Latest Articles - Volume 10 No. 1

  • Comparative study on Classification of Digital Images

    H. B. Basanth Kumar

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    A great deal of progress has been made in both fields of computer vision and computer graphics and these two fields have now begun to converge very rapidly. However, this trend has brought with it new issues and challenges concerning the authenticity of digital images. The fact that digital images can now be easily created undermines the credibility of digital images presented as evidence in a court of law since now it is difficult to distinguish whether an introduced digital image is a depiction of real-life occurrences and objects or a synthetically generated one. This paper presents a comparative study of existing schemes used to classify natural images and computer graphic images.

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  • An Optimal Resource Provisioning Algorithm for Cloud Computing Environment

    Shivangi Nigam and Abhishek Bajpai

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    Resource Provisioning in a Cloud Computing Environment ensures flexible and dynamic access of the cloud resources to the end users. The Multi-Objective Decision Making approach considers assigning priorities to the decision alternatives in the environment. Each alternative represents a cloud resource defined in terms of various characteristics termed as decision criteria. The provisioning objectives refer to the heterogeneous requirements of the cloud users. This research study proposes a Resource Interest Score Evaluation Optimal Resource Provisioning (RISE-ORP) algorithm which uses Analytical Hierarchy Process (AHP) and Ant Colony Optimization (ACO) as a unified MOMD approach to design an optimal resource provisioning system. It uses AHP as a method to rank the cloud resources for provisioning. The ACO is used to examine the cloud resources for which resource traits best satisfy the provisioning. The performance of this approach is analyzed using CloudSim. The experimental results show that our approach offers improvement in the performance of previously used AHP approach for resource provisioning.

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  • An Automated Testing Model using Test Driven Development Approach

    C.P. Patidar1 and Arun Dev Dongre2

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    Today we live in the era of software and web applications. Software is used in every minor and major field. In defense, medical, education, research, government, administration and much other field software became a necessary part. Software also brings transparency in the systems. Software also makes people’s life easy and comfortable. Software testing is a very important part of any software development process. Software testing requires approximately forty percent budget of any software development process. Like in an automobile industry every vehicle is tested before it goes to the customer. Also in software testing it is must to test the software before deployment. Because if software deployed without testing then user will face the bug and user will be unhappy with the software. In this paper we compare manual and automated testing and proposed an automated testing model with test driven development (TDD).

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  • Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust

    Suruchi Chawla

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    The main challenge for effective web Information Retrieval(IR) is to infer the information need from user’s query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user’s information need.  Trust based web page recommendations retrieve search results according to the user’s information need.  In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user’s information need and trust is used for recommendation of web pages according to the user’s information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.

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  • Assessment of Accuracy Enhancement of Back Propagation Algorithm by Training the Model using Deep Learning

    Baby Kahkeshan and Syed Imtiyaz Hassan

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    Deep learning is a branch of machine learning which is recently gaining a lot of attention due to its efficiency in solving a number of AI problems. The aim of this research is to assess the accuracy enhancement by using deep learning in back propagation algorithm. For this purpose, two techniques has been used. In the first technique, simple back propagation algorithm is used and the designed model is tested for accuracy. In the second technique, the model is first trained using deep learning via deep belief nets to make it learn and improve its parameters values and then back propagation is used over it. The advantage of softmax function is used in both the methods. Both the methods have been tested over images of handwritten digits and accuracy is then calculated. It has been observed that there is a significant increase in the accuracy of the model if we apply deep learning for training purpose.

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