IJACSA Volume 7 Issue 9 - thesai.org

IJACSA Volume 7 Issue 9

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

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Paper 1: An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

Abstract: Nowadays, as the use of cloud computing service becomes more extensive and the customers welcome this service, an increasing trend in energy consumption and operational costs of these centers may be seen. To reduce operational costs, the providers should decrease energy consumption to an extent that Service Level Agreement (SLA) maintains at a desirable level. This paper adopts the virtual machine consolidation problem in cloud computing data centers as a solution to achieve this goal, putting forward solutions to make the decision regarding the necessity of migration from hosts and finding appropriate hosts as destinations of migration. Using time-series forecasting method and Double Exponential Smoothing (DES) technique, the proposed algorithm predicts CPU utilization in near future. It also proposes an optimal equation for the dynamic lower threshold. Comparing current and predicted CPU utilization with dynamic upper and lower thresholds, this algorithm identifies and categorizes underloaded and overloaded hosts. According to this categorization, migration then occurs from the hosts that meet the necessary conditions for migration. This paper identifies a certain type of hosts as “troublemaker hosts”. Most probably, the process of prediction and decision making regarding the necessity of migration will be disrupted in the case of these hosts. Upon encountering this type of hosts, the algorithm adopts policies to modify them or switch them to sleep mode, thereby preventing the adverse effects caused by their existence. The researchers excluded all overloaded, prone-to-be-overloaded, underloaded, and prone-to-be-underloaded hosts from the list of suitable hosts to find suitable hosts as destinations of migration. An average improvement of 86.2%, 28.4%, and 87.2% respectively for the number of migrations of virtual machines, energy consumption, and SLA violation is among the simulation achievements of this algorithm using Clouds tool.

Author 1: Sara Nikzad

Author 2: Seyed EnayatOllah Alavi

Author 3: Mohammad Reza Soltanaghaei

Keywords: Cloud Computing; Service Level Agreement; Energy Consumption; Virtualization; Dynamic Consolidation; Data Center

Paper 2: Maneuverability of an Inverted Pendulum Vehicle According to the Handle Operation Methods

Abstract: This study investigated what handle operation and turning gain is comfortable for people using an inverted pendulum vehicle that is changeable the handle operation. Experimental conditions were three conditions. First is a slalom course with two cones placed at an interval of 1.8 m. Second is a slalom course with five cones placed at an interval of 1.4 m. Third is a slalom course with six cones placed at 1.8m, 1.4m, 1.8m, 1.4m, 1.8m, and 1.8m interval. The first condition considered the difference of handle operation between subjects who were used to ride and not used to ride. The second condition considered the difference of maneuverability due to gains. The third condition considered the difference of maneuverability between two handle operations in real running space in a condition of 10 gains. In a result of the first condition, a subject who was used to ride run effectively and running time is short compared with a subject who was used to ride. However, in handle yaw rotation, the difference of maneuverability was small. In a result of the second condition, running mileage about the same in two handle operation, but running time of handle yaw rotation is shorter than that of handle roll rotation. In a result of the third condition, like the second condition, running time of handle yaw rotation is shorter than that of handle roll rotation. In questionnaire evaluation, the best gain is the lower gain, 0.02. At last, An experiment was carried out by 14 subjects in the best gain, 0.02 that is best both handle operation. In the result of this experiment, 12 subjects answered that handle yaw rotation is better than handle roll rotation.

Author 1: Chihiro NAKAGAWA

Author 2: Takuya CHIKAYAMA

Author 3: Akikazu OKAMOTO

Author 4: Atsuhiko SHINTANI

Author 5: Tomohiro ITO

Keywords: personal mobility vehicle; inverted pendulum vehicle; maneuverability; handle operation; number of operations; questionnaire evaluation

Paper 3: Gaussian Mixture Model and Deep Neural Network based Vehicle Detection and Classification

Abstract: The exponential rise in the demand of vision based traffic surveillance systems have motivated academia-industries to develop optimal vehicle detection and classification scheme. In this paper, an adaptive learning rate based Gaussian mixture model (GMM) algorithm has been developed for background subtraction of multilane traffic data. Here, vehicle rear information and road dash-markings have been used for vehicle detection. Performing background subtraction, connected component analysis has been applied to retrieve vehicle region. A multilayered AlexNet deep neural network (DNN) has been applied to extract higher layer features. Furthermore, scale invariant feature transform (SIFT) based vehicle feature extraction has been performed. The extracted 4096-dimensional features have been processed for dimensional reduction using principle component analysis (PCA) and linear discriminant analysis (LDA). The features have been mapped for SVM-based classification. The classification results have exhibited that AlexNet-FC6 features with LDA give the accuracy of 97.80%, followed by AlexNet-FC6 with PCA (96.75%). AlexNet-FC7 feature with LDA and PCA algorithms has exhibited classification accuracy of 91.40% and 96.30%, respectively. On the contrary, SIFT features with LDA algorithm has exhibited 96.46% classification accuracy. The results revealed that enhanced GMM with AlexNet DNN at FC6 and FC7 can be significant for optimal vehicle detection and classification.

Author 1: S Sri Harsha

Author 2: K. R. Anne

Keywords: Vehicle detection and classification; deep neural network; AlexNet; SIFT; Gaussian Mixture Model; LDA

Paper 4: Designing and Implementing of Intelligent Emotional Speech Recognition with Wavelet and Neural Network

Abstract: Recognition of emotion from speech is a significant subject in man-machine fields. In this study, speech signal has analyzed in order to create a recognition system which is able to recognize human emotion and a new set of characteristic has proposed in time, frequency and time–frequency domain in order to increase the accuracy. After extracting features of Pitch, MFCC, Wavelet, ZCR and Energy, neural networks classify four emotions of EMO-DB and SAVEE databases. Combination of features for two emotions in EMO- DB database is 100%, for three emotions is 98.48% and for four emotions is 90% due to the variety of speech, existing more spoken words and distinguishing male and female which is better than the result of SAVEE database. In SAVEE database, accuracy is 97.83% for two emotions of happy and sad, 84.75% for three emotions of angry, normal and sad and 77.78% for four emotions of happy, angry, sad and normal

Author 1: Bibi Zahra Mansouri

Author 2: Hamid Mirvaziri

Author 3: Faramarz Sadeghi

Keywords: Recognition of emotion from speech; feature extraction; MFCC; Artificial neural network; Wavelet

Paper 5: An IoT Middleware Framework for Industrial Applications

Abstract: Starting from the RFID and the wireless sensor networks, the Internet of connected things has attracted the attention of major IT companies and later, of the industrial environment that recognized the concept as one of their key axes for future growth and development. The implementation of IoT in the industrial environment raises some significant issues related to the diversity of fieldbuses, the large number of devices and their configuration. The requirements related to reliability, security and real-time are very important. This paper proposes an industrial IoT and communications at the edge framework which has some outstanding features related to: the easy integration of fieldbuses and devices used in industrial environments with automatic configuration features, integration of multiple middleware technologies (CORBA, OPC and DDS), the uncoupling of the industrial activity from the publishing data on the Internet, security at different levels of the framework. Another important feature of the proposed framework is that it is based on mature standards and on open source or public implementations of these standards. The framework is modular, allowing the easy integration of new fieldbus protocols, middleware technologies and new objects in the client application. This paper is focused mainly on CORBA and DDS approaches.

Author 1: Nicoleta-Cristina Gaitan

Author 2: Vasile Gheorghita Gaitan

Author 3: Ioan Ungurean

Keywords: Internet of Things; Middleware; CORBA; ACE ORB (TAO); Data Distribution Service

Paper 6: A Survey of IPv6 Deployment

Abstract: The next-generation Internet protocol (IPv6) was designed to overcome the limitation in IPv4 by using a 128-bit address instead of a 32-bit address. In addition to solving the address the limitations, IPv6 has many improved features. This research focused to survey IPv6 deployment all around the world. The objectives of this survey paper are to highlight the issues related to the IPv6 deployment and to look into the IPv4 to IPv6 transition mechanisms. Furthermore, provide insight on the global effort around the world to contribute in IPv6 deployment. In addition, identify the potential solutions or suggestions that could improve the IPv6 deployment rate. In order to achieve the said objectives we survey number of papers on IPv6 deployment from different countries and continents.

Author 1: Manal M. Alhassoun

Author 2: Sara R. Alghunaim

Keywords: IPv4; IPv6; deployment; Internet

Paper 7: Intelligent Image Watermarking based on Handwritten Signature

Abstract: With the growth of digital technology over the past decades, the issue of copyright protection has become especially important. Digital watermarking is a suitable way of addressing this issue. The main problem in the area of watermarking, is the balance between image transparency and resistance to attacks after watermarking, where an increase in either one of them will always cause a decrease in the other. Providing statistical and intelligent methods, is the most common way of optimizing resistance and transparency. In this paper, the intelligent method of genetic algorithm (GA) in watermarking will be examined and also the results of using this method will be compared with the results of a statistical SVD-based method. Also, by combining the issues of watermarking and authentication, a relatively higher security in these two issues can be achieved. In this scheme, the security of watermarking increases through the provision of a new method which is based on the combination of image watermarking with a person's handwritten signature. It must be mentioned that the section of signature recognition is implemented using neural networks. The results from implementing these two methods show that in this area, intelligent methods have a better performance compared to statistical methods. This method can also be used for tasks like passport or national identity card authentication.

Author 1: Saeid Shahmoradi

Author 2: Nasrollah Sahragard

Author 3: Ahmad Hatam

Keywords: intelligent watermarking; genetic algorithm; neural networks; handwritten signature

Paper 8: Fuzzy Risk-based Decision Method for Vehicular Ad Hoc Networks

Abstract: A vehicular ad hoc network (VANET) is an emerging technology that has the potential to improve road safety and traveler comfort. In VANETs, mobile vehicles communicate with each other for the purpose of sharing various kinds of information. This information is very useful for preventing road accidents and traffic jams. On Contrary, bogus and inaccurate information may cause undesirable things, such as automobile fatalities and traffic congestion. Therefore, it is highly beneficial to consider risk before vehicle takes any decision based on the received information from the surrounding vehicles. To overcome these issues, we propose a new risk-based decision method for vehicular ad hoc networks. It determines a risk-based the three key elements: 1) application type and sensitivity level, 2) vehicle context and 3) driver’s attitude. This paper also provides theoretical analysis and evaluation of the proposed method, and it also discusses the applications of the proposed model.

Author 1: Riaz Ahmed Shaikh

Keywords: Ad hoc networks; Decision methods; Risk management; Trust management; Vehicular Networks

Paper 9: Good Quasi-Cyclic Codes from Circulant Matrices Concatenation using a Heuristic Method

Abstract: In this paper we present a method to search q circulant matrices; the concatenation of these circulant matrices with circulant identity matrix generates quasi-cyclic codes with high various code rate q/(q+1) (q an integer). This method searches circulant matrices in order to find the good quasi-cyclic code (QCC) having the largest minimum distance. A modified simulated annealing algorithm is used as an evaluator tool of the minimum distance of the obtained QCC codes. Based on this method we found 16 good quasi-cyclic codes with rates (1/2, 2/3 and 3/4), their estimated minimum distance reaches the lower bounds of codes considered to be the better linear block codes in Brouwer’s database.

Author 1: Bouchaib AYLAJ

Author 2: Said NOUH

Author 3: Mostafa BELKASMI

Author 4: Hamid ZOUAKI

Keywords: Circulant matrix; quasi-cyclic Codes; Minimum Distance; Simulated Annealing; Linear Error Correcting codes

Paper 10: Balanced Distribution of Load on Grid Resources using Cellular Automata

Abstract: Load balancing is a technique for equal and fair distribution of workloads on resources and maximizing their performance as well as reducing the overall execution time. However, meeting all of these goals in a single algorithm is not possible due to their inherent conflict, so some of the features must be given priority based on requirements and objectives of the system and the desired algorithm must be designed with their orientation. In this article, a decentralized load balancing algorithm based on Cellular Automata and Fuzzy Logic has been presented which has capabilities needed for fair distribution of resources in Grid level. Each computing node in this algorithm has been modeled as a Cellular Automata’s cell and has been provided with the help of Fuzzy Logic in which each node can be an expert system and have a decisive role which is the best choice in a dynamic environment and uncertain data. Each node is mapped of one of the VL, L, VN, H and VH state based on information exchange on certain time periods with its neighboring nodes and based on fuzzy logic tries to decrease the communication overhead and estimate the state of the other nodes in subsequent. The decision to send or receive the workload is made based on each node state. Thus, an appropriate structure for the system can greatly improve the efficiency of the algorithm. Fuzzy control does not search and optimize, just makes decisions based on inputs which are effective internal parameters of the system and are mostly based on incomplete and nonspecific information. Each node based on information exchange at specific time periods with its neighboring nodes, and according to Fuzzy Logic rules is mapped of one of the VL, L, N, H and VH states. To reduce communication overhead, with the help of Fuzzy Logic tries to estimate the state of the other nodes in subsequent periods, and based on the status of each node, makes a decision to send or receive workloads. Thus an appropriate structure for the system can improve the efficiency of the algorithm. In fact, Fuzzy Logic does not search and optimize, just makes decisions based on the input parameters which are often incomplete and imprecise.

Author 1: Amir Akbarian Sadeghi

Author 2: Ahmad Khademzadeh

Author 3: Mohammad Reza Salehnamadi

Keywords: Computing Grid; Load balancing; Cellular Automata; Fuzzy Logic

Paper 11: Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene

Abstract: In the present paper, we will propose a new and robust method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used to determine the projection matrices. The present method is based on the formulation of a non linear cost function from the determination of a relationship between two points of the scene with their opposite relative to the axis of abscise and their projections in the image planes. The resolution of this function with genetic algorithm enables us to estimate the intrinsic parameters of different cameras. The important of our approach reside in the use of a single pair of images which provides fewer equations, simplifies the mathematical complexities and minimizes the execution time of the application, the use of the data of the first image only without the use of the data of the second image, the use of any camera which makes the intrinsic parameters variable not constant and the use of a 3D scene reduces the planarity constraints. The experimental results on synthetic and real data prove the performance and robustness of our approach.

Author 1: B. SATOURI

Author 2: A. EL ABDERRAHMANI

Author 3: H. TAIRI

Author 4: K. SATORI

Keywords: Self-calibration; varying intrinsic parameters; non linear optimization; Interests points; Matching; Fundamental matrix

Paper 12: On the Internal Multi-Model Control of Uncertain Discrete-Time Systems

Abstract: In this paper, new approaches of internal multi-model control are proposed to be applied for the case of the discrete-time systems with parametric uncertainty. In this sense, two implantation structures of the internal multi-model control are adopted; the first is based on the principle of switching and the second on the residues techniques. The stability’s study of these control structures is based on the Kharitonov theorem, thus two extensions of this theorem have been applied to define the internal models. To illustrate these approaches, simulation results are presented at the end of this article.

Author 1: Chakra Othman

Author 2: Ikbel Ben Cheikh

Author 3: Dhaou Soudani

Keywords: Internal model control IMC; Internal multi-model control IMMC; Kharitonov theorem; Switching method; Residues techniques; discrete-time systems; uncertain systems

Paper 13: High Performance Computing Over Parallel Mobile Systems

Abstract: There are currently more mobile devices than people on the planet. This number is likely to multiply many folds with the Internet of Things revolution in the next few years. This may treasure an unprecedented computational power especially with the wide spread of multicore processors on mobile phones. This paper investigates and proposes a new methodology for mobile cluster computing, where multiple mobile devices including their multicore processors can be combined to perform possibly massively parallel applications. The paper presents in details the steps for building and testing the mobile cluster using the proposed methodology and proving the successful implementation.

Author 1: Doha Ehab Attia

Author 2: Abeer Mohamed ElKorany

Author 3: Ahmed Shawky Moussa

Keywords: Parallel computing; High-performance computing; Mobile computing; Cluster computing; Android OS

Paper 14: ROHDIP: Resource Oriented Heterogeneous Data Integration Platform

Abstract: During the last few years, the revolution of social networks such as Facebook, Twitter, and Instagram led to a daily increasing of data that are heterogeneous in their sources, data models, and platforms. Heterogeneous data sources have many forms such as the www, deep web, relational databases systems, No-SQL database systems, hierarchal data systems, semi-structured files, in which data are usually allocated on different machines (distributed) and have different data models (heterogeneous). Large-scale data integration efforts demonstrate that their most valuable contribution is implementing a data integration platform that provides a uniform access to the heterogeneous data sources, as well as the different versions of data reported by the same data source over time. Furthermore, the platform must be able to integrate data from a broad range of data authoring devices and database management systems. It also should be accessible by almost types of data querying devices to ensure globally querying the integration platform from any place on earth anytime and receiving the query result in any data format. In this paper, we create a resource oriented heterogeneous data integration platform (ROHDIP) that facilitates the data integration process and implements the objectives discussed above. We use the resource oriented architecture ROA to support the uniform access by most types of data querying devices from anywhere and to improve the query response time.

Author 1: Wael Shehab

Author 2: Sherin M. ElGokhy

Author 3: ElSayed Sallam

Keywords: Data Integration; Data heterogeneity; SOA; ROA; Restful; ROHDIP

Paper 15: Improving the Emergency Services for Accident Care in Saudi Arabia

Abstract: The road safety is one of the serious challenges faced by most of the governments due to the involvement of various issues. Being perfect in driving is not enough on the roads but tackling the mistakes of other persons is also an important aspect of the present day driving. Dealing with the accidents, injured personals, communicating the emergency services and dealing with other legal formalities is a serious challenge in present conditions. Providing emergency services is a real challenge due to increased population, heavy traffic and communication problems. In this paper, a novel technique is being introduced to avoid delays and major setbacks by emergency services at the time of accidents. The proposed technique works along with traffic control system of Kingdom of Saudi Arabia (KSA). By introducing such system in the healthcare, the serious drawbacks of communication can be avoided to a maximum extent. The proposed system can prove to be very effective at a place like Saudi Arabia, where millions of Hajj pilgrims visit for socio-religious gatherings.

Author 1: Amr Jadi

Keywords: Accidents; Communication; Emergency Services; Hajj Pilgrims; Healthcare; Saudi Arabia

Paper 16: Analysis of Purchasing Tendency using ID-POS Data of Social Login User

Abstract: This study targets social login registrants on an EC site and aims to clarify the difference between the purchasing tendency of social login registrants and general members by analyzing product purchasing history. The authors focused on the golf portal site that is the subject of this research. The authors analyzed the purchasing data comparing social login registrants with general members. It became clear that the social login registrants and general members have different distribution regarding the number of purchases and purchase type. Moreover, the social login registrants have a larger range of purchase types per purchase and they are purchasing from a variety of genres. In addition, the authors analyzed them with a focus on the relationship between products purchased. As the results of network analysis, it became clear that the existence of specific product combinations (concentrated sets on the network) more readily purchased simultaneously by Facebook users than by general members. Moreover, the authors compared each network tendency using a network index (degree, closeness and betweenness centrality). As the results, it became clear that social login registrants have less resistance to purchasing expensive products on an EC site compared with general members and golf gears act as a bridge for purchasing.

Author 1: Kohei Otake

Author 2: Takashi Namatame

Keywords: Social Networking Service; Consumer Behavior; Network Analysis; ID-POS Data

Paper 17: Efficient Hybrid Semantic Text Similarity using Wordnet and a Corpus

Abstract: Text similarity plays an important role in natural language processing tasks such as answering questions and summarizing text. At present, state-of-the-art text similarity algorithms rely on inefficient word pairings and/or knowledge derived from large corpora such as Wikipedia. This article evaluates previous word similarity measures on benchmark datasets and then uses a hybrid word similarity in a novel text similarity measure (TSM). The proposed TSM is based on information content and WordNet semantic relations. TSM includes exact word match, the length of both sentences in a pair, and the maximum similarity between one word and the compared text. Compared with other well-known measures, results of TSM are surpassing or comparable with the best algorithms in the literature.

Author 1: Issa Atoum

Author 2: Ahmed Otoom

Keywords: text similarity; distributional similarity; information content; knowledge-based similarity; corpus-based similarity; WordNet

Paper 18: Trends of Recent Secure Communication System and its Effectiveness in Wireless Sensor Network

Abstract: Wireless sensor network has received increasing attention from the research community since last decade due to multiple problems associated with it. Out of many other significant problems e.g. routing, energy, load balancing, resource allocation, there is a lesser extent of effective security protocols towards solving security pitfalls in wireless sensor network. This paper studies the trend of research manuscript published in last six years about security problems to find that cryptographic techniques received more attention compared to non-cryptographic-based techniques. It also reviews the existing implementation towards addressing security problems and assesses its effectiveness by highlighting beneficial factor as well as limitations. Finally, we extract a research gap to identify the unexplored area of research, which is finalized to be implemented as a part of the future study to overcome the recent security issues.

Author 1: Manjunath B E

Author 2: P.V. Rao

Keywords: Wireless Sensor Network; Security; Cryptography; Encryption; Secured Routing

Paper 19: Estimation Medicine for Diseases System to Support Medical Diagnosis by Expert System

Abstract: Researches confirmed that 70 thousand cases of death, which happen yearly in the world, were because of the misprescribing of the drug itself or its dose (overdose or lower dose). Choosing the wrong alternative drug inspired the professionals in the healthcare field to the importance of assigning the best technologies to decrease the percentages of the therapeutic methods in giving the drug to prevent mistakes in prescribing the suitable drug. A system based on Rete Algorithm is proposed where the best-chosen medicine is offered through the suggested system. Selection of Estimation Medicine for Diseases (EMD) System is introduced where the diagnosis is made basically according to the symptoms and the medical history of the patient. This research aims to acquire a good model using this algorithm to obtain more accurate choices of medicine. The system (EMD) is tested by the doctors in Iraqi hospitals and it has been found that there is no other systems that can be compared to EMD system. The accuracy of estimating the appropriate medicine for heart diseases is approximately (87.26%).

Author 1: Noor T. Mahmood

Keywords: Diagnosis; Disease; Medicine; Rete Algorithm; Expert System; Intelligent System

Paper 20: Context-Sensitive Opinion Mining using Polarity Patterns

Abstract: The growing of Web 2.0 has led to huge information is available. The analysis of this information can be very useful in various fields. In this regards, opinion mining and sentiment analysis are one of the most interesting task that many researchers have paid attention for two last decades. However, this task involves to some challenges that a very important challenge is the different polarity of words in various domain and context. Word polarity is an important feature in the determination of review polarity through sentiment analysis. Existing studies have proposed n-gram technique as a solution which allows the matching of the selected words to the lexicon. However, identification of word polarity using the standard n-gram method poses limitation as it ignores the word placement and its effect according to the contextual domain. Therefore, this study proposes a linguistic-based model to extract the word adjacency patterns to determine the review polarity. The results reflect the superiority of the proposed model compared to other benchmarking approaches.

Author 1: Saeedeh Sadat Sadidpour

Author 2: Hossein Shirazi

Author 3: Nurfadhlina Mohd Sharef

Author 4: Behrouz Minaei-Bidgoli

Author 5: Mohammad Ebrahim Sanjaghi

Keywords: Opinion mining; Polarity patterns; Pattern matching; Context-sensitive; Politics domain

Paper 21: Application of Intelligent Data Mining Approach in Securing the Cloud Computing

Abstract: Cloud computing is a modern term refers to a model for emerging computing, where it is possible to use machines in large data centers for delivering services in a scalable manner, so corporations has become in need for large scale inexpensive computing. Recently, several governments have begun to utilize cloud computing architectures, applications and platforms for meeting the needs of their constituents and delivering services. Security occupies the first rank of obstacles that face cloud computing for governmental agencies and businesses. Cloud computing is surrounded by many risks that may have major effects on services and information supported via this technology. Also, Cloud Computing is one of the promising technology in which the scientific community has recently encountered. Cloud computing is related to other research areas such as distributed and grid computing, Service-Oriented Architecture, and virtualization, as cloud computing inherited their limitations and advancements. It is possible to exploit new opportunities for security. This paper aim is to discuss and analyze how achieve mitigation for cloud computing security risks as a basic step towards obtaining secure and safe environment for cloud computing. The results showed that, Using a simple decision tree model Chaid algorithm security rating for classifying approach is a robust technique that enables the decision-maker to measure the extent of cloud securing, and the provided services. It was proved throughout this paper that policies, standards, and controls are critical in management process to safeguard and protect the systems as well as data. The management process should analyze and understand cloud computing risks for protecting systems and data from security exploits

Author 1: Hanna M. Said

Author 2: Ibrahim El Emary

Author 3: Bader A. Alyoubi

Author 4: Adel A. Alyoubi

Keywords: Cloud computing; Cloud security issue; Data mining; Naive Bayes; multilayer percepton; Support vector machine; decision tree (C4.5); and Partial Tree (PART)

Paper 22: Identifying Green Services using GSLA Model for Achieving Sustainability in Industries

Abstract: Green SLA (GSLA) is a formal agreement between service providers/vendors and users/customers incorporating all the traditional/basic commitments (Basic SLAs) as well as incorporating Ecological, Economical, and Ethical (3Es) aspects of sustainability. Recently, most of the IT (Information Technology) and ICT (Information and Communication Technology) industries are practicing sustainability under green computing domain through designing green services at their scope. However, most of these services only focused on power consumption, energy efficiency, and carbon emission. Moreover, the sustainability can not achieve without considering 3Es simultaneously. The recent development of sustainable GSLA are assisting to identify the missing green services under 3Es. This research attempts to design all missing green services for sustainability by using global informational model of Green SLA. All these newly identified green IT services could reside with other existing services in the industry. Additionally, the design and evaluation technique of these new green services could be used as a guideline for the ICT engineers and as well as other industries too. Moreover, the evaluation and monitoring of new green services are justified using general questionnaires design and analytical tools among the 20 startup ICT industries in Bangladesh and Japan. The proposed idea of designing new green services and their justification methods would be helpful for the ICT engineer to practice sustainability in their competitive businesses.

Author 1: Iqbal Ahmed

Author 2: Hiroshi Okumura

Author 3: Kohei Arai

Keywords: GSLA; Green Services; GaaS; Sustainability; Informational model

Paper 23: Using a Cluster for Securing Embedded Systems

Abstract: In today's increasingly interconnected world, the deployment of an Intrusion Detection System (IDS) is becoming very important for securing embedded systems from viruses, worms, attacks, etc. But IDSs face many challenges like computational resources and ubiquitous threats. Many of these challenges can be resolved by running the IDS in a cluster to allow tasks to be parallelly executed. In this paper, we propose to secure embedded systems by using a cluster of embedded cards that can run multiple instances of an IDS in a parallel way. This proposition is now possible with the availability of new low-power single-board computers (Raspberry Pi, BeagleBoard, Cubieboard, Galileo, etc.). To test the feasibility of our proposed architecture, we run two instances of the Bro IDS on two Raspberry Pi. The results show that we can effectively run multiple instances of an IDS in a parallel way on a cluster of new low-power single-board computers to secure embedded systems.

Author 1: Mohamed Salim LMIMOUNI

Author 2: Khalid BOUKHDIR

Author 3: Hicham MEDROMI

Author 4: Siham BENHADOU

Keywords: cluster; intrusion detection system; embedded system; security; parallel system

Paper 24: Developing a Transition Parser for the Arabic Language

Abstract: One of the most important Characteristics of the Arabic language is the exhaustive undertaking. Thus, analyzing Arabic sentences is difficult because of the length of sentences and the numerous structural complexities. This research aims at developing an Arabic parser and lexicon. A lexicon has been developed with the goal of analyzing and extracting the attributes of Arabic words. The parser was written by using a top–down algorithm parsing technique with recursive transition network. Then, the parser has been evaluated against real sentences and the outcomes were satisfactory.

Author 1: Aref abu Awad

Author 2: Essam Hanandeh

Keywords: Natural language processing; Arabic parser; lexicon; Transition Network

Paper 25: Multi- Spectrum Bands Allocation for Time-Varying Traffic in the Flexible Optical Network

Abstract: The flexible optical networks are the promising solution to the exponential increase of traffic generated by telecommunications networks. They combine flexibility with the finest granularity of optical resources. Therefore, the flexible optical networks position themselves as a better solution than conventional WDM network. In the operational phase, traffic of connections fluctuates. In fact, the user’s need is not the same during day periods. Such traffic may experiment evidence of rising working hours, end of months or years and decreases during the night or on holidays. This variation requires the expansion or contraction of the number of frequency slots allocated to a connection to match the exact needs of the moment. The expansion of the traffic around the reference frequency of connection may lead to blockage because it must share frequency slots with neighboring connections in compliance with the constraints of continuity, contiguity, and non-overlapping. In this study, we offer a technique for allocating frequency slots for time-varying traffic connections. We share out the additional traffic load on different spectrum paths by respecting the constraint of time synchronization related to the differential delay to reduce the blocking rate due to traffic fluctuation.

Author 1: KAMAGATE Beman Hamidja

Author 2: Michel BABRI

Author 3: GOORE Bi Tra

Author 4: Souleymane OUMTANAGA

Keywords: Spectrum band; Multi-spectrum bands; time-varying traffic; elastic optical network

Paper 26: Robust Image Watermarking using Fractional Sinc Transformation

Abstract: The increased utilization of internet in sharing and dissemination of digital data makes it is very difficult to maintain copyright and ownership of data. Digital watermarking offers a method for authentication and copyright protection. Digital image watermarking is an important technique for the multimedia content authentication and copyright protection. This paper present a watermarking algorithm making a balance between imperceptibility and robustness based on fractional calculus and also a domain has constructed using fractional Sinc function (FSc). The FSc model the signal as polynomial for watermark embedding. Watermark is embedded in all the coefficients of the image. Cross correlation method based on Neyman-Pearson is used for watermark detection. Moreover fraction rotation expression has constructed to achieve rotation. Experimental results confirmed the proposed technique has good robustness and outperformed another technique in imperceptibility. Furthermore the proposed method enables blind watermark detection where the original image is not required during the watermark detection and thus making it more practical than non-blind watermarking techniques.

Author 1: Almas Abbasi

Author 2: Chaw Seng Woo

Keywords: Fractional Calculus; fractional Sinc; image Watermarking; robust

Paper 27: A Semantic Approach for Mathematical Expression Retrieval

Abstract: Math search or mathematical expression retrieval has become a challenging task. Mathematical expressions are very complex, they are highly symbolic, and they have a semantic meaning that we should respect. In this paper, we propose a similarity search method for mathematical expression based on a multilevel representation of expressions and a multilevel search. We used the K-Nearest Neighbors with three types of distances to evaluate relevance between expressions. In the experimental level, the proposed system significantly outperforms statistical algorithms.

Author 1: Zahra Asebriy

Author 2: Soulaimane Kaloun

Author 3: Said Raghay

Author 4: Omar Bencharef

Keywords: Mathematical expression; Retrieval information; MathML; Semantic similarity

Paper 28: Variability of Acoustic Features of Hypernasality and it’s Assessment

Abstract: Hypernasality (HP) is observed across voiced phonemes uttered by Cleft-Palate (CP) speakers with defective velopharyngeal (VP) opening. HP assessment using signal processing technique is challenging due to the variability of acoustic features across various conditions such as speakers, speaking style, speaking rate, severity of HP etc. Most of the study for hypernasality (HP) assessment is based on isolated sustained vowels under laboratory conditions. We measure the variability of acoustic features and detect HP using vowel /i/, /a/ and /u/ in continuous read speech with gradually increasing severity of HP of CP speakers. Linear predictive coding (LPC) method is used for acoustic feature extraction. In first part of our study, we observe the variation in acoustic parameters within and across vowel category with gradually increasing HP. We observe that inter-speaker variability in spectral features among CP subjects for vowel /i/ is 0.96, /a/ has 1.13 and vowel /u/ has 2.05. The inter-speaker variability measurement suggests that high back vowel /u/ is mostly affected and has the highest variability. High front vowel /i/ is least affected and has the lowest variability with HP. In the second part, ratio of vowel space area (VSA) of hypernasal and normal speech is calculated and used as a measure for HP detection. We observe that VSA spanned by CP subjects is 0.65 times less than isolated uttered Bangla nasal VSA and 0.43 times less than read speech uttered English oral VSA.

Author 1: Shahina Haque

Author 2: Md. Hanif Ali

Author 3: A.K.M. Fazlul Haque

Keywords: Speech analysis; Acoustic feature; Hypernasality; Cleft palate; Velopharyngeal opening; Vowel space area; Read speech

Paper 29: Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Abstract: The present study aims at recognizing the problem of dynamic virtual machine (VM) Consolidation using virtualization, live migration of VMs from underloaded and overloaded hosts and switching idle nodes to the sleep mode as a very effective approach for utilizing resources and accessing energy efficient cloud computing data centres. The challenge in the present study is to reduce energy consumption thus guarantee Service Level Agreement (SLA) at its highest level. The proposed algorithm predicts CPU utilization in near future using Time-Series method as well as Simple Exponential Smoothing (SES) technique, and takes appropriate action based on the current and predicted CPU utilization and comparison of their values with the dynamic upper and lower thresholds. The four phases in this algorithm include identification of overloaded hosts, identification of underloaded hosts, selection of VMs for migration and identification of appropriate hosts as the migration destination. The study proposes solutions along with dynamic upper and lower thresholds in regard with the first two phases. By comparing current and predicted CPU utilizations with these thresholds, overloaded and underloaded hosts are accurately identified to let migration happen only from the hosts which are currently as well as in near future overloaded and underloaded. The authors have used Maximum Correlation (MC) VM selection policy in the third phase, and attempted in phase four such that hosts with moderate loads, i.e. not overloaded hosts, liable to overloading and underloaded, are selected as the migration destination. The simulation results from the Clouds framework demonstrate an average reduction of 83.25, 25.23 percent and 61.1 in the number of VM migrations, energy consumption and SLA violations (SLAV), respectively.

Author 1: Alireza Najari

Author 2: Seyed EnayatOllah Alavi

Author 3: Mohammad Reza Noorimehr

Keywords: Cloud Computing; Dynamic Consolidation; Energy Consumption; Virtualization; Service Level Agreement

Paper 30: A Light Weight Service Oriented Architecture for the Internet of Things

Abstract: Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and implementation of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval, considering objects as highly decentralized, composite and cost-sufficient services. Such services are constructed from objects located within close geographical proximity to retrieve spatiotemporal events from the gathered sensor data. To achieve this, we advocate coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to accomplish the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70% while maintaining energy consumption 20% lower than its best rivals in the literature.

Author 1: Omar Aldabbas

Keywords: Internet of Things; wireless sensor networks; sensing services; information extraction; data mining

Paper 31: Fingerprint Gender Classification using Univariate Decision Tree (J48)

Abstract: Data mining is the process of analyzing data from a different category. This data provide information and data mining will extracts a new knowledge from it and a new useful information is created. Decision tree learning is a method commonly used in data mining. The decision tree is a model of decision that looklike as a tree-like graph with nodes, branches and leaves. Each internal node denotes a test on an attribute and each branch represents the outcome of the test. The leaf node which is the last node will holds a class label. Decision tree classifies the instance and helps in making a prediction of the data used. This study focused on a J48 algorithm for classifying a gender by using fingerprint features. There are four types of features in the fingerprint that is used in this study, which is Ridge Count (RC), Ridge Density (RD), Ridge Thickness to Valley Thickness Ratio (RTVTR) and White Lines Count (WLC). Different cases have been determined to be executed with the J48 algorithm and a comparison of the knowledge gain from each test is shown. All the result of this experiment is running using Weka and the result achieve 96.28% for the classification rate.

Author 1: S. F. Abdullah

Author 2: A.F.N.A. Rahman

Author 3: Z.A. Abas

Author 4: W.H.M. Saad

Keywords: fingerprint; gender classification; global features; Univariate Decision Tree; J48

Paper 32: Enhancing Wireless Sensor Network Security using Artificial Neural Network based Trust Model

Abstract: Wireless sensor network (WSN) is widely used in environmental conditions where the systems depend on sensing and monitoring approach. Water pollution monitoring system depends on a network of wireless sensing nodes which communicate together depending on a specific topological order. The nodes distributed in a harsh environment to detect the polluted zones within the WSN range based on the sensed data. WSN exposes several malicious attacks as a consequence of its presence in such open environment, so additional techniques are needed alongside with the existing cryptography approach. In this paper an enhanced trust model based on the use of radial base artificial neural network (RBANN) is presented to predict the future behavior of each node based on its weighted direct and indirect behaviors, in order to provide a comprehensive trust model that helps to detect and eliminate malicious nodes within the WSN. The proposed model considered the limited power, storage and processing capabilities of the system.

Author 1: Adwan Yasin

Author 2: Kefaya Sabaneh

Keywords: Wireless sensor network; security; Artificial neural network; trust rate; malicious node; trust model; threat

Paper 33: Security and Privacy Issues in Ehealthcare Systems: Towards Trusted Services

Abstract: Recent years have witnessed a widespread availability of electronic healthcare data record (EHR) systems. Vast amounts of health data were generated in the process of treatment in medical centers such hospitals, clinics, or other institutions. To improve the quality of healthcare service, EHRs could be potentially shared by a variety of users. This results in significant privacy issues that should be addressed to make the use of EHR practical. In fact, despite the recent research in designing standards and regulations directives concerning security and privacy in EHR systems, it is still, however, not completely settled out the privacy challenges. In this paper, a systematic literature review was conducted concerning the privacy issues in electronic healthcare systems. More than 50 original articles were selected to study the existing security approaches and figure out the used security models. Also, a novel Context-aware Access Control Security Model (CARE) is proposed to capture the scenario of data interoperability and support the security fundamentals of healthcare systems along with the capability of providing fine-grained access control.

Author 1: Isra’a Ahmed Zriqat

Author 2: Ahmad Mousa Altamimi

Keywords: Electronic health records; Systematic review; Privacy; Security regulations; Interoperability

Paper 34: Estimation of Trajectory and Location for Mobile Sound Source

Abstract: In this paper, we present an approach to estimate mobile sound source trajectory. An artificially created sound source signal is used in this work. The main aim of this paper is to estimate the mobile object trajectory via sound processing methods. The performance of generalized cross correlation techniques is compared with that of noise reduction filters for the success of trajectory estimation. The azimuth angle between the sound source and receiver is calculated during the whole movement. The parameter of Interaural Time Difference (ITD) is utilized for determining azimuth angle. The success of estimated delay is compared with different types of Generalized Cross Correlation (GCC) algorithms. In this study, an approach for sound localization and trajectory estimation on 2D space is proposed. Besides, different types of pre-filter method are tried for removing the noise and signal smoothing of recorded sound signals. Some basic parameters of sound localization process are also explained. Moreover, the calculation error of average azimuth angle is compared with different GCC and pre-filtered methods. To conclude, it is observed that estimation of location and trajectory information of a mobile object from a stereo sound recording is realized successfully.

Author 1: Mehmet Cem Catalbas

Author 2: Merve Yildirim

Author 3: Arif Gulten

Author 4: Hasan Kurum

Author 5: Simon Dobrišek

Keywords: Sound processing; sound source localization; azimuth angle estimation; generalized cross-correlation; interaural time difference; interaural level difference

Paper 35: Proposed Bilingual Model for Right to Left Language Applications

Abstract: Using right to left languages (RLL) in software programming requires switching the direction of many components in the interface. Preserving the original interface layout and only changing the language may result in different semantics or interpretations of the content. However, this aspect is often dismissing in the field. This research, therefore, proposes a Bilingual Model (BL) to check and correct the directions in social media applications. Moreover, test-driven development (TDD) For RLL, such as Arabic, is considered in the testing methodologies. Similarly, the bilingual analysis has to follow both the TDD and BL models.

Author 1: Farhan M Al Obisat

Author 2: Zaid T Alhalhouli

Author 3: Hazim S. AlRawashdeh

Keywords: software; testing; languages; right to left; development; application; bilingual; social media

Paper 36: Between Transition from IPv4 and IPv6 Adaption: The Case of Jordanian Government

Abstract: IPv6 is being the new replacement for its predecessor IPv4, IPv6 has been used by most Internet services and adopted by most internet architecture these days. Existing protocol IPv4 reveals critical issues such as approaching exhaustion of its address space, continuous growth of the internet and rising new technologies lead to increasing the complexity of the configuration, etc. To healing from the limitations of IPv4 Internet Engineering Task Force (IETF) developed the next generation IP called IPv6. Jordan, like many other countries, is endeavoring to adapt and transit from IPv4 to in an efficient way that will provide an excellent level of service as coveted by its citizens. In this study, the author tried to navigate IPv6 concept from the literature and review the thoughts, steps, and challenges that the Jordanian government pursued in transiting from IPv4 to IPv6.

Author 1: Iman Akour

Keywords: IP networks; IPv6 protocol; IPv6 road map; ipv6 transition; IPv6 adoption

Paper 37: A Machine Vision System for Quality Inspection of Pine Nuts

Abstract: Computers and artificial intelligence have penetrated in the food industry since last decade, for intellectual automatic processing and packaging in general, and in assisting for quality inspection of the food itself in particular. The food quality assessment task becomes more challenging when it is about harmless internal examination of the ingredient, and even more when its size is also minute. In this article, a method for automatic detection, extraction and classification of raw food item is presented using x-ray image data of pine nuts. Image processing techniques are employed in developing an efficient method for automatic detection and then extraction of individual ingredient, from the source x-ray image which comprises bunch of nuts in a single frame. For data representation, statistical texture analysis is carried out and attributes are calculated from each of the sample image on the global level as features. In addition co-occurrence matrices are computed from images with four different offsets, and hence more features are extracted by using them. To find fewer meaningful characteristics, all the calculated features are organized in several combinations and then tested. Seventy percent of image data is used for training and 15% each for cross-validation and test purposes. Binary classification is performed using two state-of-the-art non-linear classifiers: Artificial Neural Network (ANN) and Support Vector Machines (SVM). Performance is evaluated in terms of classification accuracy, specificity and sensitivity. ANN classifier showed 87.6% accuracy with correct recognition rate of healthy nuts and unhealthy nuts as 94% and 62% respectively. SVM classifier produced the similar accuracy achieving 86.3% specificity and 89.2% sensitivity rate. The results obtained are unique itself in terms of ingredient and promising relatively. It is also found that feature set size can be reduced up to 57% by compromising 3.5% accuracy, in combination with any of the tested classifiers.

Author 1: Ikramullah Khosa

Author 2: Eros Pasero

Keywords: pine nuts; Image processing; neural networks; feature extraction; classification

Paper 38: Predicting CO2 Emissions from Farm Inputs in Wheat Production using Artificial Neural Networks and Linear Regression Models

Abstract: Two models have been developed for simulating CO2 emissions from wheat farms: (1) an artificial neural network (ANN) model; and (2) a multiple linear regression model (MLR). Data were collected from 40 wheat farms in the Canterbury region of New Zealand. Investigation of more than 140 various factors enabled the selection of eight factors to be employed as the independent variables for final the ANN model. The results showed the final ANN developed can forecast CO2 emissions from wheat production areas under different conditions (proportion of wheat cultivated land on the farm, numbers of irrigation applications and numbers of cows), the condition of machinery (tractor power index (hp/ha) and age of fertilizer spreader) and N, P and insecticide inputs on the farms with an accuracy of ±11% (± 113 kg CO2/ha). The total CO2 emissions from farm inputs were estimated as 1032 kg CO2/ha for wheat production. On average, fertilizer use of 52% and fuel use of around 20% have the highest CO2 emissions for wheat cultivation. The results confirmed the ANN model forecast CO2 emissions much better than MLR model.

Author 1: Majeed Safa

Author 2: Mohammadali Nejat

Author 3: Peter Nuthall

Author 4: Bruce Greig

Keywords: Artificial neural networks; modelling; CO2 emissions; wheat cultivation

Paper 39: E-Learning for Secondary and Higher Education Sectors: A Survey

Abstract: Electronic learning (e-learning) has gained reasonable acceptance from educational institutions at all levels. There are various studies conducted by researchers considering different aspects of e-learning to investigate how we can benefit in imparting quality education. However, there is a requirement to find out how researchers consider different sectors of secondary and higher education (HE) sectors. In this paper, we carefully select published research article of past six years and study how the research was conducted and which research methods are applied to attain results. We also investigate how case studies are presented for evaluating results. We finally present our findings from conducting this study of e-learning research at secondary and the higher education levels.

Author 1: Sadia Ashraf

Author 2: Tamim Ahmed Khan

Author 3: Inayat ur Rehman

Keywords: Distributed learning environments; elementary education; improving classroom teaching; intelligent tutoring systems; interactive learning environments; media in education; post-secondary education; secondary education; simulations

Paper 40: Design of a Prediction System for Hydrate Formation in Gas Pipelines using Wireless Sensor Network

Abstract: Before the evolution of the Wireless Sensor Networks (WSN) technology, many production wells in the oil and gas industry were suffering from the gas hydration formation process, as most of them are remotely located away from the host location. By taking the advantage of the WSN technology, it is possible now to monitor and predict the critical conditions at which hydration will form by using any computerized model. In fact, most of the developed models are based on two well-known hand calculation methods which are the Specific gravity and K-Factor methods. In this research, the proposed work is divided into two phases; first, the development of a three prediction models using the Neural Network algorithm (ANN) based on the specific gravity charts, the K-Factor method and the production rates of the flowing gas mixture in the process pipelines. While in the second phase, two WSN prototype models are designed and implemented using National Instruments WSN hardware devices. Power analysis is carried out on the designed prototypes and regression models are developed to give a relation between the sensing nodes (SN) consumed current, Node-to-Gateway distance and the operating link quality. The prototypes controller is interfaced with a GSM module and connected to a web server to be monitored via mobile and internet networks.

Author 1: Ahmed Raed Moukhtar

Author 2: Alaa M. Hamdy

Author 3: Sameh A. Salem

Keywords: WSN; Sensing Node; K-Factor; ANN; Link Quality Indicator; Hydrate Formation Temperature; Received Signal Strength Indicator

Paper 41: The Role of Image Enhancement in Citrus Canker Disease Detection

Abstract: Digital image processing is employed in numerous areas of biology to identify and analyse problems. This approach aims to use image processing techniques for citrus canker disease detection through leaf inspection. Citrus canker is a severe bacterium-based citrus plant disease. The symptoms of citrus canker disease typically occur in the leaves, branches, fruits and thorns. The leaf images show the health status of the plant and facilitate the observation and detection of the disease level at an early stage. The leaf image analysis is an essential step for the detection of numerous plant diseases. The proposed approach consists of two stages to improve the clarity and quality of leaf images. The primary stage uses Recursively Separated Weighted Histogram Equalization (RSWHE), which improves the contrast level. The second stage removes the unwanted noise using a Median filter. This proposed approach uses these methods to improve the clarity of the images and implements these methods in lemon citrus canker disease detection.

Author 1: K. Padmavathi

Author 2: K. Thangadurai

Keywords: Lemon tree; Citrus Canker; Recursively Separated Weighted Histogram Equalization; Median Filter; Image Enhancement; Disease detection

Paper 42: Analysis of Compensation Network in a Correlated-based Channel using Angle of Arrivals

Abstract: We explore combined effect of spatial correlation and mutual coupling matrix, and its subsequent effects on performance of multiple input multiple output (MIMO) systems After the decoupling process. We will also look at a correlation based stochastic channel model with the linear antenna arrays as the signal source. For the purpose of understanding, it is assumed that fading is correlated at both transmitter and receiver sides, in spite of the fact that the decoupling network enhances isolation between Receiving antenna array. In this paper, we model the transmit the antenna array in CST Microwave Studio, as a uniform linear Array with monopoles as antenna elements. On the receiving side, the scattering parameters of the coupled and decoupled monopole Array are measured in an anechoic chamber. The theoretical analysis and simulation results show the joint dependency of the system capacity on an angle of arrival (AoA) and antenna element spacing, with enhanced system performance at reduced AoAs with Increased antenna element separation. Consequently, essential benefits of MIMO system performance can be achieved with an efficient decoupling network while boosting the signal sources by adding further antenna elements.

Author 1: Affum Emmanuel Ampoma

Author 2: Paul Oswald Kwasi Anane

Author 3: Obour Agyekum Kwame O.-B

Author 4: Maxwell Oppong Afriyie

Keywords: Angle of arrival (AoA); channel correlation; decoupling network; mutual coupling; MIMO

Paper 43: Differential Evolution based SHEPWM for Seven-Level Inverter with Non-Equal DC Sources

Abstract: This paper presents the application of differential evolution algorithm to obtain optimal switching angles for a single-phase seven-level to improve AC voltage quality. The proposed inverter in this article is composed of two H-bridge cells with non-equal DC voltage sources in order to generate multiple voltage levels. Selective harmonic elimination pulse width modulation (SHPWM) strategy is used to improve the AC output voltage waveform generated by the proposed inverter. The differential evolution (DE) optimization algorithm is used to solve non-linear transcendental equations necessary for the (SHPWM). Computational results obtained from computer simulations presented a good agreement with the theoretical predictions. A laboratory prototype based on STM32F407 microcontroller was built in order to validate the simulation results. The experimental results show the effectiveness of the proposed modulation method.

Author 1: Fayçal CHABNI

Author 2: Rachid TALEB

Author 3: M’hamed HELAIMI

Keywords: selective harmonic elimination; multi-level inverters; differential evolution; cascade H-bridge inverters; optimization

Paper 44: Human Face Classification using Genetic Algorithm

Abstract: The paper presents a precise scheme for the development of a human face classification system based human emotion using the genetic algorithm (GA). The main focus is to detect the human face and its facial features and classify the human face based on emotion, but not the interest of face recognition. This research proposed to combine the genetic algorithm and neural network (GANN) for classification approach. There are two way for combining genetic algorithm and neural networks, such as supportive approach and collaborative approach. This research proposed the supportive approach to developing an emotion-based classification system. The proposed system received frontal face image of human as input pattern and detected face and its facial feature regions, such as, mouth (or lip), nose, and eyes. By the analysis of human face, it is seen that most of the emotional changes of the face occurs on eyes and lip. Therefore, two facial feature regions (such as lip and eyes) have been used for emotion-based classification. The GA has been used to optimize the facial features and finally the neural network has been used to classify facial features. To justify the effectiveness of the system, several images were tested. The achievement of this research is higher accuracy rate (about 96.42%) for human frontal face classification based on emotion.

Author 1: Tania Akter Setu

Author 2: Md. Mijanur Rahman

Keywords: Face Detection; Facial Feature Extraction; Genetic Algorithm; Neural Network

Paper 45: An Example-based Super-Resolution Algorithm for Multi-Spectral Remote Sensing Images

Abstract: This paper proposes an example-based super-resolution algorithm for multi-spectral remote sensing images. The underlying idea of this algorithm is to learn a matrix-based implicit prior from a set of high-resolution training examples to model the relation between LR and HR images. The matrix-based implicit prior is learned as a regression operator using conjugate decent method. The direct relation between LR and HR image is obtained from the regression operator and it is used to super-resolve low-resolution multi-spectral remote sensing images. A detailed performance evaluation is carried out to validate the strength of the proposed algorithm.

Author 1: W. Jino Hans

Author 2: Lysiya Merlin.S

Author 3: Venkateswaran N

Author 4: Divya Priya T

Keywords: Remote sensing Super-resolution; Image-pair analysis; Regression operators

Paper 46: Fitness Proportionate Random Vector Selection based DE Algorithm (FPRVDE)

Abstract: Differential Evolution (DE) is a simple, powerful and easy to use global optimization algorithm. DE has been studied in detail by many researchers in the past years. In DE algorithm trial vector generation strategies have a significant influence on its performance. This research studies that whether performance of DE algorithm can be improved by incorporating selection advancement in effective trial vector generation strategies. A novel advancement in DE trial vector generation strategies is proposed in this research to speeds up the convergence speed of DE algorithm. The proposed fitness proportion based random vector selection DE (FPRVDE) is based on the proportion of individual fitness mechanism. FPRVDE reduces the role of poor performing individuals to enhance it performance capability of DE algorithm. To form a trial vector using FPRVDE, individual based on the proportion of their fitness are selected. FPRVDE mechanism is applied to most commonly used set of DE variants. A comprehensive set of multidimensional function optimization problems is used to access the performance of FPRVDE. Experimental result shows that proposed approach accelerates DE algorithm.

Author 1: Qamar Abbas

Author 2: Jamil Ahmad

Author 3: Hajira Jabeen

Keywords: Differential Evolution; Fitness Proportion; Trial vector generation; Mutation; Optimization

Paper 47: Internet of Things based Expert System for Smart Agriculture

Abstract: Agriculture sector is evolving with the advent of the information and communication technology. Efforts are being made to enhance the productivity and reduce losses by using the state of the art technology and equipment. As most of the farmers are unaware of the technology and latest practices, many expert systems have been developed in the world to facilitate the farmers. However, these expert systems rely on the stored knowledge base. We propose an expert system based on the Internet of Things (IoT) that will use the input data collected in real time. It will help to take proactive and preventive actions to minimize the losses due to diseases and insects/pests.

Author 1: Raheela Shahzadi

Author 2: Javed Ferzund

Author 3: Muhammad Tausif

Author 4: Muhammad Asif Suryani

Keywords: Internet of Things; Smart Agriculture; Cotton; Plant Diseases; Wireless Sensor Network

Paper 48: Dependency Test: Portraying Pearson's Correlation Coefficient Targeting Activities in Project Scheduling

Abstract: In this paper, we discuss project scheduling with conflicting activity-resources. Several project activities require same resources but, may be scheduled with the certain lapse of time resulting in repeatedly using the same kind of resources for executing dissimilar activities. Due to the frequent usage of same resources multiple times, expenditure become more expensive and project duration extends. The problem is to find out such kind of activities which are developing implicit relations amid them. , we proposed a solution by introducing TVs (Transparent view of Scheduling) model. First, we analyze and enlists activities according to required resources, categorize them and then we segregate dependent and independent activities by indicating a value. Performing Dependency test on activities by using Pearson's Correlation Coefficient (PCC) to calculate the rate of relations among the ordered activities for similar resources. By using this model we can reschedule activities to avoid confusion and disordering of resources without consumption of time and capital.

Author 1: Jana Shafi

Author 2: Amtul Waheed

Author 3: Sumaya Sanober

Keywords: TVS; Transparent; Dependency; PCC; Activity; Resource; Schedule; Project Introduction

Paper 49: Comparison of Digital Signature Algorithm and Authentication Schemes for H.264 Compressed Video

Abstract: In this paper we present the advantages of the elliptic curve cryptography for the implementations of the electronic signature algorithms “elliptic curve digital signature algorithm, ECDSA”, compared with “the digital signature algorithm, DSA”, for the signing and authentication of H.264 compressed videos. Also, we compared the strength and add-time of these algorithms on a database containing several videos sequences.

Author 1: Ramzi Haddaji

Author 2: Samia Bouaziz

Author 3: Raouf Ouni

Author 4: Abdellatif Mtibaa

Keywords: Elliptic curve cryptography; H.264; DSA (Digital signature algorithm); ECDSA (Elliptic Curve Digital Signature Algorithm); Implementation

Paper 50: A Novel Information Retrieval Approach using Query Expansion and Spectral-based

Abstract: Most of the information retrieval (IR) models rank the documents by computing a score using only the lexicographical query terms or frequency information of the query terms in the document. These models have a limitation as they does not consider the terms proximity in the document or the term-mismatch or both of the two. The terms proximity information is an important factor that determines the relatedness of the document to the query. The ranking functions of the Spectral-Based Information Retrieval Model (SBIRM) consider the query terms frequency and proximity in the document by comparing the signals of the query terms in the spectral domain instead of the spatial domain using Discrete Wavelet Transform (DWT). The query expansion (QE) approaches are used to overcome the word-mismatch problem by adding terms to query, which have related meaning with the query. The QE approaches are divided to statistical approach Kullback-Leibler divergence (KLD) and semantic approach P-WNET that uses WordNet. These approaches enhance the performance. Based on the foregoing considerations, the objective of this research is to build an efficient QESBIRM that combines QE and proximity SBIRM by implementing the SBIRM using the DWT and KLD or P-WNET. The experiments conducted to test and evaluate the QESBIRM using Text Retrieval Conference (TREC) dataset. The result shows that the SBIRM with the KLD or P-WNET model outperform the SBIRM model in precision (P@), R-precision, Geometric Mean Average Precision (GMAP) and Mean Average Precision (MAP).

Author 1: Sara Alnofaie

Author 2: Mohammed Dahab

Author 3: Mahmoud Kamal

Keywords: Information Retrieval; Discrete Wavelet Transform; Query Expansion; Term Signal; Spectral Based Retrieval Method

Paper 51: A Hybrid Steganography System based on LSB Matching and Replacement

Abstract: This paper proposes a hybrid steganographic ap-proach using the least significant bit (LSB) technique for grayscale images. The proposed approach uses both LSB match-ing (LSB-M) and LSB replacement to hide the secret data in images. Using hybrid LSB techniques increase the level of security. Thus, attackers cannot easily, if not impossible, extract the secret data. The proposed approach stores two bits in a pixel. The embedding rate can reach up to 1.6 bit per pixel. The proposed approach is evaluated and subjected to various kinds of image processing attacks. The performance of the proposed algorithm is compared with two other relevant techniques; pixel-value differencing (PVD) and Complexity Based LSB-M (CBL). Experimental results indicate that the proposed algorithm out-performs PVD in terms of imperceptibility. Also, it significantly outperforms CBL in two main features; higher embedding rate (ER), and more robust to most common image processing attacks such as median filtering, histogram equalization, and rotation.

Author 1: Hazem Hiary

Author 2: Khair Eddin Sabri

Author 3: Mohammed S. Mohammed

Author 4: Ahlam Al-Dhamari

Keywords: Steganography; LSB matching; LSB replacement; Embedding capacity; Imperceptibility

Paper 52: A Novel High Dimensional and High Speed Data Streams Algorithm: HSDStream

Abstract: This paper presents a novel high speed clustering scheme for high-dimensional data stream. Data stream clustering has gained importance in different applications, for example, network monitoring, intrusion detection, and real-time sensing. High dimensional stream data is inherently more complex when used for clustering because the evolving nature of the stream data and high dimensionality make it non-trivial. In order to tackle this problem, projected subspace within the high dimensions and limited window sized data per unit of time are used for clustering purpose. We propose a High Speed and Dimensions data stream clustering scheme (HSDStream) which employs exponential mov-ing averages to reduce the size of the memory and speed up the processing of projected subspace data stream. It works in three steps: i) initialization, ii) real-time maintenance of core and outlier micro-clusters, and iii) on-demand offline generation of the final clusters. The proposed algorithm is tested against high dimensional density-based projected clustering (HDDStream) for cluster purity, memory usage, and the cluster sensitivity. Experi-mental results are obtained for corrected KDD intrusion detection dataset. These results show that HSDStream outperforms the HDDStream in all performance metrics, especially, the memory usage and the processing speed.

Author 1: Irshad Ahmed

Author 2: Irfan Ahmed

Author 3: Waseem Shahzad

Keywords: Evolving data stream; high dimensionality; pro-jected clustering; density-based clustering; micro-clustering

Paper 53: Anti-noise Capability Improvement of Minimum Energy Combination Method for SSVEP Detection

Abstract: Minimum energy combination (MEC) is a widely used method for frequency recognition in steady state visual evoked potential based BCI systems. Although it can reach acceptable performances, this method remains sensitive to noise. This paper introduces a new technique for the improvement of the MEC method allowing ameliorating its Anti-noise capability. The Empirical mode decomposition (EMD) and the moving average filter were used to separate noise from relevant signals. The results show that the proposed BCI system has a higher accuracy than systems based on Canonical Correlation Analysis (CCA) or Multivariate Synchronization Index (MSI). In fact, the system achieves an average accuracy of about 99% using real data measured from five subjects by means of the EPOC EMOTIVE headset with three visual stimuli. Also by using four commands, the system accuracy reaches 91.78% with an information-transfer rate of about 27.18 bits/min.

Author 1: Omar Trigui

Author 2: Wassim Zouch

Author 3: Mohamed Ben Messaoud

Keywords: Brain-Computer Interface; Steady State Visual Evoked Potential; Minimum Energy Combination; Empirical Mode Decomposition

Paper 54: An Analysis on Natural Image Small Patches

Abstract: The method of computational homology is used to analyze natural image 8 × 8 and 9x9-patches locally. Our experimental results show that there exist subspaces of the spaces of 8x8 and 9x9-patches that are topologically equivalent to a circle and a Klein bottle respectively. These extend the results of the paper ”on the local behavior of spaces of natural images.” To the larger patches. The Klein bottle feature of natural image patches can be used in image compression.

Author 1: Shengxiang Xia

Author 2: Wen Wang

Author 3: Di Liang

Keywords: natural image analysis; persistent homology; high-contrast patches; Klein bottle; barcode

Paper 55: Intelligent Pedestrian Detection using Optical Flow and HOG

Abstract: Pedestrian detection is an important aspect of autonomous vehicle driving as recognizing pedestrians helps in reducing accidents between the vehicles and the pedestrians. In literature, feature based approaches have been mostly used for pedestrian detection. Features from different body portions are extracted and analyzed for interpreting the presence or absence of a person in a particular region in front of car. But these approaches alone are not enough to differentiate humans from non-humans in dynamic environments, where background is continuously changing. We present an automated pedestrian detection system by finding pedestrians’ motion patterns and combing them with HOG features. The proposed scheme achieved 17.7% and 14.22% average miss rate on ETHZ and Caltech datasets, respectively.

Author 1: Huma Ramzan

Author 2: Bahjat Fatima

Author 3: Ahmad R. Shahid

Author 4: Sheikh Ziauddin

Author 5: Asad Ali Safi

Keywords: Pedestrian detection, pedestrian protection system, HOG descriptor, optical flow, motion vectors, FPPI, miss-rate

Paper 56: Modeling and Analyzing Anycast and Geocast Routing in Wireless Mesh Networks

Abstract: Wireless technology has become an essential part of this era’s human life and has the capability of connecting virtually to any place within the universe. A mesh network is a self healing wireless network, built through a number of distributed and redundant nodes to support variety of applications and provide reliability. Similarly, anycasting is an important service that might be used for a variety of applications. In this paper we have studied anycast routing in the wireless mesh networks and the anycast traffic from the gateway to the mesh network having multiple anycast groups. We have also studied the geocast traffic in which the packets reach to the group head via unicast traffic and then are broadcasted inside the group. Moreover, we have studied the intergroup communication between different anycast groups. The review of the related literature shows that no one has considered anycasting and geocasting from gateway to the mesh network while considering the multiple anycast groups and intergroup communication. The network is modeled, simulated and analyzed for its various parameters using OMNET++ simulator.

Author 1: Fazle Hadi

Author 2: Sheeraz Ahmed

Author 3: Abid Ali Minhas

Author 4: Atif Naseer

Keywords: Mesh Network; Anycast; Geocast; Routing; Unicast

Paper 57: MOSIC: Mobility-Aware Single-Hop Clustering Scheme for Vehicular Ad hoc Networks on Highways

Abstract: As a new branch of Mobile ad hoc networks, Vehicular ad hoc networks (VANETs) have significant attention in academic and industry researches. Because of high dynamic nature of VANET, the topology will be changed frequently and quickly, and this condition is causing some difficulties in maintaining topology of these kinds of networks. Clustering is one of the controlling mechanism that able to grouping vehicles in same categories based upon some predefined metrics such as density, geographical locations, direction and velocity of vehicles. Using of clustering can make network’s global topology less dynamic and improve the scalability of it. Many of the VANET clustering algorithms are taken from MANET that has been shown that these algorithms are not suitable for VANET. Hence, in this paper we proposed a new clustering scheme that use Gauss Markov mobility (GMM) model for mobility predication that make vehicle able to prognosticate its mobility relative to its neighbors. The proposed clustering scheme’s goal is forming stable clusters by increasing the cluster head lifetime and less cluster head changes number. Simulation results show that the proposed scheme has better performance than existing clustering approach, in terms of cluster head duration, cluster member duration, cluster head changes rate and control overhead.

Author 1: Amin Ziagham Ahwazi

Author 2: MohammadReza NooriMehr

Keywords: Vehicular Ad hoc Networks; Mobile ad hoc Net-works; Network Topology Control; Clustering Scheme

Paper 58: Peak-to-Average Power Ratio Reduction based Varied Phase for MIMO-OFDM Systems

Abstract: One of the severe drawbacks of orthogonal fre-quency division multiplexing (OFDM) is high Peak-to-Average Power Ratio (PAPR) of transmitted OFDM signals. During modulation the sub-carriers are added together with same phase which increases the value of PAPR, leading to more interference and limits power efficiency of High Power Amplifier (HPA), it’s requires power amplifier’s (PAs) with large linear oper-ating ranges but such PAs are difficult to design and costly to manufacture. Therefore, to reduce PAPR various methods have been proposed. As a promising scheme, partial transmit sequences (PTS) provides an effective solution for PAPR reduction of OFDM signals. In this paper, we propose a PAPR reduction method for an OFDM system with variation of phases based on PTS schemes and Solid State Power Amplifiers (SSPA) of Saleh model in conjunction with digital predistortion (DPD), in order to improve the performance in terms of PAPR, the HPA linearity and for the sake of mitigating the in-band distortion and the spectrum regrowth. The simulation results show that the proposed algorithm can not only reduces the PAPR significantly, but also improves the out-of-band radiation and decreases the computational complexity.

Author 1: Lahcen Amhaimar

Author 2: Saida Ahyoud

Author 3: Adel Asselman

Author 4: Elkhaldi Said

Keywords: OFDM; MIMO; PAPR; PTS; HPA; GA

Paper 59: Solving Nonlinear Eigenvalue Problems using an Improved Newton Method

Abstract: Finding approximations to the eigenvalues of non-linear eigenvalue problems is a common problem which arises from many complex applications. In this paper, iterative algo-rithms for finding approximations to the eigenvalues of nonlinear eigenvalue problems are verified. These algorithms use an efficient numerical approach for calculating the first and second deriva-tives of the determinant of the problem. Here we present and examine a technique for solving nonlinear eigenvalue problems using Newton method. Computational aspects of this approach for a nonlinear eigenvalue problem are analyzed. The efficiency of the algorithm is demonstrated using an example.

Author 1: S.A Shahzadeh Fazeli

Author 2: F. Rabiei

Keywords: nonlinear eigenvalue problems; Newton method; LU-decomposition; refined eigenvalues

Paper 60: Automatic Generation of Model for Building Energy Management

Abstract: This paper proposes a model transformation ap-proach for model-based energy management in buildings. Indeed, energy management is a large area that covers a wide range of applications such as simulation, mixed integer linear pro-gramming optimization, simulated annealing optimization, model parameter estimation, diagnostic analysis,. . . Each application re-quires a model but in a specific formalism with specific additional information. Up to now, application models are rewritten for each application. In building energy management, because the optimization problems may be dynamically generated, model transformation should be done dynamically, depending on the problem to solve. For this purpose, a model driven engineering approach combined with the use of a computer algebra system is proposed. This paper presents the core specifications of the transformation of a so-called high level pivot model into applica-tion specific models. As an example, transformations of a pivot model into both an acausal linear model for mixed integer linear programming optimization and a causal non-linear model for simulated annealing optimization are presented. These models are used for energy management of a smart building platform named Monitoring and Habitat Intelligent located at PREDIS/ENSE3 in Grenoble, France.

Author 1: Quoc-Dung Ngo

Author 2: Yanis Hadj-Said

Author 3: St´ephane Ploix

Author 4: Ujjwal Maulik

Keywords: building energy management system, model trans-formation, model driven engineering, optimization, mixed integer linear programming, simulated annealing