International Journal of Information Technology and Computer Science 

About: International Journal of Information Technology and Computer Science is an academic journal published by MECS Publisher. The journal publishes majorly in the area(s): Cloud computing & Computer science. It has an ISSN identifier of 2074-9007. It is also open access. Over the lifetime, 1068 publications have been published receiving 7564 citations. The journal is also known as: I.J. information technology and computer science.

Ensemble decision tree classifier for breast cancer data

Abstract: Data mining is the process of analyzing large quantities of data and summarizing it into useful information. In medical diagnoses the role of data mining approaches increasing rapidly. Particularly Classification algorithms are very helpful in classifying the data, which is important in decision making process for medical practitioners. Further to enhance the classifier accuracy various pre-processing techniques and ensemble techniques were developed. In this study a hybrid approach, CART classifier with feature selection and bagging technique has been considered to evaluate the performance in terms of accuracy and time for classification of various breast cancer datasets.

Image Classification using Support Vector Machine and Artificial Neural Network

Abstract: Image classification is one of classical problems of concern in image processing. There are various approaches for solving this problem. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. Firstly, we separate the image into many sub-images based on the features of images. Each sub-image is classified into the responsive class by an ANN. Finally, SVM has been compiled all the classify result of ANN. Our proposal classification model has brought together many ANN and one SVM. Let it denote ANN_SVM. ANN_SVM has been applied for Roman numerals recognition application and the precision rate is 86%. The experimental results show the feasibility of our proposal model.

An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment

Abstract: Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. Cloud computing represents supplement, consumption and delivery model for IT services that are based on internet on pay as per usage basis. The scheduling of the cloud services to the consumers by service providers influences the cost benefit of this computing paradigm. In such a scenario, Tasks should be scheduled efficiently such that the execution cost and time can be reduced. In this paper, we proposed a meta-heuristic based scheduling, which minimizes execution time and execution cost as well. An improved genetic algorithm is developed by merging two existing scheduling algorithms for scheduling tasks taking into consideration their computational complexity and computing capacity of processing elements. Experimental results show that, under the heavy loads, the proposed algorithm exhibits a good performance.

Real Time Efficient Scheduling Algorithm for Load Balancing in Fog Computing Environment

Abstract: Cloud computing is the new era technology, which is entirely dependent on the internet to maintain large applications, where data is shared over one platform to provide better services to clients belonging to a different organization. It ensures maximum utilization of computational resources by making availability of data, software and infrastructure with lower cost in a secure, reliable and flexible manner. Though cloud computing offers many advantages, but it suffers from certain limitation too, that during load balancing of data in cloud data centers the internet faces problems of network congestion, less bandwidth utilization, fault tolerance and security etc. To get rid out of this issue new computing model called Fog Computing is introduced which easily transfer sensitive data without delaying to distributed devices. Fog is similar to the cloud only difference lies in the fact that it is located more close to end users to process and give response to the client in less time. Secondly, it is beneficial to the real time streaming applications, sensor networks, Internet of things which need high speed and reliable internet connectivity. Our proposed architecture introduced a new scheduling policy for load balancing in Fog Computing environment, which complete real tasks within deadline, increase throughput and network utilization, maintaining data consistency with less complexity to meet the present day demand of end users.

Artificial Neural Networks for Diagnosis of Kidney Stones Disease

Abstract: Artificial Neural networks are often used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. They have several advantages over parametric classifiers such as discriminate analysis. The objective of this paper is to diagnose kidney stone disease by using three different neural network algorithms which have different architecture and characteristics. The aim of this work is to compare the performance of all three neural networks on the basis of its accuracy, time taken to build model, and training data set size. We will use Learning vector quantization (LVQ), two layers feed forward perceptron trained with back propagation training algorithm and Radial basis function (RBF) networks for diagnosis of kidney stone disease. In this work we used Waikato Environment for Knowledge Analysis (WEKA) version 3.7.5 as simulation tool which is an open source tool. The data set we used for diagnosis is real world data with 1000 instances and 8 attributes. In the end part we check the performance comparison of different algorithms to propose the best algorithm for kidney stone diagnosis. So this will helps in early identification of kidney stone in patients and reduces the diagnosis time.