July 2012, No. 4
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Reliable Broadcasting Using Efficient Forward Node Selection for Mobile Adhoc Networks

Govindaswamy Kalpana and Muthusamy Punithavalli
1Department of Computer Science, Sri Ramakrishna College of Arts and Science for women, Bharathiar University, India

Abstract: Due to the broadcasting nature of radio transmission, the most fundamental task in MANETs is the broadcast operation. All the current routing protocols depend upon the easier form of broadcasting called flooding which can result in high broadcast redundancy and packet collisions. In this paper, we propose to develop a Reliable Broadcasting algorithm which is a Sender-based algorithm. In this algorithm, the broadcasting nodes select a subset of their neighbors to forward the message using an efficient forward node selection mechanism. The retransmissions of the forwarding nodes are overheard by the sender as the confirmation of their reception of the packet. Moreover, a NACK mechanism is used to provide full reliability for all non forwarding nodes. This algorithm reduces the average retransmission redundancy, avoids both the broadcast storm problem and the ACK implosion problem, recovers the transmission error locally and increases the broadcast delivery ratio. By simulation results, we show that our proposed algorithm achieves good delivery ratio with less forwarding and control overhead.

Keywords: Mobile adhoc networks, broadcasting, forward node selection, reliable broadcasting algorithm, and sensor.

Received January 19, 2010; accepted August 10, 2010  

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Training of Fuzzy Neural Networks via Quantum-Behaved Particle Swarm Optimization and Rival Penalized Competitive Learning

Saeed Farzi
 Faculty of Computer Engineering, Islamic Azad University, Iran

Abstract: There are some difficulties encountered in the application of fuzzy radial basis function neural network. One of them is how to determine the number of hidden (rule) neurons and another difficulty is about interpretability. In order to overcome these difficulties, we have proposed a fuzzy neural network based on radial basis function network and takagi-sugeno fuzzy system. We have used a new structure of fuzzy radial basis function neural network, which has been proved that it is better than other structures in term of interpretability. Our model also uses rival penalized competitive learning and a swarm based algorithm called quantum-behaved particle swarm optimization to determine design parameters of hidden layer and design parameters of output layer, respectively-rival penalized competitive learning is the best clustering algorithm that is introduced so far. The particle swarm optimization is a well-known population-based swarm intelligence algorithm. The quantum-behaved particle swarm optimization is also proposed by combining the classical particle swarm optimization philosophy and quantum mechanics to improve performance of particle swarm optimization. We have compared the performance of the proposed method with gradient based method. Simulation results of nonlinear function approximation demonstrate the superiority of the proposed method over gradient based method.

Keywords: Fuzzy radial basis function, rival penalized competitive learning, and particle swarm optimization.

Received January 22, 2010; accepted August 10, 2010  

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The Effect of Using P-XCAST Routing Protocol on Many-to-Many Applications

Faisal Alzyoud and Tat-Chee Wan
School of Computer Sciences, Universiti Sains Malaysia, Malaysia

Abstract: There are two types of wireless networks, infrastructure wireless network and Wireless ad hoc networks. Wireless ad hoc networks are well suited for use by emergency response teams, for search and rescue operations that require team-based communications in the absence of working telecommunications infrastructure, while infrastructure networks require the existence of access point in which all the communications are done through it. Unfortunately, wireless ad hoc networks suffer from limited bandwidth and QoS constraints. A Priority eXplicit multiCAST based routing protocol (P-XCAST) is presented in this paper to support team-based many-to-many communications in wireless ad hoc networks. eXplicit multiCAST (XCAST) is well suited for supporting a large number of small groups effectively, in comparison with multicast based protocols. However, since XCAST was initially designed for wired networks, it was not optimized for wireless ad hoc network use. The proposed P-XCAST protocol enhances XCAST for wireless ad hoc network use by modifying the Route Request mechanism in AODV to build the network topology, and route data packets containing the list of destinations for a given group in the XCAST header, by classifying the destinations according to similarities in their next hop neighbors and hop counts. A single data packet is XCASTed in lieu of sending n Unicast data packets to n destinations with the same next hop neighbor. In addition, P-XCAST is merged with a new mobile group management protocol to handle mobility of group members. In this paper, P-XCAST was tested using topologies with different sources that were sending and receiving data at the same time to handle foreground and background many-to-many applications. The results of simulation experiments show that P-XCAST achieved better QoS performance compared with other routing protocols for small group sizes typical of group communications applications such as Push-To-Talk (PTT).

  Keywords: MANETs, P-XCAST, QoS, and PTT.

Received January 23, 2010; accepted May 20, 2010 

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Automatic Plagiarism Detection using Similarity Analysis

Shanmugasundaram Hariharan
Department of Information Technology, School of Computer and Information Sciences
JJ College of Engineering and Technology, India.

Abstract: Plagiarism involves reproducing the existing information in modified format or sometimes the original document as it is. This is quiet common among students, researchers and academicians. This has made some strong influence on research community and awareness among academic peoples to prevent such a kind of malpractice. Though there exits some commercial tools to detect plagiarism, still plagiarism is tricky and quiet challenging task due to abundant information available online. Commercially existing software adopt methods like paraphrasing, sentence matching or keyword matching. Such techniques are not too good in identifying the plagiarized contents effectively. However this paper focuses its attention on identifying some key parameters that would help to identify plagiarism in a better manner. The results seem to be promising and have further scope in detecting the plagiarism.   

Keywords: Plagiarism detection, similarity measures, information extraction, and text matching.

Received February 1, 2010; accepted October 24, 2010

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A Hybrid Approach for Modeling
Financial Time Series

Alina Barbulescu and Elena Bautu
 Faculty of Mathematics and Computer Science, Ovidius University, Constanta, Romania

Abstract: The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate different linear and non-linear patterns. ARMA models, although flexible, assume a linear form for the models. GEP evolves models adapting to the data without any restrictions with respect to the form of the model or its coefficients. Our approach benefits from the capability of ARMA to identify linear trends as well as GEP’s ability to obtain models that capture nonlinear patterns from data. Investigations are performed on real data sets. They show a definite improvement in the accuracy of forecasts of the hybrid method over pure ARMA and GEP used separately. Experimental results are analyzed and discussed. Conclusions and some directions for further research end the paper.

Keywords: Financial time series, forecasting, ARMA, GEP, and hybrid methodology.

 Received February 6, 2010; accepted October 24, 2010

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Intrinsic Authentication of Multimedia Objects Using Biometric Data Manipulation

Maqsood Mahmud1, 2, 3, Muhammad Khan1, Khaled Alghathbar1, 2, Abdul Hanan Bin Abdullah3,
and Mohammad Bin Idris3
1Center of Excellence in Information Assurance, King Saud University, Saudi Arabia
2 Department of Information Systems, King Saud University, Saudi Arabia
3Faculty of Computer Science and Information Systems, University Technology, Malaysia 
Abstract: The Biometric-Gaussian-Stream (BGS) cryptosystem was extended by extensive research experimentation. Using this system, complexity is added to an image by passing it through a Gaussian noise function. This function is applied with specific parameters for the mean and variance, which also works as a parallel key. To implement a stream cipher (BGS) with help of biometric images, the Initial Condition (IC) for (Linear Feed Back Shift Register) LFSR  from is extracted iriscode. A comparison between various stream ciphers is also made to measure the strength of the BGS cryptosystem. The previous experimentation work has been extended by formulating the algorithmic runtime complexity of the BGS Cryptosystem which proves to be O (n) algorithmically. New techniques to encrypt and assess multimedia objects have been introduced.

Keywords: Cryptosystem, LFSR, gaussian noise, computational runtime complexity, and stream ciphers.

Received February 14, 2010; accepted May 20, 2010.

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OIAHCR: Online Isolated Arabic Handwritten Character Recognition Using Neural Network

Basem Al-Ijla and Kathrein Kwaik
      Faculty of Information Technology, Islamic University Of Gaza, Palestine

Abstract: In this paper, an online Isolated Arabic Handwritten Character Recognition system is introduced. The system can be adapted to achieve the demands of hand-held and digital tablet applications. To achieve this goal, despite of single Neural Networks, four Neural Networks are used, one for each cluster of characters. Feed Forward Back Propagation Neural Networks are used in classification process. This approach is employed as classifiers due to the low computation overhead during training and recall process. The system recognizes On-Line isolated Arabic character and achieves an accuracy rate 95.7% from untrained writers and 99.1% for trained writers.

Keywords: Back propagation, classification, feature extraction, feature selection, feed forward neural networks, and Optical Character Recognition (OCR).

Received February 15, 2010; accepted May 20, 2010

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Impact of CMMI-Based Process Maturity Levels on Effort, Productivity and Diseconomy of Scale

Majed Alyahya, Rodina Ahmad, and Sai Lee
Department of Software Engineering, University of Malaya, Malaysia
Abstract: The Software Capability Maturity Model Integration (CMMI) has become a popular Software Process Improvement (SPI) model for enhancing software development processes with the goal of developing high-quality software within budget and schedule. Since Software development effort can be greatly affected by the organizational process maturity level, this study examines the impact of different CMMI-based process maturity levels on effort, productivity development team and diseconomy of scale for a standard project sizes. The COnstructive COst MOdel (COCOMO) is employed to compute the software development effort. The percentage of change (increase or decrease) in software development effort, productivity and diseconomy of scale is employed as a measure of effectiveness for this study.  The results of this work demonstrate that each higher CMMI maturity level has a considerable impact in decreasing the development effort, increasing the productivity rate and reducing the diseconomy of scale. The results also indicate that the impact of CMMI-based maturity levels significantly increases with project sizes.

Keywords: CMMI, process maturity, COCOMO II, effort multipliers, scale factors, diseconomy of scale, and productivity rate.

Received February 19, 2010; accepted August 10, 2010

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Support System for Novice Researchers (SSNR): USABILITY Evaluation of the First Use

Maizatul Ismail1, Mashkuri Yaacob2, Sameem Abdul Kareem1, Fariza Nasaruddin1, and
1 Noorhidawati Abdullah
1Faculty of Computer Science and Information Technology, University of Malaya, Malaysia
2Office of the Vice Chancellor, University Tenaga Nasional, Malaysia
Abstract: Scholars make use of research output in the form of conference proceedings, journal and theses as references as guideline in generating new knowledge for the use of future generations. Support in the early stage of study is crucial for novice researchers as it will give them some insights of where to seek for extra information on relevant literature, institutions, people and research trend without having to go through tedious process of identifying this information all by themselves. The result of the implementation of SSNR shows significant information that can be utilized by novice researchers in accelerating research process. Thus, this paper will discuss on the evaluation of SSNR by novice researchers, in terms of its usability. The results are promising which indicated that SSNR work as it should.

Keywords: SSNR, novice researchers, usability, and human-machine system.

Received February 21, 2010; accepted August 10, 2010

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Building an Effective Rule-Based Light Stemmer for Arabic Language to Improve Search Effectiveness

Mohamad Ababneh1, Riyad Al-Shalabi2, Ghassan Kanaan2, and Alaa Al-Nobani1
1 Computer Information Systems Department, Al-Balqa Applied University, Jordan
2 Computer Information Systems Department, Arab Academy for Banking and Financial Science, Jordan
Abstract: Building an effective stemmer for Arabic language has been always a hot research topic in the IR field since Arabic language has a very different and difficult structure than other languages, that’s because it is a very rich language with complex morphology. Many linguistic and light stemmers have been developed for Arabic language but still there are many weakness and problems, in this paper we introduce a new light stemming technique and compare it with other used stemmers and show how it improves the search effectiveness.

Keywords: Stemmer, natural language, and light stemmers.

Received February 22, 2010; accepted May 20, 2010

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Verification of Cooperative Transient Fault Diagnosis and Recovery in Critical Embedded Systems

Zibouda Aliouat and Makhlouf Aliouat
University of Ferhat Abbes Faculty of Sciences, Computer Science Department Sétif 19000 Algeria
Abstract: The faults caused by ambient cosmic radiation are a growing threat to the dependability of advanced embedded computer systems. Maintaining availability and consistency in distributed applications is one of the fundamental attribute in building complex critical systems.  To achieve this, a key factor is the ability to detect the fault and handle it by means of recovery.  Such systems can use membership protocols designed to provide this function. The objective of membership protocol is to give all entities of every node in the cluster a consistent view of the system status, all within a pre-defined time. This paper describes a formal analysis of an extension of the group membership algorithm implemented in the time-triggered protocol. The proposed extension is to allow nodes reintegration after transient fault. We provide a detailed analysis of properties of formal model of the algorithm. The paper is intended to verify the safety and liveness properties that the protocol must satisfy. The correctness of the protocol is verified by the PVS theorem prover.

Keywords: Group membership protocol, formal verification, fault-tolerant distributed algorithm, and node reintegration.

Received February 25, 2010; accepted August 10, 2010

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Local Directional Pattern Variance (LDPv): A Robust Feature Descriptor for Facial Expression Recognition

Mohammad Kabir, Taskeed Jabid, and Oksam Chae
Department of Computer Engineering, Kyung Hee University, South Korea
Abstract: Automatic facial expression recognition is a challenging problem in computer vision, and has gained significant importance in the applications of human-computer interactions. The vital component of any successful expression recognition system is an effective facial representation from face images. In this paper, we have derived an appearance-based feature descriptor, the Local Directional Pattern Variance (LDPv), which characterizes both the texture and contrast information of facial components. The LDPv descriptor is a collection of LDP codes weighted by their corresponding variances. The feature dimension is then reduced by extracting the most discriminative elements of the representation with Principal Component Analysis (PCA). The recognition performance based on our LDPv descriptor has been evaluated using Cohn-Kanade expression database with a Support Vector Machine (SVM) classifier. The discriminative strength of LDPv representation is also assessed over a useful range of low resolution images. Experimental results with prototypic expressions show that the LDPv descriptor has achieved a higher recognition rate, as compared to other existing appearance-based feature descriptors.

Keywords: Facial expression recognition, feature descriptor, Local Directional Pattern (LDP), LDP variance (LDPv), Principal Component Analysis (PCA), and Support Vector Machine (SVM) classifier.

Received March 7, 2010; accepted May 20, 2010

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A Grid Enabled E-Theses and Dissertations Repository System

Lip Yee Por, Sim Ying Ong, Delina Beh, and Maizatul Ismail
Faculty of Computer Science and Information Technology, University of Malaya, Malaysia
Abstract: Some of the universities in Malaysia are still implementing Hybrid Electronic Theses and Dissertations (ETD) approach in managing Theses and Dissertations (TD). One of the limitations of the Hybrid ETD approach is its online cataloguing method, which is only available at the physical location of the TD instead of enabling the information to be retrieved online.  Maintaining the performance and the data accessing rate of an ETD system has become challenging, due in part to the high number of scholars who utilise and access the system. In order to allow remote access and maintain the services (such as scalability, accessibility, availability and expressibility), a Grid Enabled E-Theses and dissertations repository system (GREET) has been proposed in this paper to provide uniform access of knowledge integration among distributed heterogeneous platforms and repositories by using data grid technology. Comparative performance results between a non-grid architecture and GREET has been benchmarked. It has been proven that GREET is able to increase the processing time approximately three times faster than the non-grid architecture. Furthermore, multiple file streams can be opened to support larger volume and larger capacity of file operation so that GREET is able to decrease the chances of network congestion caused by input/output file operations. For future direction, research will be focused on searching algorithm using data mining or pattern discovery to minimise the respond time.

Keywords: Electronic theses and dissertations, theses and dissertations repository system, catalogue, and repository services.

Received March 8, 2010; accepted August 10, 2010

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