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Software Quality Prediction Model with the Aid of Advanced Neural Network with HCS

Procedia computer science 2016年 92卷 418-424页

作者： Kavita Sheoran Pradeep Tomar Rajesh Mishra Reader Department of Computer Science MSIT Delhi Assistant Professor in Department of Computer Science and Engineering School of Information and Communication Technology Gautam Bhudda University Greater Noida Assistant Professor School of ICT Gautam Buddha University Greater Noida Uttar Pardesh

Software quality is regarded as the highly important factors for assessing the global competitive position of any software product. To assure quality, and to assess the reliability of software products, many software quality prediction models have been proposed in the past decades. In this proposed method we have utilized a hybrid method for quality prediction. The prediction is done with the help of the Advanced Neural network which is incorporated with Hybrid Cuckoo search (HCS) optimization algorithm for better prediction accuracy. The application software is first subjected to test case generation and once the test cases are generated they are applied to advanced neural network for the prediction of quality. The neural network is improved by utilizing HCS which optimizes the weight factor for improving the prediction. The quality metrics like maintainability and reliability are estimated for predicting the software quality and the results are compared with other existing techniques to verify the effectiveness of our proposed method.

关键词： Software quality prediction Cuckoo search algorithm Neural Network Optimization Test cases

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Trusted and Reputed Services Using Enhanced Mutual Trusted and Reputed Access Control Algorithm in Cloud

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Procedia computer science 2016年 92卷 506-512页

作者： G. Sarojini A. Vijayakumar K. Selvamani Final ME Student Department of Information TechnologyJerusalem College of EngineeringChennai 600100 India Assistant Professor Department of Computer Science and EngineeringAnna UniversityChennai 600025 India

In cloud computing, trust management is more important when providing users with virtualized and scalable web services. Many existing systems sharply divide trust value into right or wrong. Trust means an act of faith; confidence and reliability in something that is expected to behave or deliver as promised. Trust is required to solve the problem created due to uncertainty and vulnerability caused by open conditions of cloud computing. This paper presents a mutual trust for both cloud users and cloud service providers to avoid security related issues in cloud computing. This is to be done by providing trusted services to the cloud users using enhanced mutual trust to be known as Enhanced Mutual Trusted Access Control Algorithm (EMTACA). This paper proposes the model which considers both the cloud user's behavior trust and cloud service provider's credibility. Trust calculations are based on the behavior which includes history of direct communication between both the user and service provider; based on friends and third party recommendations. The hierarchy based reputation systems has suggested how to safeguard the data objects in the level of file access which ensures the cloud security. The different security measures are used to protect the different cloud services. This proposed system which includes the EMTACA algorithm can assure enhanced, guaranteed and trusted and reputation based cloud services among the users in a cloud environment.

关键词： Cloud Computing Behavioral Trust Mutual Trusted Access Control Reputation System Cloud Security EMTACA.

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Fuzzy Logic Based Image Encryption for Confidential Data Transfer Using (2, 2) Secret Sharing Scheme

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Procedia computer science 2016年 78卷 632-639页

作者： Hinal M. Mudia Pallavi V. Chavan M-Tech Student (Computer Science & Engineering) Department of Computer Engineering BDCOE Sewagram-442102 WARDHA(M.S.) India Assistant Professor (Sr.Gr) Department of Computer Engineering BDCOE Sewagram-442102 WARDHA(M.S.) India

In this fast world of networking, secure image transmission of data whether the image having some information or some text, is one of the area focus for security. Here we had tried to implement the proposed technique fuzzy logic based image encryption for confidential data transfer using (2, 2) secret sharing scheme. Using fuzzy logic makes encryption and decryption complex. The (2, 2) secret sharing scheme provides secured and confidential authentication. For image encryption AES algorithm is used for encryption and decryption. The implementation of encryption algorithms can be very complex when we are focusing on rule based share generation.

关键词： Secret Sharing Visual Cryptography Fuzzy Logic Fuzzy Inference System Image Encryption Match Value Error Value.

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Optimization of heterogeneous Bin packing using adaptive genetic algorithm

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IOP Conference Series: Materials science and engineering 2017年第1期183卷

作者： R Sridhar M Chandrasekaran C Sriramya Tom Page Assistant Professor and Research Scholar Department of Mechanical Engineering Vels University Chennai TN India. Professor Department of Mechanical Engineering Vels University Chennai TN India. Assistant Professor Department of computer Science and Engineering Saveetha Engineering College Chennai TN India. Professor Lough borough University U.K.

This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of 'n' number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.

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Proposing Solution to XOR Problem Using Minimum Configuration MLP

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Procedia computer science 2016年 85卷 263-270页

作者： Vaibhav Kant Singh Assistant Professor Department of Computer Science & Engineering Institute ot Technology Guru Ghasidas Vishwavidyalaya Central University Bilaspur Chhattisgarh Pincode 495001 India

This paper is an extension to what the author had already done in [1] and [2] . In this paper we will see one solution to the XOR problem using minimum configuration MLP an ANN model. The proposed solution is proved mathematically in this paper. The problem of non-linear separability is addressed in the paper. The Architectural Graph representation of the proposed model is placed and also an equivalent Signal Flow Graph is represented to show how the proof the proposed solution. The non-linear Activation function used for the hidden layer minimum configuration MLP is Logistic function.

关键词： ANN XOR MLP

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Assimilating Text-Mining & Bio-Informatics Tools to Analyze Cellulase structures

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IOP Conference Series: Materials science and engineering 2017年第1期225卷

作者： K P N V Satyasree Dr B Lalitha Kumari Dr K S N V Jyotsna Devi S M Roy Choudri K Pratap Joshi Associate Professor Department of Computer Science & Engineering Vignan's Nirula Institute of Technology & Science for Women Palakaluru Guntur A.P. India Associate Professor scientist-grade-1 Srimaharshi Research Institute Of Vedic Technology Syamala Nagar Guntur A.P India Assistant Professor Department of Computer Science & Engineering Rajamahendri Institute of Engineering & Technology Bhoopalapatnam Rajahmundry A.P India Associate Professor Department of Computer Science & Engineering Usha Rama College of Engineering and Technology Telaprolu Vijayawada A.P India Associate Professor Department of Computer Science & Engineering Hindu College of Engineering and Technology Amaravathi Road Guntur A.P. India.

Text-mining is one of the best potential way of automatically extracting information from the huge biological literature. To exploit its prospective, the knowledge encrypted in the text should be converted to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. But text mining could be helpful for generating or validating predictions. Cellulases have abundant applications in various industries. Cellulose degrading enzymes are cellulases and the same producing bacteria – Bacillus subtilis & fungus Pseudomonas putida were isolated from top soil of Guntur Dt. A.P. India. Absolute cultures were conserved on potato dextrose agar medium for molecular studies. In this paper, we presented how well the text mining concepts can be used to analyze cellulase producing bacteria and fungi, their comparative structures are also studied with the aid of well-establised, high quality standard bioinformatic tools such as Bioedit, Swissport, Protparam, EMBOSSwin with which a complete data on Cellulases like structure, constituents of the enzyme has been obtained.

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An Effective Mechanism for Virtual Machine Placement using Aco in IAAS Cloud

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IOP Conference Series: Materials science and engineering 2017年第1期225卷

作者： Rajalakshmi Shenbaga Moorthy U. Fareentaj T.K. Divya Assistant Professor Department of Computer Science and Engineering St.Joseph's Institute of Technology Chennai India. Student Department of Computer Science and Engineering St.Joseph's Institute of Technology Chennai India.

Cloud computing provides an effective way to dynamically provide numerous resources to meet customer demands. A major challenging problem for cloud providers is designing efficient mechanisms for optimal virtual machine Placement (OVMP). Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. In order to provide appropriate resources to the clients an optimal virtual machine placement algorithm is proposed. Virtual machine placement is NP-Hard problem. Such NP-Hard problem can be solved using heuristic algorithm. In this paper, Ant Colony Optimization based virtual machine placement is proposed. Our proposed system focuses on minimizing the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment and the response time of each cloud provider is monitored periodically, in such a way to minimize delay in providing the resources to the users. The performance of the proposed algorithm is compared with greedy mechanism. The proposed algorithm is simulated in Eclipse IDE. The results clearly show that the proposed algorithm minimizes the cost, response time and also number of migrations.

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Big Data Analysis on Geographical Segmentations and Resource Constrained Scheduling of Production of Agricultural Commodities for Better Yield

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Procedia computer science 2016年 87卷 80-85页

作者： S. Senthilvadivu S. Vinu Kiran S. Prasanna Devi S. Manivannan Assistant Professor ArulmiguMeenakshi Amman College of Engineering Vadamanvandal Kanchipuram 604410 India Department of Computer Science & Engineering Apollo Engineering College Chennai 602105 India Deputy Dean Dr.MGR Educational & Research Institute University Chennai 600107 India

Agricultural geography is one of the subportion of human and our economic geography which examines the primary, secondary, tertiary and quaternary yield types activities that are carried out in agriculture. In this paper, we are examining the spatial distribution and concentration of crops and their yields along with their crop periods using Big data Analytics to find out the cropping patterns and combinations that varies in space and time. Our primary objective is to findout the crop associations and patterns under climatic influence for each geographical segmentation, to give better yeilds. The Big data analytic based association won’t to last as many of the farmers and scientists are rightly challenging over the agricultural sustainability. However, there is a strong possibility of the farmers to adopt a new combination in the coming decades as the Big data analytic based crop pattern decision facilitates the farmers, always try to optimize their agricultural re-turns and adopt new innovations which gives better yeilds since we are performing factual data centric analytics.

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Bigdata Analytics on Diabetic Retinopathy Study (DRS) on Real-time Data Set Identifying Survival Time and Length of Stay

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Procedia computer science 2016年 87卷 227-232页

作者： V. Sujatha S. Prasanna Devi S. Vinu Kiran S. Manivannan Phd Scholar MS University Assistant Professor Vellore Institute of TechnologyVellore Tamil Nadu 632014 India Department of Computer Science & Engineering Apollo Engineering College Chennai 602105 India Deputy Dean Dr.MGR Educational & Research Institute University Chennai 600107 India

In this paper, we had analyzed a large scale Diabetic data sets for several patients to find the length of time taken for treatment for each class of Diabetes and the risk of re-admission of diabetic patients performing Bigdata analytics, the type of diabetes and its outcome which acted as a high risk sample of patient data sets. We have collected and integrated different sources of diabetic information for several patients, from primary and secondary treatment information to administrative information, to analyze novel view of patient care processes such as type of treatments and every patient behaviors on which results multifaceted nature of chronic care that we take into our account to predict the survival factors and length of stay. Nowadays by using electronic medical equipments with high quality and high degree calibrations, we are able to gather large amounts of real-time diabetic data sets. The requires the usage of distributed platforms for making BigData analysis that results on making decisions based on available data and its trends. This type of Bigdata analysis allows geographical and environmental information of patients’ enables the capability of interpreting the ethnicity of data gathered and extract new analysis to identify survival options and treatment timelines (LOS) from them.

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Diabetic Retinopathy Detection Based on Eigenvalues of the Hessian Matrix

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Procedia computer science 2015年 47卷 311-318页

作者： S. Saranya Rubini A. Kunthavai Assistant Professor Department of Computer Science Engineering Coimbatore Institute of Technology Coimbatore India Assistant Professor (Senior Grade) Department of Computer Science Engineering Coimbatore Institute of Technology Coimbatore India

Diabetic Retinopathy (DR) is a medical condition caused by fluctuating insulin level in the blood which causes vision loss in case of severity. Timely treatment of such risks requires identification of the first clinical symptoms like microaneurysms (MAs) and hemorrhages (HMAs). The presence of those symptoms are visible in the digital color photographs of the retina and appear as round dark red spots in the image. In this paper, two approaches in the detection of MAs and HMAs are proposed. First, the semi automated approach applies semi automated hessian-based candidate selection algorithm (SHCS) followed by thresholding to detect true MAs and HMAs. The automated approach applies automated hessian-based candidate selection algorithm (AHCS) followed by feature extraction and SVM classification that uses twenty images for training manually annotated by medical domain experts. Implementations of both the approaches have been tested on real world images from retinal scan. From the results, the detection rate of automated algorithm when compared with that of the semi automated algorithm has been found to be significantly lesser with a probability p<0.005.

关键词： Diabetic retinopathy Microaneurysms Hemorrhages SVM SHCS AHCS

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