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Universal Journal of Computer Science UniCSE
Universal Journal of Computer Science UniCSE's
An Invisible Communication for Secret Sharing against Transmission Error
The electronic and information revolutions have brought a plethora of sophistications to the today’s world. Computer, one of the versatile inventions of human, always has more to offer to the benefit of the planet. The electronic substitutions to the five senses of humans have unveiled many unknown possibilities of harnessing the power of computers. The security of information handled in real time transmission and reception like internet is of paramount consideration, as this information may be confidential. This paper proposes a novel solution for handling of confidential information in real time systems, using a modern steganographic approach instead of conventional cryptographic methods. The proposed solution brings down the required channel capacity to transfer secret data in real time systems besides improving security.
A New Strategy for Gene Expression Programming and Its Applications in Function Mining
Population diversity is one of the most important factors that influence the convergence speed and evolution efficiency of gene expression programming (GEP) algorithm. In this paper, the population diversity strategy of GEP (GEP-PDS) is presented, inheriting the advantage of superior population producing strategy and various population strategy, to increase population average fitness and decrease generations, to make the population maintain diversification throughout the evolutionary process and avoid “premature” and to ensure the convergence ability and evolution efficiency. The simulation experiments show that GEP-PDS can increase the population average fitness by 10% in function mining, and decrease the generations for convergence to the optimal solution by 30% or more compared with other improved GEP.
An Energy Aware WSN Geographic Routing Protocol
Abstract- Wireless Sensor Networks (WSNs) consist of small nodes with sensing, computation, and wireless communications capabilities. Many routing, power management, and data dissemination protocols have been specially designed for WSNs. The focus has been given to the routing protocols which might differ depending on the application and network architecture. In this paper, we propose an energy efficient data forwarding protocol called Energy Aware Geographic Routing Protocol (EAGRP) for wireless sensor networks to extend the life time of the network. In EAGRP, both position information and energy are available at nodes used to route packets from sources to destination. This will prolong the lifetime of the sensor nodes; hence the network life time and thus get higher packet delivery ratio and minimal compromise of energy efficiency. The proposed protocol is an efficient and energy conservative routing technique for multi-hop wireless sensor networks. The routing design of EAGRP is based on two parameters: location and energy levels of nodes. Each node knows the location and energy level of its neighbors. The performance measures have been analyzed with variable number of nodes. Our simulation results indicate that the proposed algorithm gives better performance in terms of higher packet delivery ratio, delay, and energy consumption.
Adaptive System Simulation and Noise Analysis Toolbox (ASSNAT) : The Open-Source Toolbox Developed with Newer Features for Adaptive System Simulation
Abstract- This paper introduces Adaptive System Simulation and Noise Analysis Toolbox (ASSNAT) version 1.1 (v1.1), which is an open- source MATLAB based software package for simulation and analysis of Adaptive signal processing systems and noises, with its new feature Learning curve method, where we can make an advanced level comparative study based analysis. ASSNAT v1.1 contains a variety of adaptive systems, filter algorithm and a wide range of input signals for simulation in a user friendly graphical interface. Central and advanced features, underlying models and algorithms, and case studies are presented in this paper to demonstrate the capabilities of this toolbox and its suitability for educational and research purposes.
Making secure Semantic Web
this paper first describes ways of semantic web security implementation through layers. These layers are presented as a backbone for semantic web architecture and are represented in XML security, RDF security and in an idea of semantic web security standardization.
A Dedicated Web-Based Learning System
The fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are full of open source and commercial products including Blackboard, WEB CT, and Moodle. These systems are tutor-oriented, not designed to facilitate personalized learning support for an individual learner. Professors and students, frustrated with current LMS, need a new, innovative, user-friendly alternative to encourage and empower students to take control of their education, and teachers to explore new styles of teaching, depending on their students’ needs. Most of the students in the developing world, specially in the middle east region, suffer from limited English proficiency, in addition to limited computer skills. These obstacles compose a barrier to, and impose limitations on the design and implementation of E-learning systems. This paper introduces a prototype for a simple, dedicated, learner-oriented e-learning system to facilitate the learning process. The proposed system enables the student (even with moderate English level and general IT knowledge) to wander through the system, and register for a specific course to make use of the scientific material available there in. In addition, the design of a computer engineering course conforming SCORM standards is introduced. The proposed prototype is made available for students to examine and evaluate. Feedbacks will be analyzed and enhancements will be proposed.
Geometric Transformation Technique for Total Hip Implant in Digital Medical Images
The use of geometric transformation is extremely crucial in the medical field because it can assist surgeons in carrying out pre-surgery process effectively and properly. This study aims to produce techniques and algorithms that can be used to implement the implant transformation process such as rotation and reflection on medical images. The main objective of this paper is to show the hip joint implant transformation algorithm used in x-ray images of hip joint patients. The computerised hip joint replacement process developed by a group of researchers from the Industrial Computing Research Group, Faculty of Technology and Information Science, Universiti Kebangsaan Malaysia shows how the implant transformation process being perform. The code of two transformation algorithms (rotation and reflection transformation) were shown in this paper. The example showed that by using the suggested transformation, the position of the hip joint implant can be manipulated to obtain the optimal position on the x-ray images of patients.
Cancer Diagnosis Using Modified Fuzzy Network
In this study, a modified fuzzy c-means radial basis functions network is proposed. The main purposes of the suggested model are to diagnose the cancer diseases by using fuzzy rules with relatively small number of linguistic labels, reduce the similarity of the membership functions and preserve the meaning of the linguistic labels. The modified model is implemented and compared with adaptive neuro-fuzzy inference system (ANFIS). The both models are applied on
A New Software Data-Flow Testing Approach via Ant Colony Algorithms
Search-based optimization techniques (e.g., hill climbing, simulated annealing, and genetic algorithms) have been applied to a wide variety of software engineering activities including cost estimation, next release problem, and test generation. Several search based test generation techniques have been developed. These techniques had focused on finding suites of test data to satisfy a number of control-flow or data-flow testing criteria. Genetic algorithms have been the most widely employed search-based optimization technique in software testing issues. Recently, there are many novel search-based optimization techniques have been developed such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Immune System (AIS), and Bees Colony Optimization. ACO and AIS have been employed only in the area of control-flow testing of the programs. This paper aims at employing the ACO algorithms in the issue of software data-flow testing. The paper presents an ant colony optimization based approach for generating set of optimal paths to cover all definition-use associations (du-pairs) in the program under test. Then, this approach uses the ant colony optimization to generate suite of test-data for satisfying the generated set of paths. In addition, the paper introduces a case study to illustrate our approach.
Effective Method for Extracting Rules from Fuzzy Decision Trees based on Ambiguity and Classifiability
Crisp Decision trees (CDT) algorithms have been the most widely employed methodologies for symbolic knowledge acquisition. There are many methodologies have been presented to address the problems of the continuous data, multi-valued data, missing data, uncertainty data and noisy features. Recently, due to the widespread use of the fuzzy representation, a lot of researchers have utilized the fuzzy representation in decision trees to overcome the preceding problems. Fuzzy decision trees (FDT) are generalization for the CDT. FDTs are built by using fuzzy or crisp attributes and classes which often need pruning to reduce their size. FDTs have been successfully used to extract knowledge in uncertain classification problems. In this paper, we present a technique to build FDT by employing the ambiguity of attributes and classifiability of instance. Our technique builds a reduced FDT which does not need for applying the pruning algorithms to reduce the size. The paper also presents the results of a set of empirical studies conducted on a dataset of UCI Repository of Machine Learning Database that evaluate the effectiveness of our technique compared to Fussy Iterative Dichotomiser 3 (FID3), ambiguity, and FID3 with classifiability techniques. The studies show the effective of our technique in reducing the number of the extracted rules without loosing of the rules accuracy.
Automatic Model Based Methods to Improve Test Effectiveness
Software testing covers a large percent of the software development expenses. However, formal methods are applied, usually, to improve or ensure the correctness of the requirements, design, code, or testing. In order to utilize formal methods particularized to different cases, the subject matter needs to be written in a formal language or syntax. In this research, several model based methods are investigated and experimented in order to reduce testing expenses, improve test coverage, and the effectiveness of the testing process. Formal models are generated from the application during runtime. For this purpose a tool is developed to automatically derive the formal syntax from the application at runtime. Later on, the formal model is used in improving test effectiveness. In addition, the model is used to find some possible dynamic problems in the application that might be hard to be discovered by traditional testing methods. Finally, a test monkey tool is proposed in order to test the application for deadlock or progress problems and test the application ability to reject invalid test cases as well.
Model Transformations in Model Driven Architecture
Transformation is one of the prominent features and the rising research area of Model Driven Architecture since last few years. There are many techniques which have been proposed as a Request for Proposal (RFP) in Query, View and Transformation (QVT). In this paper we have conducted a survey on transformation techniques. The surveyed techniques include pattern based approaches, transformation languages, transformation rules, Metamodel based approaches etc. This work has summarize, categorized and identified different analysis parameters of these techniques. On the basis of identified parameters we have presented an analysis matrix to describe the strength of different approaches. The major focus of the work is on model to model transformation techniques i.e. from PIM to PSM transformation.
Detection of Cardiac Infarction in MRI C-SENC Images
Composite Strain Encoding (C-SENC) is an Magnetic Resonance Imaging (MRI) technique for acquiring simultaneous viability and functional and images of the heart. It combines two imaging techniques, Delayed Enhancement (DE) and Strain Encoding (SENC). In this work, a novel multi-stage method is proposed to identify ventricular infarction in the functional and viability images provided by C-SENC MRI. The proposed method is based on sequential application of Otsu’s thresholding, morphological opening, square boundary tracing and the subtractive clustering algorithm. This method is tested on images of ten patients with and without myocardial infarction (MI). The resulting clustered images are compared with those marked up by expert cardiologists who assisted in validating results coming from the proposed method. Infarcted tissues are correctly identified using the proposed method with high levels of sensitivity and specificity.
Distributing Arabic Handwriting Recognition System Based on the Combination of Grid Meta-Scheduling and P2P Technologies (Omnivore)
Character recognition is one of the oldest fields of research. It is the art of automating both the process of reading and keyboard input of text in documents. A major part of information in documents is in the form of alphanumeric text. Significant movement has been made in handwriting recognition technology over the last few years. Up until now, Arabic handwriting recognition systems have been limited to small and medium size of documents to recognize. The facility of dealing with large database (large scale), however, opens up many more applications. Our idea consists to use a strong and complimentary approach which needs enough computing power. We have used a distributed Arabic handwriting system based on the combination of Grid meta-scheduling and Peer–to-Peer (P2P) technologies such as Omnivore. Obtained results confirm that our approach present a very interesting framework to speed up the Arabic optical character recognition process and to integrate (combine) strong complementary approaches which can lead to the implementation of powerful handwriting OCR systems .
Reference Point Based Multi-Objective Optimization Using Hybrid Artificial Immune System
Abstract—during the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI).There has been increasing interest in the development of computational models inspired by several immunological principles. Although there are advantages of knowing the range of each objective for Pareto-optimality and the shape of the Pareto-optimal frontier itself in a problem for an adequate decision-making, the task of choosing a single preferred Pareto optimal solution is also an important task. In this paper, a Reference Point Based Multi-Objective Optimization Using hybrid Artificial intelligent approach based on the clonal selection principle of Artificial Immune System (AIS) and Neural Networks is proposed. And, instead of one solution, a preferred set of solutions near the reference points can be found. Modified Multi-objective Immune System Algorithm (MISA) is proposed with real parameters value not binary coded parameters, uniform and non uniform mutation operator is applied to the clones produced. Real parameter MISA works on continuous search space.
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