing the various distinguishing features of Arabic language including its unique declension system. It also sets new baselines that are prospect for further exploration by future efforts.

Author 1: Mohammad Fasha

Keywords: Arabic natural language processing (ANLP); part-of-speech (POS) tagging; part-of-speech tokenization scheme; morpho-syntactic tagging; Arabic declension system

Paper 11: The Performance of the Bond Graph Approach for Diagnosing Electrical Systems

Abstract: The increasing complexity of automated industrial systems, the constraints of competitiveness in terms of cost of production and facility security have mobilized in the last years a large community of researchers to improve the monitoring and the diagnosis of this type of processes. This work proposes a reliable and efficient method for the diagnosis of an electrical system. The improvement of the reliability of the systems depends essentially on the algorithms of fault detection and isolation. The developed method is based on the use of analytical redundancy relations allowing the detection and isolation of faults which occur in the various elements of the system using a structural and causal analysis. In this context, the bond graph appears as an interesting approach since it models physical systems element by element which facilitates the detection and location of faults. The simulation of the system is performed through 20-sim software dedicated to the bond graph applications.

Author 1: Dhia Mzoughi

Author 2: Abderrahmene Sallami

Author 3: Abdelkader Mami

Keywords: Bond graph; faults detection and isolation; electrical system; analytical redundancy relations

Paper 12: Application of the Tabu Search Algorithm to Cryptography

Abstract: Tabu search is a powerful algorithm that has been applied with great success to many difficult combinatorial problems. In this paper, we have designed and implemented a symmetrical encryption algorithm whose internal structure is mainly based on Tabu search algorithm. This heuristic performs multiple searches among different solutions and stores the best solutions in an adaptive memory. First of all, we coded the encryption problem by simulating a scheduling problem. Next, we have used an appropriate coding for our problem. Then we used the suitable evaluation function. Through the symmetric key generated by our algorithm, we have illustrated the principle of encryption and decryption. The experimentations of our approach are given at the end of this paper, from which we examined our new system strengths and the elements that could be improved.

Author 1: Zakaria KADDOURI

Author 2: Fouzia OMARY

Keywords: Symmetric encryption; heuristic; Tabu search; algorithm; scheduling problem; combinatorial problems

Paper 13: Hearing Aid Method by Equalizing Frequency Response of Phoneme Extracted from Human Voice

Abstract: Hearing aid method by equalizing frequency response of phoneme which is extracted from human voice is proposed. One of the problems of the existing hearing aid is poor customization of the frequency response compensation. Frequency response characteristics are different by the person who need hearing aid. The proposed hearing aid is based on frequency response equalization by phoneme by phoneme. Frequency characteristics of phoneme are to be equalized. This is the specific feature of the proposed hearing aid method. Through experiments, it is found that the proposed hearing aid by phoneme is superior to the conventional hearing aid.

Author 1: Kohei Arai

Author 2: Takuto Konishi

Keywords: Hearing aid; phoneme; frequency response; equalization filter; hidden markov model (HMM)

Paper 14: New Divide and Conquer Method on Endmember Extraction Techniques

Abstract: In hyperspectral imagery, endmember extraction (EE) is a main stage in hyperspectral unmixing process where its role lies in extracting distinct spectral signature, endmembers, from hyperspectral image which is considered as the main input for unsupervised hyperspectral unmixing to generate the abundance fractions for every pixel in hyperspectral data. EE process has some difficulties. There are less distinct endmembers than its mixed background; also, there are endmembers that have rare occurrences in data that are considered as difficulties in EE process. In this paper, we propose a new technique that uses divide and conquer method for EE process to find out these difficult (rare or less distinct) endmembers. divide and conquer method is used to divide hyperspectral data scene to multiple divisions and take each division as a standalone scene to enable endmember extraction algorithms (EEAs) to extract difficult endmembers easily and finally conquer all extracted endmembers from all divisions. We implemented this method on real dataset using three EEAs: ATGP, VCA, and SGA and recorded the results that outperform the results from usual endmember extraction techniques methods in all used algorithms.

Author 1: Ihab Samir

Author 2: Bassam Abdellatif

Author 3: Amr Badr

Keywords: Endmember extraction algorithm (EEA); endmember extraction (EE); automatic target generation process (ATGP); hyperspectral imagery; simplex growing algorithm (SGA); hyperspectral unmixing; vertex component analysis (VCA); divide and conquer method

Paper 15: Real-Time Analysis of Students’ Activities on an E-Learning Platform based on Apache Spark

Abstract: Real time analytics is the capacity to extract valuables insights from data that comes continuously from activities on the web or network sensors. It is largely used in web based business to drive decisions based on user’s experiences, such dynamic pricing and personalized advertising. Many universities have adopted web based learning in their learning process. They use data-mining techniques to better understand students’ behavior, and most of the tools developed are based on historical and stored data, and do not allow real time reactivity. Online activities of learners generate at high speed a huge amount of data in form of users’ interactions which have all characteristics to be considered as Big data. Deal with volume and velocity of these data in order to inform and enable decisions-makers to act at right time lead us to use new methods to capture E-Learning data, and process it in real time. This paper focuses on the design and implementation of modern and hybrid real time data pipeline architecture using Apache Flume to collect data, Apache Spark as an unified engine computation for performing analytics on students’ activities data and Apache Hive as a data warehouse for storing the processed data and for use by various reporting tools. To conceive this platform we conduct an experiment on Moodle database source.

Author 1: Abdelmajid Chaffai

Author 2: Larbi Hassouni

Author 3: Houda Anoun

Keywords: Real time analytics; e-learning; big data; Hadoop; spark; Moodle; change data capture; streaming; data visualization clustering

Paper 16: Reducing Dimensionality in Text Mining using Conjugate Gradients and Hybrid Cholesky Decomposition

Abstract: Generally, data mining in larger datasets consists of certain limitations in identifying the relevant datasets for the given queries. The limitations include: lack of interaction in the required objective space, inability to handle the data sets or discrete variables in datasets, especially in the presence of missing variables and inability to classify the records as per the given query, and finally poor generation of explicit knowledge for a query increases the dimensionality of the data. Hence, this paper aims at resolving the problems with increasing data dimensionality in datasets using modified non-integer matrix factorization (NMF). Further, the increased dimensionality arising due to non-orthogonally of NMF is resolved with Cholesky decomposition (cdNMF). Initially, the structuring of datasets is carried out to form a well-defined geometric structure. Further, the complex conjugate values are extracted and conjugate gradient algorithm is applied to reduce the sparse matrix from the data vector. The cdNMF is used to extract the feature vector from the dataset and the data vector is linearly mapped from upper triangular matrix obtained from the Cholesky decomposition. The experiment is validated against accuracy and normalized mutual information (NMI) metrics over three text databases of varied patterns. Further, the results prove that the proposed technique fits well with larger instances in finding the documents as per the query, than NMF, neighborhood preserving: nonnegative matrix factorization (NPNMF), multiple manifolds non-negative matrix factorization (MMNMF), robust non-negative matrix factorization (RNMF), graph regularized non-negative matrix factorization (GNMF), hierarchical non-negative matrix factorization (HNMF) and cdNMF.

Author 1: Jasem M. Alostad

Keywords: Data mining; non-integer matrix factorization; Cholesky decomposition; conjugate gradient algorithm

Paper 17: An Efficient Distributed Traffic Events Generator for Smart Highways

Abstract: This paper deals with a spatiotemporal traffic events generator for real highway networks. The goal is to use the event generator to test real-time and batch traffic analysis applications. In this context, we represent a highway network as an oriented graph based on the geographic data of the different sensors locations. The traffic is generated based on a socio-cultural calendar using a virtual clock to speed up the simulation process. In order to enable our generator to support the global worldwide highway networks, we propose a dynamic sized distributed architecture based on multi-agent systems. In this platform, we distinguish the physical model based on sensors from the logical model based on an oriented graph. The architecture of the simulator and the results of some of its implementations applied to the Moroccan highway network are presented.

Author 1: Abdelaziz Daaif

Author 2: Omar Bouattane

Author 3: Mohamed Youssfi

Author 4: Oum El Kheir Abra

Keywords: Event generator; smart highway; simulation; multi-agent systems; distributed computing

Paper 18: The Role of Strategic Information Systems (SIS) in Supporting and Achieving the Competitive Advantages (CA): An Empirical Study on Saudi Banking Sector

Abstract: The purpose of this research paper is to identify the significant role of Strategic Information Systems (SIS) in supporting the Competitive Advantage (CA). It also explains its role on the dimensions that increase the competitive advantage which are the operational efficiency, information quality and innovation. In order to achieve the goal of this study and to collect the primary data, the researchers designed a survey, in the form of an electronic questionnaire. This survey instrument consisted of 20 questions. It was distributed to members of the study sample, which contains the managers at all levels, and the employees in the Saudi banking sector. The number of the participants included in the survey was 147. The results of this study revealed that there is a significant role of strategic information systems on increasing operational efficiency, improving the quality of information and promoting innovation. This in turn enabled the organizations to achieve higher levels of competitive advantages. The strategic information systems have deep consequences for organizations that adopt them; managers could achieve great and sustainable competitive advantages from such systems if carefully considered and developed. On the other hand, this study was conducted in the banking sector in KSA context. So, more research is needed in other sectors and in the context of other countries; to confirm and generalize the results. Finally, the paper’s primary value lies in its ability to provide the evidence that the strategic information systems play a significant role in supporting and achieving the competitive advantages in Saudi Arabia, particularly in the banking sector. Since there was a lack of such research in the Saudi context, this paper can provide a theoretical basis for future researchers as well as practical implications for managers.

Author 1: Nisreen F. Alshubaily

Author 2: Abdullah A. Altameem

Keywords: Strategic information systems (SIS); competitive advantage (CA); operational efficiency; information quality; innovation

Paper 19: The Optimization of Query Processing in Seabase Cloud Databases based on CCEVP Model

Abstract: A cloud database is a database usually installed on cloud computing software platforms. There are several methods for query processing in cloud databases. This study tried to optimize query processing in the SeaBase cloud database and reduce query processing time. This method used adaptability for optimization. This method was designed for cloud-based databases. The algorithm is composed of three components: 1) multi cloud query separator; 2) query similarity detector based on the execution plan; and 3) replacement policy. This method is implemented as a system for a fully object-oriented simulation. The system is added to the SeaBase as an agent. The evaluation result show that this method reduced response time by 1.9 percent.

Author 1: Abdulkadir ÖZDEMIR

Author 2: Hasan Asil

Keywords: SeaBase; optimization; query processing; database; adaption

Paper 20: A Copula Statistic for Measuring Nonlinear Dependence with Application to Feature Selection in Machine Learning

Abstract: Feature selection in machine learning aims to find out the best subset of variables from the input that reduces the computation requirement and improves the predictor performance. In this paper, a new index based on empirical copulas, termed the Copula Statistic (CoS) to assess the strength of statistical dependence and for testing statistical independence is introduced. It is shown that this test exhibits higher statistical power than other indices. Finally, the CoS is applied to feature selection in machine learning problems, which allow a demonstration of the good performance of the CoS.

Author 1: Mohsen Ben Hassine

Author 2: Lamine Mili

Author 3: Kiran Karra

Keywords: Copula; multivariate dependence; nonlinear systems; feature selection; machine learning

Paper 21: Security in OpenFlow Enabled Cloud Environment

Abstract: Inception of flow tables as data plane abstraction, and forwarding rules that are managed by centralized controllers in emerging Software Defined Networks (SDN) has stemmed significant progress in OpenFlow based architectures. SDN is particularly fueled by data center networking and cloud computing. OpenFlow coupled with cloud solutions provide dynamic networking capabilities. With the benefits obtained from network services, security enforcement become more important and need powerful techniques for its implementation. Extensive researches in cloud security bring forward numerous methods of leveraging the SDN architecture with efficient security enforcement. The future of SDN and mobile networks is also enlightened if security models are satisfactory to cover dynamic and flexible requirements of evolving networks. This paper presents a survey of the state of the art research on security techniques in OpenFlow based cloud environments. Security is one of the main aspect of any network. A fair study and evaluation of these methods are carried out in the paper along with the security considerations in SDN and its enforcement. The security issues and recommendations for 5g network are covered briefly. This work provides an understanding of the problem, its current solution space, and anticipated future research directions.

Author 1: Abdalla Alameen

Author 2: Sadia Rubab

Author 3: Bhawna Dhupia

Author 4: Manjur Kolhar

Keywords: Software defined networks; OpenFlow; 5G network component; ONF; virtualization; SDN security framework; future security networks

Paper 22: Analysis of Received Power Characteristics of Commercial Photodiodes in Indoor Los Channel Visible Light Communication

Abstract: To date, the photodiode still the first choice component is used in optical communication, especially for visible light communication (VLC) system. It has advantages of speed, energy consumption, and sensitivity, compared to other devices (e.g. image sensor). There are many practical implementations of high-speed VLC which uses photodiode. Commercially available photodiode typically have specific characteristics, so that it needs some consideration to be used as optimal receiver devices in VLC system. In this paper, analysis of received power characteristics of the photodiode in indoor line-of-sight (LoS) channel of VLC system is discussed. MATLAB® simulation is used as approach model (student version). The experiments are done by changing several parameters such as the semi-angle half power of the transmitter, distance from the transmitter to receiver, room size, field-of-view (FOV), lens index and optical filter gain. From the results, it can be known that distance, room size, FOV and LED power factor to have linear characteristic against the received power of commercial photodiode. Also in LoS channel model, the gain of optical filter and lens index plays an important role in defining the characteristics of received power.

Author 1: Syifaul Fuada

Author 2: Angga Pratama Putra

Author 3: Trio Adiono

Keywords: Commercial photodiodes; LoS channel; power received; visible light communication

Paper 23: An Efficient Spectral Amplitude Coding (SAC) Technique for Optical CDMA System using Wavelength Division Multiplexing (WDM) Concepts

Abstract: This article introduces an improved method for Optical Code Division Multiple Access system (OCDMA). In this scheme, a hybrid technique is used in which Wavelength Division Multiplexing (WDM) is merged with Spectral Amplitude Coding (SAC) to efficiently diminish Multiple Access Interference (MAI) and alleviate the impact of Phase Induced Intensity Noise (PIIN) appearing in photo-detecting process. The proposed technique SAC-OCDMA/WDM MP (SW-MP) is implemented by using Matrix Partitioning (MP) code family, which is constructed via merging mathematics sequence and algebraic approaches. The key notion is to create the code patterns in SAC domain, then diagonally replicate it in the wavelength domain as blocks which preserves the same code patterns of a given code weight. The SW-MP code family preserves convenient code length property which gives flexibility in transmitter-receiver design. It is reported that the proposed scheme has potential to remove MAI proficiently and improve the system performance significantly.

Author 1: Hassan Yousif Ahmed

Author 2: Medien Zeghid

Keywords: Optical Code Division Multiple Access System (OCDMA); Multiple Access Interference (MAI); Spectral Amplitude Coding (SAC); Wavelength Division Multiplexing (WDM); SAC-OCDMA/WDM MP (SW-MP) code; Cross Correlation (CC)

Paper 24: Efficient Key Agreement and Nodes Authentication Scheme for Body Sensor Networks

Abstract: Technological evolvement of Wireless Sensor Networks (WSNs) gave birth to an attractive research area for health monitoring called Body Sensor Network (BSN). In BSN tiny sensor nodes sense physiological data of patients under medical health care and transmit this data to Base Station (BS) and then forward to Medical Server (MS). BSN is exposed to security threats due to vulnerable wireless channel. Protection of human physiological data against adversaries is a major addressable issue while keeping constrained resources of BSN under consideration. Our proposed scheme consists of three stages. In first stage deployment of initial secret key by the ward Medical Officer (MO), in second stage secure key exchange and node authentication, in third stage secure data communication are performed. We have compared our proposed scheme with three existing schemes. Our scheme is efficient in computation cost, communication overhead and storage as compared to existing schemes while providing enough security against the adversaries.

Author 1: Jawaid Iqbal

Author 2: Noor ul Amin

Author 3: Arif Iqbal Umar

Author 4: Nizamud Din

Keywords: Body sensor network; hash function; node authentication; key agreement; session key

Paper 25: Design of Efficient Pipelined Router Architecture for 3D Network on Chip

Abstract: As a relevant communication structure for integrated circuits, Network-on-Chip (NoC) architecture has attracted a range of research topics. Compared to conventional bus technology, NoC provides higher scalability and enhances the system performance for future System-on-Chip (SoC). Divergently, we presented the packet-switching router design for 2D NoC which supports 2D mesh topology. Despite the offered benefits compared to conventional bus technology, NoC architecture faces some limitations such as high cost communication, high power consumption and inefficient router pipeline usage. One of the proposed solutions is 3D design. In this context, we suggest router architecture for 3D mesh NoC, a natural extension of our prior 2D router design. The proposal uses the wormhole switching and employs the turn mod negative-first routing algorithm Thus, deadlocks are avoided and dynamic arbiter are implemented to deal with the Quality of Service (QoS) expected by the network. We also adduce an optimization technique for the router pipeline stages. We prototyped the proposal on FPGA and synthesized under Synopsys tool using the 28 nm technology. Results are delivered and compared with other famous works in terms of maximal clock frequency, area, power consumption and estimated peak performance.

Author 1: Bouraoui Chemli

Author 2: Abdelkrim Zitouni

Author 3: Alexandre Coelho

Author 4: Raoul Velazco

Keywords: 3D network on chip; router optimization; turn model; parallel communication; router pipeline stages

Paper 26: UHF RFID Reader Antenna using Novel Planar Metamaterial Structure for RFID System

Abstract: An Ultra High Frequency (UHF) half-loop antenna used in Radio Frequency Identification (RFID) systems is proposed with a planar patterned metamaterial structure of compact size. The size of the planar patterned metamaterial structure is (0.20λ*0.20λ*0.0023λ') mm3. This antenna consists of two metamaterial unit cells having negative permittivity and permeability. Simulation results of input return loss, radiation pattern, and directivity of this antenna are presented using CST software. A comparison between the conventional antenna and the new metamaterial half-loop antenna is also provided. The simulated results show that the metamaterial antenna has a resonance frequency of 0.866 GHz, a realized gain of 1.96 dB, and an efficiency increase of about 20%. Simulation and measurement results are in perfect agreement, which proves that the proposed antenna can operate in the UHF band for RFID systems.

Author 1: Marwa Zamali

Author 2: Lotfi Osman

Author 3: Hedi Ragad

Author 4: Mohamed Latrach

Keywords: Loop antenna; metamaterial; miniaturization; RFID system; UHF band

Paper 27: Factors Influencing Users’ Intentions to Use Mobile Government Applications in Saudi Arabia: TAM Applicability

Abstract: M-government applications in Saudi Arabia are still at an early stage. In this study, a modified technology acceptance model (TAM) was used to identify and measure the factors that influence users’ intentions to use m-government applications in Saudi Arabia. This study focuses on the relationships between behavioural intention to use (BIU) and six independent factors: three TAM constructs (perceived usefulness [PU], attitude towards use [ATU], and perceived ease of use [PEU]) and three external factors: perceived trustworthiness [TRU], perceived security [SEC], and awareness [AWAR]). Only PU, ATU and TRU had a significant positive influence on BIU for m-government applications. The results also showed that most participants had a positive attitude towards using m-government applications. Overall, the results demonstrate that the model is suitable in the Saudi m-government context.

Author 1: Raed Alotaibi

Author 2: Luke Houghton

Author 3: Kuldeep Sandhu

Keywords: TAM; Saudi Arabia; e-government; m-government applications

Paper 28: Image and AES Inspired Hex Symbols Steganography (IAIS) for Anti-Forensic Artifacts

Abstract: Technology (including mobiles and computers) has become a basic, indispensable need in our daily life. With an initial purpose of achieving basic functions such as communication, technology has evolved into a virtual gate to the whole world connecting individuals through social media and various websites and applications. Most importantly, technology became the reservoir of our personal information and important, sensitive data. This has led to increased risks of security breaches and data thefts demanding countermeasure approaches. One of these approaches is Steganography. Steganography is a data hiding approach that allows for invisible, relatively safe communication. Several forms of steganography have been developed, among which are Image steganography and our previously developed AES Inspired Steganography. In this paper we propose a new variation in which we combine both of these approaches, Image and AES Inspired Steganography (IAIS). This approach proposes hiding the hex symbol format of the encrypted secret data into a carrier image file. The image file is converted to a hexadecimal representation in which the hex symbol could be embedded without applying any noticeable changes to the original image. Deciphering the hidden information requires secret keys agreed upon by the communicating parties confidentially. These carrier files can be exchanged among mobile devices and/or computers. Comparisons between the original cover images and the cover images with the hidden text have shown that no changes occurred in the colour histogram of the images. However, the noise test has shown that exposure to noise can affect the hexadecimal content of the image, hence the embedded hex symbol representation of the secret text.

Author 1: Somyia M. Abu Asbeh

Author 2: Sarah M. Hammoudeh

Author 3: Arab M. Hammoudeh

Keywords: Mobile Forensics, Anti-Forensics, Data Hiding, Steganography, AES, AIS.

Paper 29: Network Traffic Classification using Machine Learning Techniques over Software Defined Networks

Abstract: Nowadays Internet does not provide an exchange of information between applications and networks, which may results in poor application performance. Concepts such as application-aware networking or network-aware application programming try to overcome these limitations. The introduction of Software-Defined Networking (SDN) opens a path towards the realization of an enhanced interaction between networks and applications. SDN is an innovative and programmable networking architecture, representing the direction of the future network evolution. Accurate traffic classification over SDN is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service (QoS) to providing operators with useful forecasts for long-term provisioning. In this paper, four variants of Neural Network estimator are used to categorize traffic by application. The proposed method is evaluated in the four scenarios: feedforward; Multilayer Perceptron (MLP); NARX (Levenberg-Marquardt) and NARX (Naïve Bayes). These scenarios respectively provide accuracy of 95.6%, 97%, 97% and 97.6%.

Author 1: Mohammad Reza Parsaei

Author 2: Mohammad Javad Sobouti

Author 3: Seyed Raouf khayami

Author 4: Reza Javidan

Keywords: Software defined networks; openflow; traffic classification; neural network; multilayer perceptron

Paper 30: A New Approach for Leukemia Identification based on Cepstral Analysis and Wavelet Transform

Abstract: This paper implements a new leukemia identification method which depends on Mel frequency cepstral coefficient (MFCC) feature extraction and wavelet transform. Leukemia identification is a measurement of blood cell features for detecting the blood cancer of a patient. Blood cell feature extraction is based on transforming the blood cell two dimensional (2D) image into one dimensional (1D) signal and thereafter extracting MFCCs from such signal. Furthermore, discrete wavelet transform (DWT) of the 1D blood cell signals are used for extracting extra MFCCs features to assist the identification procedure. In addition, Wavelet transform with denoising is used to reduce noise and increase classification accuracy. Feature matching/classification of the blood cell to be a normal cell or leukemia cell is performed in the proposed method using five different classifiers. Experimental results of leukemia identification method show that the proposed method is very good with wavelet transform and robust in the presence of noise.

Author 1: Amira Samy Talaat Abou Taleb

Author 2: Amir F. Atiya

Keywords: MFCC; feature extraction; classification; identification system; leukemia

Paper 31: A New Strategy of Validities’ Computation for Multimodel Approach: Experimental Validation

Abstract: The evaluation of validities is a fundamental step in the design of the multimodel approach. Indeed, it is thanks to validities that we estimate the contribution of each base-model in the reproduction of the behavior of the global process in a given operating area. These coefficients are calculated most commonly by the approach of the residues formulated by the distance between the real output and the sub-models’ outputs. In this paper, a strategy allowing to improve the performances of the residues’ approach in terms of precision and robustness is proposed. This strategy is based on a quasi-hierarchical structuring. A simulation example and a validation on a semi-batch reactor showed the interest and the effectiveness of the proposed strategy.

Author 1: Abdennacer BEN MESSAOUD

Author 2: Samia TALMOUDI BEN AOUN

Author 3: Moufida LAHMARI KSOURI

Keywords: Validities; residues’ approach; multimodel; quasi-hierarchical structuring; experimental validation

Paper 32: Simulation and Analysis of Quality of Service (QoS) Parameters of Voice over IP (VoIP) Traffic through Heterogeneous Networks

Abstract: Identifying those causes and parameters that affect the Quality of Service (QoS) of Voice-over-Internet Protocol (VoIP) through heterogeneous networks such as WiFi, WiMAX and between them are carried out using the OPNET simulation tool. Optimization of the network for both intra- and inter-system traffic to mitigate the deterioration of the QoS are discussed. The average value of the jitter of the VoIP traffic traversing through the WiFi-WiMAX network was observed to be higher than that of utilizing WiFi alone at some points in time. It is routinely surmised to be less than that of transiting across the WiFi network only and obviously higher than passing through the increased bandwidth network of WiMAX. Moreover, both the values of the packet end-to-end delay and the Mean Opinion Score (MOS) were considerably higher than expected. The consequences of this optimization, leading to a solution, which can ameliorate the QoS over these networks are analyzed and offered as the conclusion of this ongoing research.

Author 1: Mahdi H. Miraz

Author 2: Suhail A. Molvi

Author 3: Muzafar A. Ganie

Author 4: Maaruf Ali

Author 5: AbdelRahman H. Hussein

Keywords: Voice over Internet Protocol (VoIP); Quality of Service (QoS); Mean Opinion Score (MOS); simulation

Paper 33: A Comparative Analysis of Quality Assurance of Mobile Applications using Automated Testing Tools

Abstract: Use of mobile applications are trending these days due to adoption of handheld mobile devices with operating systems such as Android, iOS and Windows. Delivering quality mobile apps is as important as in any other web or desktop application. Simplification and ease of quality assurance or evaluation in mobile devices is achieved by using automated testing tools. These tools have been evaluated for their features, platforms, code coverage, and efficiency. However, they have not been evaluated and compared to each other for different quality attributes they can enhance in the apps under test. This research study aims to evaluate different testing tools focusing on identifying quality factors they aid to achieve in the apps under test. Furthermore, it aims to measure overall trends of essential quality factors achieved using automated testing tools. The findings of this study are beneficial to the practitioners and researchers. The practitioners need to look up for specific tools which aid them to assure the desired quality factors in the apps under test. The researchers may base their studies on the findings of this study to propose solutions or revise existing tools in order to achieve maximum number of critical quality attributes in the app under test. This study revealed that the trend of automated testing is high on usability, correctness and robustness. Moreover, the trend is average on testability and performance. However, for assurance of extensibility, maintainability, scalability, and platform compatibility, only a few tools are available.

Author 1: Haneen Anjum

Author 2: Muhammad Imran Babar

Author 3: Muhammad Jehanzeb

Author 4: Maham Khan

Author 5: Saima Chaudhry

Author 6: Summiyah Sultana

Author 7: Zainab Shahid

Author 8: Furkh Zeshan

Author 9: Shahid Nazir Bhatti

Keywords: Mobile application; quality assurance; automated testing; testing tools

Paper 34: Detection of Cardiac Disease using Data Mining Classification Techniques

Abstract: Cardiac Disease (CD) is one of the major causes of death. An important task is to identify the Cardiac disease very minutely and precisely. Generally medical diagnostic errors are dangerous and costly. Worldwide they are leading to deaths. Data mining techniques are very important to minimize the diagnostic errors as well as to improve the patient’s safety. Data mining techniques are very effective in designing a medical support system and enrich ability to determine the unseen patterns and associations in clinical data. In this paper, the application of classification technique, decision tree for the detection of heart disease have been introduced. Classification tree uses many factors including age, blood sugar and blood pressure; it can detect the probability of patients fallen in CD by using fewer diagnostic tests which save time and money.

Author 1: Abdul Aziz

Author 2: Aziz Ur Rehman

Keywords: Cardiac disease; classification technique; decision tree; knowledge discovery

Paper 35: Image Encryption Technique based on the Entropy Value of a Random Block

Abstract: The use of digital images in most fields of information technology systems makes these images usually contain confidential information. When these images transmitted via the Internet especially in the Cloud, it becomes necessary to protect these images in a way that ensure putting the confidential information that are contained far away from the attackers. A proposed image encryption technique has been presented in this work. This technique used a secret key that is extracted from the image content itself. Therefore, there is no need to find a secret channel to exchange any key where, sender and receiver authenticate each other with regards to a shared secret key extracted from the image. The technique constructs its secret key that is used to encrypt the image, based on the entropy values of a set of randomly selected blocks from the image itself. Vairous experiments have been conducted to evaluate the strength and performance of the technique. The experimental results shows that the proposed technique can be used effectively in the field of image security to protect and authenticate images.

Author 1: Mohammed A. F. Al-Husainy

Author 2: Diaa Mohammed Uliyan

Keywords: Image security; image encryption; secret key; image authentication.

Paper 36: Enhancing Lean Software Development by using Devops Practices

Abstract: Competition between companies has made a great pressure to produce new features continuously as fast as possible, subsequently successful software companies needs to learn more about customers and get new features out to them more rapidly. Lean software development cannot integrate between development and operation teams. DevOps enables this merge between them and creates operational parts as one part of the development process and made it up to date during the development phase, so reduced errors during the deployment. The purpose of this paper is to investigate how can use devops practices to improve the performance of lean software development production process and introduces a new framework that merge lean and devops process. The research has been evaluated on a sample of 2 departments in Faculty of Commerce at Helwan University. The results of this work have led to reduce the response delivery time for customers and rapid feedback provides accurate expectations for customer needs that lead to lower levels of deployment pains and lower change fail rates.

Author 1: Ahmed Bahaa Farid

Author 2: Yehia Mostafa Helmy

Author 3: Mahmoud Mohamed Bahloul

Keywords: Lean software development; DevOps; development & IT operations; continuous delivery; monitoring; continuous integration

Paper 37: SEUs Mitigation on Program Counter of the LEON3 Soft Processor

Abstract: Analyzing and evaluating the sensitivity of embedded systems to soft-errors have always been a challenge for aerospace or safety equipment designer. Different automated fault-injection methods have been developed for evaluating the sensitivity of integrated circuit. Also many techniques have been developed to get a fault tolerant architecture in order to mask and mitigate fault injection in a circuit. Fault injection mitigation and repair techniques are applied together on LEON3 processor in goal to study the reliability of a soft-core. The so-called NETlist Fault Injection (NETFI+) tool is a fault injection techniques used in this paper. The prediction of Single Event Upset (SEU) error-rates between radiation ground testing and FPGA implementation have been done with good and accurate result. But no functional simulations have been performed. A Triple Modular Redundancy (TMR) is used in this paper as a repair technique versus fault injection. This paper analyses the effectiveness of fault tolerant method on LEON3 soft-core running a benchmark. It starts by evaluating the behavior of LEON3’s program counter against Single Event Upset error-rate accuracy between the functional simulation and the FPGA emulation and an analysis of the LEON3 reliability in presence of fault tolerant technique. The objective is to offer, through the new version of NETFI+ with introducing a fault tolerant technique, the possibility to designers to evaluate the benefits of SEUs mitigation for the LEON3 processor on the program counter.

Author 1: Afef KCHAOU

Author 2: Wajih EL HADJ YOUSSEF

Author 3: Rached TOURKI

Keywords: NETFI+ ; fault injection; SEUs; LEON3; simulation; emulation; reliability; TMR

Paper 38: Deep Learning based Computer Aided Diagnosis System for Breast Mammograms

Abstract: In this paper, a framework has been presented by using a combination of deep Convolutional Neural Network (CNN) with Support Vector Machine (SVM). Proposed method first perform preprocessing to resize the image so that it can be suitable for CNN and perform enhancement quality of the images can be enhanced. Deep Convolutional Neural Network (CNN) has been used for features extraction and classification with Support Vector Machine (SVM). Standard dataset MIAS and DDMS has been employed for testing the proposed framework by generating new images from these datasets by the process of augmentation. Different performance measures like Accuracy, Sensitivity, Specificity and area under the curve (AUC) has been employed as a quantitative measure and compared with state of the art existing methods. Results shows that proposed framework has attained accuracy 93.35% and 93% sensitivity.

Author 1: M. Arfan Jaffar

Keywords: Classification; breast mammograms; computer aided diagnosis; deep learning

Paper 39: ODSA: A Novel Ordering Divisional Scheduling Algorithm for Modern Operating Systems

Abstract: CPU scheduling is defined as scheduling multiple processes that are required to be executed in a specific time period. A large number of scheduling algorithms have been proposed to achieve maximum CPU utilization/throughput and minimizing turn around, waiting and response time. Existing studies claim that Round Robin (RR) is providing best results in terms of above-mentioned factors. In RR, a process is assigned to CPU for a fixed time quantum then the process starts its execution, in case that assigned time quantum greater than CPU’s capacity then remaining section of that process waits for its next turn. Although RR schedules processes in an efficient manner, however, it has certain limitations such as if time quantum is too small or large, it causes frequent context switching and response time can increase. To address these identified problems, various improved versions of RR also exist. The purpose of this paper is twofold: 1) a comparison between different improved versions of RR; and 2) a new algorithm named Ordering Divisional Scheduling Algorithm (ODSA) is also proposed that combines various features of different algorithms and is actually an improvement to RR. Our results show that ODSA can schedule processes with less turn around and average waiting time as compared to existing solutions.

Author 1: Junaid Haseeb

Author 2: Muhammad Tayyab

Author 3: Khizar Hameed

Author 4: Samia Rehman

Author 5: Muhammad Junaid

Author 6: Agha Muhammad Musa Khan

Keywords: CPU scheduling; round robin scheduling algorithm; turnaround time; waiting time; context switching

Paper 40: Customized Descriptor for Various Obstacles Detection in Road Scene

Abstract: Recently, real-time object detection systems have become a major challenge in the smart vehicle. In this work, we aim to increase both pedestrian and driver safety through improving their recognition rate in the vehicle’s embedded vision systems. Based on the Histogram of Oriented Gradients (HOG) descriptor, an optimized object detection system is presented in order to achieve an efficient recognition system for several obstacles. The main idea is to customize the weight of each bin in the HOG-feature vector according to its contribution in the description process of the extracted relevant features. Performance studies using a linear SVM classifier prove the efficiency of our approach. Indeed, based on the INRIA datasets, we have improved the sensitivity rate of the pedestrian detection by 11% and the vehicle detection by 5%.

Author 1: Haythem Ameur

Author 2: Abdelhamid Helali

Author 3: J. Ramírez

Author 4: J. M. Gorriz

Author 5: Ridha Mghaieth

Author 6: Hassen Maaref

Keywords: ADAS; customized HOG; linear SVM; obstacle detection

Paper 41: A Mathematical Model for Comparing Memory Storage of Three Interval-Based Parametric Temporal Database Models

Abstract: Interval-Based Parametric Temporal Database Model (IBPTDM) captures the historical changes of database object in single tuple. Such data model violates 1NF and it is difficult to be implemented on top of conventional Database Management Systems (DBMS). The reason behind that, IBPTDM cannot directly use relational storage structure or query evaluation technique that depends on atomic attribute values as well as it is unfixed attribute size. 1NF model with its features can be used to solve such challenge. Modeling time-varying data in 1NF model raise a question about memory storage efficiency and ease of use. A novel approach for representing temporal data in 1NF model and compare it with other main approaches in literature is the main goal of this research. To this end, a mathematical model for comparing a three different storage models is demonstrated to illustrate that the proposed model is more efficient than other approaches under certain conditions. The simulation results showed that the proposed model overcomes the needless redundancy of data, achieves saving in memory storage, and it is easy to be implemented in relational data model or to be adapted with a production systems that need to track temporal aspects of functioning database Systems.

Author 1: Nashwan Alromema

Author 2: Mohd Shafry Mohd Rahim

Author 3: Ibrahim Albidewi

Keywords: Valid-time data model; N1NF; tnterval-based timestamping; temporal data model; 1NF

Paper 42: Semantic based Data Integration in Scientific Workflows

Abstract: Data Integration has become the most prominent aspect of data management applications, especially in scientific domains like ecology, biology, and geosciences. Today’s complex scientific applications and the rise of diverse data generating devices in scientific domains (e.g. sensors) have made data integration a challenging task. In response to these types of challenges, data management applications are providing ground-breaking functionalities which come at the price of high complexity. This paper presents a semantic data integration framework which is based on the exploitation of ontologies. Exploiting a Description Logics formalism and associated reasoning procedures, the framework is able the handle heterogeneous formats and different semantics. Besides an in-depth discussion of the ontology-based integration capability, the paper also discusses a brief overview of the system architecture and its application in a real world scenario taken from ecological research.

Author 1: M. Abdul Rehman

Author 2: Jamil Ahmed

Author 3: Ahmed Waqas

Author 4: Ajmal Sawand

Keywords: Data integration; scientific workflows; ontology; data semantics; data management

Paper 43: Comparative Analysis of Online Rating Systems

Abstract: Online rating systems serve as decision support tool for choosing the right transactions on the internet. Consumers usually rely on others’ experiences when do transaction on the internet, therefore their feedbacks are helpful in succeeding such transactions. One important form of such feedbacks is the product ratings. Most online rating systems have been proposed either by researchers or industry. But there is much debate about their accuracies and stability. This paper looks at the accuracy and stability of set of common online rating systems over dense and sparse datasets. To accomplish that we used three evaluation measures namely, Mean Absolute Errors (MAE), Mean Balanced Relative Error (MBRE) and Mean Inverse Balanced Relative Error (MIBRE), in addition to Borda count to assess the stability of ranking among various rating systems. The results showed that both median and Dirichlet are the most accurate models for both sparse and dense datasets, whereas the BetaDR model is the most stable model across different evaluation measures. Therefore we recommend using Dirichlet or BetaDR for the products with few number of ratings and using the median model with products of large number of ratings.

Author 1: Mohammad Azzeh

Keywords: Online rating systems; reputation models; comparative analysis; decision making; e-commerce

Paper 44: Method for System Requirements Approval

Abstract: The requirements approval method is necessary to ensure that the system requirements have been identified in right way and the understanding between the contractor and the client exist. During research conducted is identified that most of the scholars have been working for the requirements definition during the meeting with the client, even they started to initiate the validation by checking whether the requirements captures the needs of client but not the approval of the requirements. Therefore, it is proposed the Joint Approval Requirements (JAR) method based on identified gaps through literature review and work experience. In this paper, this theoretical JAR method has been developed further on, through the presentation of its details about approval of the final version of the functional and non-functional requirements document and the integrated conceptual model of the IS. The presented method is ready for the research community in order to implement in different industries to measure the effect of the JAR method in the system requirements.

Author 1: Lindita Nebiu Hyseni

Author 2: Zamir Dika

Keywords: Approval method; approve requirements; system requirements; functional and non-functional requirements; joint approval requirements

Paper 45: A Novel Modeling based Agent Cellular Automata for Advanced Residential Mobility Applications

Abstract: Nowadays, residential mobility (RM) is usually interconnected with other urban phenomena to give more realistic and effective to the simulation models in order to support urban planners and decision makers. Recent RM research works to describe models from a functional view; however researchers do less focus in providing software modeling of their RM applications. Based on this note, the article presents an agent cellular automata based modeling for advanced RM applications. The proposed modeling contains six models based on UML 2.0 diagrams which models parts of the system from different views. The work could be of interest for specialists (researchers, designers and developers) when modeling advanced RM applications.

Author 1: Elarbi Elalaouy

Author 2: Khadija Rhoulami

Author 3: Moulay Driss Rahmani

Keywords: Residential mobility, multi agent systems, cellular automata; urban modeling

Paper 46: Intelligent Diagnostic System for Nuclei Structure Classification of Thyroid Cancerous and Non-Cancerous Tissues

Abstract: Recently, image mining has opened new bottlenecks in the field of biomedical discoveries and machine leaning techniques have brought significant revolution in medical diagnosis. Especially, classification problem of human cancerous tissues would assume to be one of the really challenging problems since it requires very high optimized algorithms to select the appropriate features from histopathological images of well-differentiated thyroid cancers. For instance prediction of initial changes in neoplasm such as hidden patterns of nuclei overlapping sequences, variations in nuclei structures, distortion in chromatin distributions and identification of other micro- architectural behaviors would provide more meticulous assistance to doctors in early diagnosis of cancer. In-order to mitigate all above stated problems this paper proposes a novel methodology so called “Intelligent Diagnostic System for Nuclei Structural Classification of Thyroid Cancerous and Non-Cancerous Tissues” which classifies nuclei structures and cancerous behaviors from medical images by using proposed algorithm Auto_Tissue_Analysis. Overall methodology of approach is comprised of four layers. In first layer noise reduction techniques are used. In second layer feature selection techniques are used. In third layer decision model is constructed by using random forest (tree based) algorithm. Finally result visualization and performance evaluation is done by using confusion matrix, precision and recall measures. The overall classification accuracy is measured about 74% with 10-k fold cross validation.

Author 1: Jamil Ahmed Chandio

Author 2: M. Abdul Rehman Soomrani

Keywords: Machine learning; decision support system; clustering; classification; cancer cells

Paper 47: Mobility for an Optimal Data Collection in Wireless Sensor Networks

Abstract: Sensor nodes located in the vicinity of a static sink drain rapidly their batteries since they have to carry more traffic burden. This situation results in network partition, holes as well as data losses. To mitigate this issue, many research proposed the use of mobile sink in data collection as a potential solution. However, due to its speed, the mobile sink has very short communication time to pick up all data from the sensor nodes within the network, therefore the sink is forced to return back to gather the remaining data. In this paper, we propose a new data collection scheme that aims to decrease the latency and enlarge the staying time between the mobile sink and the meeting points that buffer data originated from the other sensor nodes. We have also handled the case of urgent data so that they can be delivered without any delay. Our proposed scheme is validated via extensive simulations using NS2 simulator. Our approach significantly decreases the latency and prolongs the contact time between the mobile sink and sensor nodes.

Author 1: EZ-ZAIDI Asmaa

Author 2: RAKRAK Said

Keywords: Contact time; mobile sink; wireless sensor networks; meeting point; data gathering

Paper 48: Estimating True Demand in Airline’s Revenue Management Systems using Observed Sales

Abstract: Forecasting accuracy is very important in revenue management. Improved forecast accuracy, improves the decision made about inventory and this lead to a greater revenue. In the airline’s revenue management systems, the inventory is controlled by changing the product availability. As a consequence of changing availability, the recorded sales become a censored observation of underlying demand, so could not depict the true demand, and the accuracy of forecasting is affected by this censored data. This paper proposed a method to estimate true demand from censored data. In the literature, this process is referred to as unconstraining or uncensoring. Multinomial Logit model is used to model the customer choice behaviour. A simple algorithm is proposed to estimate the parameters (customers’ preference) of the model by using historical sales data, product availability info and the market share. The proposed method is evaluated using different simulated datasets and the results are compared with three benchmark models that are used commonly in airline revenue management practice. The experiments show that proposed method outperforms the others in terms of execution time and accuracy. A 47.64% improvement is reported in root mean square error between simulated and estimated demand in contrast to the benchmark models.

Author 1: Alireza Nikseresht

Author 2: Koorush Ziarati

Keywords: Demand estimation; demand modelling; forecasting; revenue management; inventory control; unconstraining; uncensoring

Paper 49: 2.5 D Facial Analysis via Bio-Inspired Active Appearance Model and Support Vector Machine for Forensic Application

Abstract: In this paper, a fully automatic 2.5D facial technique for forensic applications is presented. Feature extraction and classification are fundamental processes in any face identification technique. Two methods for feature extraction and classification are proposed in this paper subsequently. Active Appearance Model (AAM) is one of the familiar feature extraction methods but it has weaknesses in its fitting process. Artificial bee colony (ABC) is a fitting solution due to its fast search ability. However, it has drawback in its neighborhood search. On the other hand, PSO-SVM is one of the most recent classification approaches. However, its performance is weakened by the usage of random values for calculating velocity. To solve the problems, this research is conducted in three phases as follows: the first phase is to propose Maximum Resource Neighborhood Search (MRNS) which is an enhanced ABC algorithm to improve the fitting process in current AAM. Then, Adaptively Accelerated PSO-SVM (AAPSO-SVM) classification technique is proposed, by which the selection of the acceleration coefficient values is done using particle fitness values in finding the optimal parameters of SVM. The proposed methods AAM-MRNS, AAPSO-SVM and the whole 2.5D facial technique are evaluated by comparing them with the other methods using new 2.5D face image data set. Further, a sample of Malaysian criminal real case of CCTV facial investigation suspect has been tested in the proposed technique. Results from the experiment shows that the proposed techniques outperformed the conventional techniques. Furthermore, the 2.5D facial technique is able to recognize a sample of Malaysian criminal case called “Tepuk Bahu” using CCTV facial investigation.

Author 1: Siti Norul Huda Sheikh Abdullah

Author 2: Mohammed Hasan Abdulameer

Author 3: Nazri Ahmad Zamani

Author 4: Fasly Rahim

Author 5: Khairul Akram Zainol Ariffin

Author 6: Zulaiha Othman

Author 7: Mohd Zakree Ahmad Nazri

Keywords: Face recognition; active appearance model; ant bee colony; particle swarm optimization; support vector machine

Paper 50: Efficient Feature Selection for Product Labeling over Unstructured Data

Abstract: The paper introduces a novel feature selection algorithm for labeling identical products collected from online web resources. Product labeling is important for clustering similar or same products. Products blindly crawled over the web sources, such as online sellers, have unstructured data due to having features expressed in different representations and formats. Such data result in feature vectors whose representation is unknown and non-uniform in length. Thus, product labeling, as a challenging problem, needs efficient selection of features that best describe the products. In this paper, an efficient feature selection algorithm is proposed for product labeling problem. Hierarchical clustering is used with the state of the art similarity metrics to assess the performance of the proposed algorithm. The results show that the proposed algorithm increases the performance of product labeling significantly. Furthermore, the method can be applied to any clustering algorithm that works on unstructured data.

Author 1: Zeki YETGIN

Author 2: Abdullah ELEWI

Author 3: Furkan GÖZÜKARA

Keywords: Product labeling; product clustering; feature selection; similarity metrics; hierarchical clustering

Paper 51: Intelligent System for Detection of Micro-Calcification in Breast Cancer

Abstract: Recently; medical image mining has become one of the well-recognized research area(s) of machine learning and artificial intelligence techniques have been vastly used in various computer added diagnostic systems. Specifically; breast cancer classification problem is considered as one of the most significant problems. For instance, complex, diverse and heterogamous malignant features of micro-calcification in DICOM (Digital Communication in Medicine) images of mammography are very difficult to classify because the persistence of noise in mammogram images creates lots of confusions for doctors. In order to reduce the chances of misdiagnosis and to discernment the difference between malignant and benign lesions of micro-calcification this paper proposes a system so called “Intelligent System For Detection of Micro-Calcification in Breast Cancer” by considering all above stated problems. Overall our system comprises over three main stages. In first stage, adaptive threshold algorithm is used to reduce the noise, and canny edge detection algorithm is used to detect the edges of every macro or micro classification. In second stage, deginated as feature selection is done by using auto-crop algorithm, which crops all types of calcifications and lesions by proposed algorithm so called CFEDNN (Calcification Feature Extraction Deep Neural Networks) which is designed to avoid the manual ROIs (Region of Interest). Decision model is constructed by using DNN (Deep Neural Networks) and the best classification accuracy is measured as 95.6%.

Author 1: M. Abdul Rehman

Author 2: Jamil Ahmed

Author 3: Ahmed Waqas

Author 4: Ajmal Sawand

Keywords: Medical image mining; machine learning; feature extraction; classification; Digital Communication in Medicine (DICOM)

Paper 52: Financial Market Prediction using Google Trends

Abstract: Financial decisions are among the most significant life-changing decisions that individuals make. There is a strong correlation between financial decision making and human behavior. In this research the relationship between what people think and how stock market moves is investigated. The data from 2010 to 2015 of some of business, political and financial events which directly impact the local stock market in Pakistan is analyzed. The data was collected from search engine Google via Google trends. The association between internet searches regarding the political or business events and how the subsequent stock market moves is established. It was found that increase in search of these topics may lead to stock market fall or rise. The overall objective of this research is to predict Karachi Stock Exchange (now known as Pakistan stock exchange) 100 index by quantifying the semantics of international market. In addition to that, the relation between what an individual thinks while searching on Google which affects the local market is also investigated. The collected data has been mined by Multiclass Neural Network and Multiclass Decision Trees. The result shows that Multiclass Decision Trees performed best with an accuracy of 94%.

Author 1: Farrukh Ahmed

Author 2: Dr. Raheela Asif

Author 3: Dr. Saman Hina

Author 4: Muhammad Muzammil

Keywords: Google trends; financial market; stock market; Karachi stock market; multiclass neural network; multiclass decision trees

Paper 53: Ultra-Wideband Antenna Design for GPR Applications: A Review

Abstract: This paper presents a comparative review study on ultra-wideband (UWB) antenna technology for Ground Penetrating Radar (GPR) applications. The proposed antenna designs for UWB ground penetrating radar include a bow-tie antennas, Vivaldi antennas, horn antennas, planar antennas, tapered slot antennas, dipole antennas, and spiral antennas. Furthermore a comprehensive study in terms of operating frequency range, gain and impedance bandwidth on each antenna is performed in order to select a suitable antenna structure to analyze it for GPR systems. Based on the design comparison, the antenna with a significant gain and enhanced bandwidth has been selected for future perspective to examine the penetration depth and resolution imaging, simultaneously suitable for GPR detection applications. Three different types of antennas are chosen to be more suitable from the final comparison which includes Vivaldi, horn and tapered slot antennas. On further analysis a tapered slot antenna is a promising candidate as it has the ability to address the problems such as penetration depth and resolution imaging in GPR system due to its directional property, high gain and greater bandwidth operation, both in the lower and higher frequency range.

Author 1: Jawad Ali

Author 2: Noorsaliza Abdullah

Author 3: Muhammad Yusof Ismail

Author 4: Ezri Mohd

Author 5: Shaharil Mohd Shah

Keywords: Ultra-wideband antennas; ground penetrating radar; antennas; antenna review

Paper 54: Introducing Time based Competitive Advantage in IT Sector with Simulation

Abstract: Incompletion of projects in time leads to project failure which is the major dilemma of the software industry. Different strategies are used to gain a competitive advantage over competitors in business. In software perspective, time is an incredibly critical factor, software products should be delivered in time to gain competitive advantage. However, at a halt, there is no such strategy that covers time perspective. In this paper, a time-based strategy for software products is introduced. More specifically, the importance of time-based strategy by analyzing its associated factors is highlighted using simulations.

Author 1: Rida Maryam

Author 2: Adnan Naseem

Author 3: Junaid Haseeb

Author 4: Khizar Hameed

Author 5: Muhammad Tayyab

Author 6: Babar Shahzaad

Keywords: Business strategy; competitive advantage; time-base; a competitor; simulation; software industry

Paper 55: New Deep Kernel Learning based Models for Image Classification

Abstract: Deep learning system is used for solving many problems in different domains but it gives an over-fitting risk when richer representations are increased. In this paper, three different models with different deep multiple kernel learning architectures are proposed and evaluated for the breast cancer classification problem. Discrete Wavelet transform and edge histogram descriptor are used to extract the image features. For image classification purpose, support vector machine with the proposed deep multiple kernel models are used. Also, the span bound is employed for optimizing these models over the dual objective function. Furthermore, the comparison between the performance of the traditional support vector machine which uses only single kernel and the introduced models is worked out that show the efficiency of the experimental results of the proposed models.

Author 1: Rabha O. Abd-elsalam

Author 2: Yasser F.Hassan

Author 3: Mohamed W.Saleh

Keywords: Deep learning; multiple kernel; support vector machine; image classification

Paper 56: Low-Power Hardware Design of Binary Arithmetic Encoder in H.264

Abstract: Context-Based Adaptive Binary Arithmetic Coding (CABAC) is a well-known bottleneck in H.264/AVC, owing to the highly serialized calculation and high data dependency of the binary arithmetic encoder. This work presents a hardware architecture for the sub-module binary arithmetic encoder of the CABAC. Moreover, a clock gating technique is inserted into our design for power saving. An FPGA design of the proposed architecture can work at a frequency up to 268 MHz on Virtex 5. The suggested design can achieve 17% of power consumption saving, which allows it to be applied for low power video coding applications.

Author 1: Ben Hamida Asma

Author 2: Nedra Jarray

Author 3: Zitouni Abdelkrim

Keywords: H.264; Binary Arithmetic Encoder (BAE); Context-based Adaptive Binary Arithmetic Coding (CABAC); clock gating

Paper 57: Dynamic Access Control Policy based on Blockchain and Machine Learning for the Internet of Things

Abstract: The Internet of Things (IoT) is now destroying the barriers between the real and digital worlds. However, one of the huge problems that can slow down the development of this global wave, or even stop it, concerns security and privacy requirements. The criticality of these latter comes especially from the fact that the smart objects may contain very intimate information or even may be responsible for protecting people’s lives. In this paper, the focus is on access control in the IoT context by proposing a dynamic and fully distributed security policy. Our proposal will be based, on one hand, on the concept of the blockchain to ensure the distributed aspect strongly recommended in the IoT; and on the other hand on machine learning algorithms, particularly on reinforcement learning category, in order to provide a dynamic, optimized and self-adjusted security policy.

Author 1: Aissam OUTCHAKOUCHT

Author 2: Hamza ES-SAMAALI

Author 3: Jean Philippe LEROY

Keywords: Internet of Things; security; access control; dynamic policy; security policy; blockchain; machine learning; reinforcement learning

Paper 58: Eye Controlled Mobile Robot with Shared Control for Physically Impaired People

Abstract: Physically impaired and disabled people are an integral part of human society. Devices providing assistance to such individuals can help them contribute to the society in a more productive way. The situation is even worse for patients with locked-in syndrome who cannot move their body at all. These problems were the motivation to develop an eye controlled robot to facilitate such patients. Readily available commercial headset is used to record electroencephalogram (EEG) signals for classification and processing. Classification based control signals were then transmitted to robot for navigation. The robot mimics a brain controlled wheelchair with eye movements. The robot is based on shared control which is safe and robust. The analysis of robot navigation for patients showed promising results.

Author 1: Muhammad Wasim

Author 2: Javeria Khan

Author 3: Dawer Saeed

Author 4: Dr. Usman Ghani Khan

Keywords: Locked-in syndrome; EEG; shared contol; eye controlled robot

Paper 59: Fast–ICA for Mechanical Fault Detection and Identification in Electromechanical Systems for Wind Turbine Applications

Abstract: Recently, the approaches based on source separation are increasingly adopted for the fault diagnosis in several industrial applications. In particular, Independent Component Analysis (ICA) method is attractive, thanks to its simplicity of implementation. In the context of electrical rotating machinery with a variable speed, namely the wind turbine type, the interaction between the electrical and mechanical parts along with the fault is complex. Therefore, the essential system variables are affected and it thereby requires to be analyzed in order to detect the presence of certain faults. In this paper, the target system is the classical association of a doubly-fed induction motor to a two stage gearbox for wind energy application system. The investigated mechanical fault is a uniform wear of two gear wheels for the same stage. The idea behind the proposed technique is to consider the fault detection and identification as a source separation problem. Based on the analysis into independent components, Fast–ICA algorithm is adopted to separate and identify the sources of the gear faults. Afterwards, a spectral analysis is applied on the signals resulting from the separation in order to identify the fault components related to the damaged wheels. The efficiency of the proposed technique for the separation and identification of the fault components is evaluated by numerical simulations.

Author 1: Mohamed Farhat

Author 2: Yasser Gritli

Author 3: Mohamed Benrejeb

Keywords: Source separation; fault diagnosis; independent component analysis; fast–ICA; spectral analysis

Paper 60: An Enhanced Approach for Detection and Classification of Computed Tomography Lung Cancer

Abstract: The paper presents approaches for nodule detection and extraction in axial lung computed tomography. The goal is to detect correctly pulmonary nodule to recognize and screen lung cancer patients. The pulmonary nodule detection is very challenging problem. The proposed model developed a hybrid efficient model based on affine-invariant representation and shape of segmented nodule. Due to large number of extracted features for all slices on patient, feature selection is an important step to select the most important feature for classification. We apply forward stepwise least squares regression that maximizes the Rsquared value, this criterion provides a fast preprocessing feature selection assessment for systems with huge volumes of features based on a linear models framework. Moreover, gradient boosting have been suggested to select the relevant features based on boosting approach. Classification of patients has been done by support vector machine. Kaggle DSB dataset is used to test the accuracy of our model. The results show major improvement in accuracy and the features are reduced.

Author 1: Wafaa Alakwaa

Author 2: Mohammad Nassef

Author 3: Amr Badr

Keywords: Lung cancer; computed tomography; affine invariant moments; pulmonary nodules; R2; feature selection; support vector machine

Paper 61: Short Survey on Static Hand Gesture Recognition

Abstract: This paper presents a survey of methods which have been recently proposed for recognizing static hand gestures. These approaches are first summarized and then are assessed based on a common dataset. Because mentioned methods employ different types of input, the survey focuses on stages of feature extraction and classification. Other former steps, such as pre-processing and hand segmentation, are slightly modified. In experiments, this work does not only consider the recognition accuracy but also suggests suitable scenarios for each method according to its advantages and limitations.

Author 1: Huu-Hung Huynh

Author 2: Duc-Hoang Vo

Keywords: Hand gesture; rank-order correlation matrix, Gabor filter; block; centroid distance; Fourier transform

Paper 62: Mobility based Net Ordering for Simultaneous Escape Routing

Abstract: With the advancement in electronics technology, number of pins under the ball grid array (BGA) are increasing on reduced size components. In small size components, a challenging task is to solve the escape routing problem where BGA pins escape towards the component boundary. It is often desirable to perform ordered simultaneous escape routing (SER) to facilitate area routing and produce elegant Printed Circuit Board (PCB) design. Some heuristic techniques help in finding the PCB routing solution for SER but for larger problems these are time consuming and produce sub-optimal results. This work propose solution which divides the problem into two parts. First, a novel net ordering algorithm for SER using network theoretic approach and then linear optimization model for single component ordered escape routing has been proposed. The model routes maximum possible nets between two components of the PCB by considering the design rules based on the given net ordering. Comparative analysis shows that the proposed net ordering algorithm and optimization model performs better than the existing routing algorithms for SER in terms of number of nets routed. Also the running time using proposed algorithm reduces to O(2NE=2) + O(2NE=2) for ordered escape routing of both components. This time is much lesser than O(2NE) due to exponential reduction.

Author 1: Kashif Sattar

Author 2: Aleksandar Ignjatovic

Author 3: Anjum Naveed

Author 4: Muhammad Zeeshan

Keywords: Net ordering; optimization model; ordered escape routing; PCB routing; simultaneous escape routing

Paper 63: A Survey on User Interfaces for Interaction with Human and Machines

Abstract: Interaction with the machines and computers is achieved using user interfaces. Nowadays, with the tremendous growth of technology, the interaction is made more simple and flexible. The study of user interfaces for human-computers and machines interaction is the main focus of this paper. In particular, an extensive overview of different user interfaces available so far is provided. The review covers text-based, graphical-based, and new class of emerging user interfaces to interact with the machines and computers. This work will be helpful for the development of new user interfaces.

Author 1: Mirza Abdur Razzaq

Author 2: Muhammad Ali Qureshi

Author 3: Kashif Hussain Memon

Author 4: Saleem Ullah

Keywords: Command line interface (CLI); graphical user interface (GUI); user interface (UI); sixth sense device; natural language interface; brain-computer interface; emerging user interfaces

Paper 64: OSPF vs EIGRP: A Comparative Analysis of CPU Utilization using OPNET

Abstract: Routing is difficult in enterprise networks because a packet might have to traverse many intermediary nodes to reach the final destination. The selection of an appropriate routing protocol for a large network is difficult task. The focus of this work is to select and identify the best routing technique for a computer network. In this study, the performance of OSPF and EIGRP routing protocols with respect to CPU utilization is analyzed using OPNET simulator. The results depict EIGRP acquires redundant information which effect CPU utilization.

Author 1: Muhammad Kashif Hanif

Author 2: Ramzan Talib

Author 3: Nafees Ayub

Author 4: Muhammad Umer Sarwar

Author 5: Sami Ullah

Keywords: Network protocols; topology; OPNET; interior gateway protocols (IGPs); OSPF

Paper 65: Anonymized Social Networks Community Preservation

Abstract: Social Networks have been widely used in the society. Most of the people are connected to one another, communicated with each other and share the information in different forms. The information gathered from different social networking sites is growing tremendously in large volumes of various research, marketing and other purposes which is creating security and privacy concerns. The gathered information contains some sensitive and private information about an individual, such as the relationship of an individual or group information. So, to protect the data from unauthorized users the data should be anonymized before publishing. In this paper, we study how the k-degree and k-NMF anonymized methods preserve the existing communities of the original social networks. We use an existing heuristic algorithm called Louvian method to identify the communities in social networks. We conduct the experiments on real data sets and compare the performances of the two anonymized social networks for preservation of communities of the original social networks.

Author 1: Jyothi Vadisala

Author 2: Valli Kumari Vatsavayi

Keywords: Community; anonymity; degree; social network

Paper 66: A Comparative Study for Performance and Power Consumption of FPGA Digital Interpolation Filters

Abstract: The development of FPGA-based digital signal processing devices has been gaining attention. Researchers seek to reduce power consumption and enhance signal processing quality in these devices with given resources and spatial limits. Hence, there is a need to investigate both the capability and the power consumption associated with the various digital filtering schemes commonly used in FPGA-based devices. We carry out a set of performance and power consumption measurements of interpolation filters using an FPGA and other basic signal processing building blocks. We compare the signal processing performance with theoretical prediction, and measure the power consumed by the filters. Our experimental measurements also confirm the accuracy of the numerical tools used for predicting FPGA power consumption. This paper is aimed at providing a framework to accurately test basic signal processing across various interpolation schemes and compare the respective schemes’ software-side contributions to power consumption and filtering quality.

Author 1: Tim Donnelly

Author 2: Jungu Choi

Author 3: Alexander V. Kildishev

Author 4: Matthew Swabey

Author 5: Mark C. Johnson

Keywords: Digital signal processing; digital interpolation filters; FPGA

Paper 67: A Survey of Datasets for Biomedical Question Answering Systems

Abstract: The massively ever increasing amount of textual and linked biomedical data available online poses many challenges for information seekers. So, the focus of information retrieval community has shifted to precise information retrieval, i.e. providing exact answer to a user question. In recent years, many datasets related to Biomedical Question Answering (BioQA) have emerged which the researchers can use to evaluate the performance of their systems. We reviewed these biomedical datasets and analyzed their characteristics. The survey in this paper covers these datasets for BioQA and has a two fold purpose: to provide an overview of the available datasets in this domain and to help researchers select the most suitable dataset for benchmarking their system.

Author 1: Muhammad Wasim

Author 2: Dr. Waqar Mahmood

Author 3: Dr. Usman Ghani Khan

Keywords: Biomedical; QA system; review; survey

Paper 68: A Comprehensive Analysis on the Security Threats and their Countermeasures of IoT

Abstract: Internet of Things referred as a pervasive network architecture which provides services to the physical world by processing and analyzing data. In this modern era Internet of Things has been shown much significance and rapidly developing by connecting heterogeneous devices with various technologies. By this way interconnectivity of large number of electronic devices connected with the IoT network leads the risk of security and confidentiality of data. This paper analyzes different security issues, their counter measures and discusses the future directions of security in IoT. Furthermore, this paper also discusses essential technologies of security like encryption in the scenario of IoT for the prevention of harmful threats in the light of latest research.

Author 1: Abdul Wahab Ahmed

Author 2: Omair Ahmad Khan

Author 3: Mian Muhammad Ahmed

Author 4: Munam Ali Shah

Keywords: Internet of things; security threats; countermeasures; privacy

Paper 69: Ladder Networks: Learning under Massive Label Deficit

Abstract: Advancement in deep unsupervised learning are finally bringing machine learning close to natural learning, which happens with as few as one labeled instance. Ladder Networks are the newest deep learning architecture that proposes semi-supervised learning at scale. This work discusses how the ladder network model successfully combines supervised and unsupervised learning taking it beyond the pre-training realm. The model learns from the structure, rather than the labels alone transforming it from a label learner to a structural observer. We extend the previously-reported results by lowering the number of labels, and report an error of 1.27 on 40 labels only, on the MNIST dataset that in a fully supervised setting, uses 60000 labeled training instances.

Author 1: Behroz Mirza

Author 2: Tahir Syed

Author 3: Jamshed Memon

Author 4: Yameen Malik

Keywords: Ladder networks; semi-supervised learning; deep learning; structure observer

Paper 70: Privacy-preserving Twitter-based Solution for Visually Impaired People

Abstract: Visually impaired people is a big community all over the world. They usually seek help to perform their daily activities such as reading the expired date of food cans or medicine, reading out PIN of a certain ATM Visa, identifying the color of clothes or differentiate between the money notes and other objects with the same shape. A number of IT-based solutions have been proposed to help and assist blind and/or visually impaired people. Generally speaking, these solutions, however, do not support Arabic languages nor protect blind users’ privacy. In this paper, Trusted Blind Society (TBS) mobile application is proposed. It is an android application which allows blind users to recognize their unknown surroundings by utilizing two concepts: social networks sites and friendsourcing. These two concepts were employed by allowing family members and the trusted friends, who are registered on Twitter, to answer blind users’ questions on a real time. The solution is also bilingual, supports (Arabic/English) and allows screen reader using Android talk-back service. The performance of the TBS system was evaluated using loader.io to check its stability under the heavy load and it was tested by a number of blind volunteers and the results showed good performance comparing to most related work.

Author 1: Dina Ahmed Abdraboo

Author 2: Tarek Gaber

Author 3: Mohamed El Sayed Wahed

Keywords: Human powered technology; blind people; visually impaired people; user’s privacy; IT-based solution; social networks; friend-sourcing; crowd-sourcing; accessibility; low vision; bilingual; screen reader

Paper 71: A Text based Authentication Scheme for Improving Security of Textual Passwords

Abstract: User authentication through textual passwords is very common in computer systems due to its ease of use. However textual passwords are vulnerable to different kinds of security attacks, such as spyware and dictionary attacks. In order to overcome the deficiencies of textual password scheme, many graphical password schemes have been proposed. The proposed schemes could not fully replace textual passwords, due to usability and security issues. In this paper a text based user authentication scheme is proposed which improves the security of textual password scheme by modifying the password input method and adding a password transformation layer. In the proposed scheme alphanumeric password characters are represented by random decimal numbers which resist online security attacks such as shoulder surfing and key logger attacks. In the registration process password string is converted into a completely new string of symbols or characters before encryption. This strategy improves password security against offline attacks such as brute-force and dictionary attacks. In the proposed scheme passwords consist of alphanumeric characters therefore users are not required to remember any new kind of passwords such as used in graphical authentication. Hence password memorability burden has been minimized. However mean authentication time of the proposed scheme is higher than the textual password scheme due to the security measures taken for the online attacks.

Author 1: Shah Zaman Nizamani

Author 2: Syed Raheel Hassan

Author 3: Tariq Jamil Khanzada

Author 4: Mohd Zalisham Jali

Keywords: Password security; security; usability; alphanumeric passwords; authentication

Paper 72: Impact of Pulse Voltage as Desulfator to Improve Automotive Lead Acid Battery Capacity

Abstract: This paper studies the impact of Pulse Voltage as Desulfator to recover weak automotive Lead Acid Battery capacity which is caused by Sulfation. This technique is used to overcome the premature loss of battery capacity and speed up the process of charging and extend the lead acid battery life cycle 3 to 4 times compared with traditional charging methods using constant current. Sulfation represents the accumulation of lead sulfate on the electrodes (lead plates). This phenomenon appears naturally at each discharge of the battery, and disappears during a recharge. This is common with starter batteries in cars driven in the city with a load-hungry accessory. A motor in idle or at low speed cannot charge the battery sufficiently. Voltage pulse decompose the sulfate (PbSO4) attached to the electrode which is the main cause of the loss of capacity. In this paper, we study the effects of the recovery capacity of a Lead Acid Battery. Voltage pulses will be applied on a commercial automotive battery to collect data, using a charger/Desulfator prototype based on a PCDUINO. The experiment results show that there is improvement of Cold Cranking Amps level, and charge time duration of the Lead Acid Battery after using our prototype.

Author 1: EL MEHDI LAADISSI

Author 2: ANAS EL FILALI

Author 3: MALIKA ZAZI

Keywords: Lead acid battery; desulfator; pulse charging; cold cranking; sulfation

Paper 73: The Dynamics of IT Workaround Practices - A Theoretical Concept and an Empirical Assessment

Abstract: An interesting phenomenon that has received limited attention in the extant literature is that of IT workaround practices. Based on Ashby's Law of Requisite Variety, workarounds were found to be used to accomplish the basic task of matching unmatched variety in the system. The Interaction Effectiveness (IE) ratio of 1.4 was used as a baseline to uncover potential sources of workarounds. The Echo method was used to collect data from 42 users in a high-technology company (HTC). Enablers of and barriers to workaround practices were divided into four main categories - flexibility, reliability, ease of use, and coordination - whereas workarounds were divided into three categories - using other tools, seeking help, and accepting. The results of the case study indicate that "reliability" is the dominant category for both helpful and non-helpful incidents, whereas "coordination" was the least significant. Of the workaround mechanisms, "using other tools" was the most significant category for all users. The findings suggest cycles of continuous improvement to the IE ratio to alleviate the need for workarounds, but a more fundamental issue concerning the source of workaround behaviors is a function of misfits between input variety by users and variety handling capabilities of the system.

Author 1: Ahmed Alojairi

Keywords: IT effectiveness; workarounds; cybernetics