September 2018, No. 5
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Phishing Detection using RDF and Random Forests

Vamsee Kiran, Archanaa R, and Shriram Vasudevan

Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham University, India

Abstract: Phishing is one of the major threats in this internet era. Phishing is a smart process where a legitimate website is cloned and victims are lured to the fake website to provide their personal as well as confidential information, sometimes it proves to be costly. Though most of the websites will give a disclaimer warning to the users about phishing, users tend to neglect it. It is not a fully responsible action by the websites also and there is not much that the websites could really do about it. Since phishing has been in persistence for a long time, many approaches have been proposed in past that can detect phishing websites but very few or none of them detect the target websites for these phishing attacks, accurately. Our proposed method is novel and an extension to our previous work, where we identify phishing websites using a combined approach by constructing RDF models and using ensemble learning algorithms for the classification of websites. Our approach uses supervised learning techniques to train our system. This approach has a promising true positive rate of 98.8%, which is definitely appreciable. As we have used random forest classifier that can handle missing values in dataset, we were able to reduce the false positive rate of the system to an extent of 1.5%. As our system explores the strength of RDF and ensemble learning methods and both these approaches work hand in hand, a highly promising accuracy rate of 98.68% is achieved.

Keywords: Phishing, ensemble learning, RDF models, phishing target, metadata, vocabulary, random forests.


Received April 22, 2015; accepted


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Medical Image Segmentation Based on Fuzzy Controlled Level Set and Local Statistical Constraints

Mohamed Yaghmorasan Benzian1, 2 and Nacéra Benamrane2

1Computer Science Department, University Abou Bekr Belkaid of Tlemcen, Algeria

2Computer Science Department, University of Science and Technology Oran USTO-MB, Algeria

Abstract: Image Segmentation is one of the most important fields in artificial vision due to its complexity and the diversity of its application to different image cases. In this paper, a new ROI segmentation in medical images approach is proposed, based on modified level sets controlled by fuzzy rules and incorporating local statistical constraints (mean, variance) in level set evolution function, and low image resolution analysis by estimating statistical constraints and curvature of curve at low image scale. The image and curve at low resolution provide information on rough variation of respectively image intensity and curvature value. The weights of different constraints are controlled and adapted by fuzzy rules which regularize their influence. The objective of using low resolution image analysis is to avoid stopping the evolution of the level set curve at local maxima or minima of images. This method is tested on medical images. The obtained results of the technique presented are satisfying and give a good precision.

Keywords: Segmentation, level sets, medical images, image resolution, fuzzy rules, ROI.

Received April 8, 2015; accepted December 28, 2015


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Enhanced Hybrid Prediction Models for Time Series Prediction

Purwanto1 and Chikkannan Eswaran2

1Faculty of Computer Science, Dian Nuswantoro University, Indonesia

2Faculty of Computing and Informatics, Multimedia University, Malaysia

Abstract: Statistical techniques have disadvantages in handling the non-linear pattern. Soft computing (SC) techniques such as artificial neural networks are considered to be better for prediction of data with non-linear patterns. In the real-life, time-series data comprise complex pattern, and hence it may be difficult to obtain high prediction accuracy rates using the statistical or SC techniques individually. We propose two enhanced hybrid models for time series prediction. The first model is an enhanced hybrid model combining statistical and neural network techniques. Using this model, one can select the best statistical technique as well as the best configuration for the neural network for time series prediction. The second model is an enhanced adaptive neuro-fuzzy inference system which combines fuzzy inference system and neural network. The proposed enhanced ANFIS model can determine the optimum input lags for obtaining the best accuracy results. The prediction accuracies of the two proposed hybrid models are compared with those obtained with other models based on three time series data sets. The results indicate that the proposed hybrid models yield better accuracy results compared to ARIMA, exponential smoothing, moving average, weighted moving average and Neural Network models.

Keywords: Hybrid Model, Adaptive Neuro-Fuzzy Inference Systems, Soft Computing, Neural Network, Statistical Techniques.

Received March 25, 2015; accepted October 7, 2015


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Paradigma: A Distributed Framework for Parallel Programming

Sofien Gannouni, Ameur Touir, and Hassan Mathkour

College of Computer and Information Sciences, King Saud University, Saudi Arabia

Abstract: Recent advances in high-speed networks and the newfound ubiquity of powerful processors have revolutionized the nature of parallel computing. It is becoming increasingly attractive to perform parallel tasks on distant, autonomous, and heterogeneous networked machines. This paper presents a simple and efficient new distributed framework for parallel programming known as Paradigma. In this framework, parallel program development is simplified using the Gamma formalism, providing sequential programmers with a straightforward mechanism for solving large-scale problems in parallel. The programmer simply specifies the action to be performed on an atomic data element known as a molecule. The workers compete in simultaneously running the action specified on the various molecules extracted from the input until the entire dataset is processed. The proposed framework is dedicated for fine-grained parallel processing and supports both the Simple Program Multiple Data and Multiple Program Multiple Data programming models.

Keywords: Distributed Systems, Parallel Programming, Gamma Formalism, Single Program Multiple Data, Multiple Program Multiple Data.

 Received March 5, 2015; accepted March 9, 2016


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Maximum Spanning Tree based Redundancy

Elimination for Feature Selection of High

Dimensional  Data

Bharat Singh and OP Vyas

Department of Information Technology,Indian Institute of Information Technology, India

Abstract: Feature selection adheres to the phenomena of preprocessing step for High Dimensional data to obtain optimal results with reference of speed and time. It is a technique by which most prominent features can be selected from a set of features that are prone to contain redundant and relevant features. It also helps to lighten the burden on classification techniques, thus makes it faster and efficient.We introduce a novel two tiered architecture of feature selection that can able to filter relevant as well as redundant features. Our approach utilizes the peculiar advantage of identifying highly correlated nodes in a tree. More specifically, the reduced dataset comprises of these selected features. Finally, the reduced dataset is tested with various classification techniques to evaluate their performance. To prove its correctness we have used many basic algorithms of classification to highlight the benefits of our approach. In this journey of work we have used benchmark datasets to prove the worthiness of our approach.

Keywords: Data Mining, Feature Selection,  Tree based approaches, Maximum Spanning Tree, High dimensional Data.

 Received February 15, 2015; accepted December 21, 2015


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Multi-Classifier Model for Software Fault


Pradeep Singh1 and Shrish Verma2

1Department of Computer Science and Engineering, National Institute of Technology, Raipur

2Department of Electronics and Telecommunication Engineering, National Institute of Technology, Raipur

Abstract:  Prediction of fault prone module prior to testing is an emerging activity for software organizations to allocate targeted resource for development of reliable software. These software fault prediction depend on the quality of fault and related code extracted from previous versions of software.  This paper, presents a novel framework by combining multiple expert machine learning systems. The proposed multi-classifier model takes the benefits of best classifiers in deciding the faulty modules of software system with consensus prior to testing. An experimental comparison is performed with various outperformer classifiers in the area of fault prediction. We evaluate our approach on 16 public dataset from promise repository which consists of NASA MDP projects and Turkish software projects. The experimental result shows that our multi classifier approach which is the combination of SVM, Naive Bayes and Random forest machine significantly improves the performance of software fault prediction.

Keywords: Software Metrics, Software Fault prediction, Machine Learning.

Received February 7, 2015; accepted September 7, 2015


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Edge Preserving Image Segmentation using Spatially Constrained EM algorithm

Meena Ramasamy1 and  Shantha Ramapackiam2

1Electronics and Communication Engineering Department, P. S. R. Engineering College, India

2ECE Department, Mepco Schlenk Engineering College, India

Abstract: In this paper, a new method for edge preserving image segmentation based on the Gaussian Mixture Model is presented. The standard GMM considers each pixel as independent and does not incorporate the spatial relationship among the neighboring pixels. Hence segmentation is highly sensitive to noise. Traditional smoothing filters average the noise, but fail to preserve the edges. In the proposed method, a bilateral filter which employs two filters - domain filter and range filter, is applied to the image for edge preserving smoothing.  Secondly, in the Expectation Maximization algorithm used to estimate the parameters of GMM, the posterior probability is weighted with the Gaussian kernel to incorporate the spatial relationship among the neighboring pixels. Thirdly, as an outcome of the proposed method, edge detection is also done on images with noise. Experimental results obtained by applying the proposed method on synthetic images and simulated brain images demonstrate the improved robustness and effectiveness of the method.

Keywords: Gaussian Mixture Model, Expectation Maximization, bilateral filter, image segmentation.

Received December 23, 2014; accepted December 21, 2015


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Auto-Poietic Algorithm for Multiple Sequence Alignment

Amouda Venkatesan and Buvaneswari Shanmugham

Centre for Bioinformatics, Pondicherry University, India


Abstract: The concept of self-organization is applied to the operators and parameters of genetic algorithm to develop a novel Auto-poietic algorithm solving a biological problem, Multiple Sequence Alignment (MSA). The self-organizing crossover operator of the developed algorithm undergoes a swap and shuffle process to alter the genes of chromosomes in order to produce better combinations. Unlike Standard Genetic Algorithms (SGA), the mutation rate of auto-poietic algorithm is not fixed. The mutation rate varies cyclically based on the improvement of fitness value in turn, determines the termination point of algorithm. Automated assignment of various parameter values reduces the intervention and inappropriate settings of parameters from user without prior the knowledge of input. As an advantage, the proposed algorithm also circumvents the major issues in standard genetic algorithm, premature convergence and time requirements to optimize the parameters. Using BAliBASE reference multiple sequence alignments, the efficiency of the auto-poietic algorithm is analyzed. It is evident that the performance of auto-poietic algorithm is better than SGA and produces better alignments compared to other MSA tools.

Keywords: Auto-poietic, crossover, genetic algorithm, mutation, multiple sequence alignment, selection.

Received October 27, 2014; accepted November 29, 2015


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Temporal Tracking on Videos with Direction Detection

Shajeena Johnson1, Ramar Kadarkarai2

1Department of Computer Science and Engineering, James College of Engineering and Technology, India

2Einstein College of Engineering, India

Abstract: Tracking is essentially a matching problem. This paper proposes a tracking scheme for video objects on compressed domain. This method mainly focuses on locating the object region and predicting (evolving) the detection of movement, which improves tracking precision. Motion Vectors (MVs) are used for block matching. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of histogram matching. During the process of matching and evolving the direction of movement, similarities of target region are compared to ensure that there is no overlapping and tracking performed in a right way. Experiments using the proposed tracker on videos demonstrate that the method can reliably locate the object of interest effectively.

Keywords: Motion vector, distance measure, histogram, block matching, DCT, tracking.


Received August 19, 2014; accepted April 2, 2015


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A Network Performance Aware QoS Based

Workflow Scheduling for Grid Services

 Shinu John and Maluk Mohamed

 Department of CSE, MAM College of Engineering, India

Abstract: Grids enable sharing, selection and aggregation of geographically distributed resources among various organizations. They are now emerging as promising computing paradigms for resource and compute intensive scientific workflow applications modeled as a Directed Acyclic Graph (DAG) with intricate inter-task dependencies. Job scheduling is an important and challenging issue in a grid environment. There are various scheduling algorithm proposed for grid environments to distribute the load among processors and maximize resource utilization while reducing task execution time. Task execution time is not the only parameter to be improved; various QoS parameters are also to be considered in job scheduling in grid computing. In this Research we have studied the existing QoS based Task scheduling, work flow scheduling and formulated the problem. The possible solutions are developed for the problems identified in existing algorithms. The scheduling of dependent task (work flow) is more challenging than independent task scheduling. The scheduling of both dependent and independent tasks with satisfying QOS requirements of users is a very challenging issue in grid computing.  This paper proposes a Novel Network aware QoS workflow scheduling method for Grid Services. The proposed scheduling algorithm considers network and QoS constraints. The goal of the proposed scheduling algorithm is to implement the workflow schedule so that it reduces execution time and resource cost and yet meets the deadline imposed by the user. The experimental result shows that the proposed algorithm improves the success ratio of tasks and throughput of resources while reducing makespan and workflow execution cost.

Keywords: Grid Scheduling, QoS, DAG, Execution time, Deadline, Trust Rate.

Received June 25, 2014; accepted September 7, 2016

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