MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Computer Engineering & Technology

Energy consumption affects Wireless Sensor Networks (WSN) lifetime and may cause network degradation. Communication distance between the sensor nodes and base station affects network energy. The more communication distance between two nodes the more energy consumed. Clustering technique is used to reduce the energy consumption in the wireless data transmission. To develop an energy-efficient system using clustering, there are three requirements. First, to determine the optimum number of sensor nodes in a specific cluster, Second, to find the optimal number of clusters in the network and the third is to determine the correct position of Cluster Head (CH) in a particular cluster. Selecting an optimal number of clusters in WSNs gives greater improvement in system scalability, increases energy efficiency, and enhances network lifetime, in the network. For a large WSN lifetime of network depends on the cluster diameter and number of hops. The energy cost of network is directly proportional to the number of clusters. Clustering technique balances the load distribution in network and reduces traffic transmission. © 2016 ACM.

World University root node

Engineering and Technology

ACM International Conference Proceeding Series

Comparative study of optimal clustering techniques in wireless sensor network: A survey

The TCP protocol is used by the majority of the network applications on the Internet. TCP performance is strongly influenced by its congestion control algorithms that limit the amount of transmitted traffic based on the estimated network capacity and utilization. In this work TCP Tahoe, TCP Reno, TCP New-Reno, TCPOM are considered. Also discussed how TCP reacts to the congestions and depending on the algorithm how the congestion window is minimized and how the packets are retransmitted. Simulation has been done with WAN type network analyzing the throughput, delay, packet delivery ratio, number of packet sent, number of packet dropped, number of packet received and performance of these TCP variants in order to discover which of them has a better performance. After analyzing with several TCP variant it was found that TCPOM exhibits a better performance with respect to throughput, delay, packet delivery ratio than the other TCP variants considered in this paper. TCPOM algorithm is explained in this paper and is compared with other TCP Variants algorithm like TCP Tahoe, TCP New-Reno, TCP-Reno and graph are plotted with respect to throughput, delay and packet delivery ratio. Simulation is done in NS3 environment [1] [8].

International Journal of Appiled Engineering and Research

Modified Congestion Control Algorithm TCP

This paper proposes an innovative idea for segmenting a video sequence including vigorous textures in transform domain. The advanced mode is based on the contourlet transform. This transform method agreed to re-evaluate spatiotemporal structures for a given level and inclination. All advances examine and compare multi-scale dissolution to recognize dynamic texture in image sequences and then intend a simple efficient approach to dynamic texture segmentation using contourlet transform with measures to find the vibratory attribute. So it is competent to specify the traffic of an image sequence by calculating the parameters. The observation that, for objects that generally travel, it is normally easier to see them when they are moving than when they are stationary. However when there will be more traffic crowd, it is difficult to find the task features. The segmentation proficiency is well applied on video sequence of dynamic texture and also traffic classification is done by measuring the parameter. Future prospects are finally discussed. © 2015 IEEE.

International Conference on Emerging Trends in Engineering and Technology, ICETET

Traffic Video Surveillance Using Contourlet Transform

Cloud Computing is one of the large and essential environment now a days to work for the storage collection and privacy preserve to that data. Cloud data security is most important and major concern for the client while use of the cloud services provided by the different service providers. There can be some major security concern and conflicts between the client and the service provider. To get out from those issues, a third party auditor uses as an auditor for assurance of data in the environment. Storage systems for the cloud has many fundamental challenges still today. All basic as well critical challenges among which storage space and security is generally the top concern in the cloud environment. To give the appropriate security issues we have proposed third party authentication system. The cloud not only for the simplified data storage but also secure data acquisition in cloud environment. At last we have perform different security analysis as well performance analysis. It give the results that proposed scheme has significant increases in efficiency for maintaining highly secure data storage and acquisition. The proposed method also helps to minimize the cost in environment and also increases communication efficiency in the cloud environment. © 2015 IEEE.

Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015

Cost and Communication Efficient Auditing over Public Cloud

In this paper, we propose a method for detecting moving objects in a video stream. Initially four existing methods, Basic Background Subtraction (BSS), Frame Differencing (FD), Adaptive Background Subtraction (ABS) and Background Subtraction Frame Differencing (BSFD) are evaluated for low resolution action recognition videos. Focus of this paper is on low resolution videos as most of the time CCTV recordings of the public areas are of low resolution. Recognizing an action performed by a human being in such a video is a challenging task. Videos recorded with various view angles are used for testing to evaluate view invariance property of algorithms. Concept of three frame differencing method is used along with adaptive background in an Adaptive Background subtraction and Frame Differencing (ABSFD) method proposed in this paper. A dynamic background image is used to detect the moving object. Background is updated with small constant value to accommodate minute changes in background. The proposed algorithm gives consistent results for videos recorded at different view angles. The experimental results show that this algorithm can detect moving objects successfully with precision above 95% in most of the test cases. © 2016 Institution of Engineering and Technology. All rights reserved.

IET Conference Publications

Evaluation of moving object detection techniques for low resolution videos

Non verbal approaches of emotion detection plays vital role in various applications like E-learning, automatic pain monitoring, driver alert system, cognitive assessment etc. which are developed to enhance quality of human life. Facial expressions based approach is one of the very effective approaches which is used widely on standalone basis or combined with other approaches known as multimodal techniques of emotion detection. The paper discusses facial expressions based emotion detection which uses patch based face features and SVM Classifier. The experimentation carried out on JAFFE database provides 90.65 % average accuracy for emotion detection for basic emotions happy, anger, sad, surprise, disgust, fear and neutral. The other performance parameters through experimentation are obtained as Average True positive rate is 90.5942 %, average false positive rate is 9.3671 % and average false negative rate is 9.4 %. The average feature extraction time is 18.49 s and emotion detection time through these extracted features is 1.1 s.

Studies in Computational Intelligence

Non verbal approach for emotion detection

Classification is important task of data mining used to extract knowledge from huge volume of data. By nature, classification is multi-objective problem, as it required optimization of multiple objectives simultaneously like accuracy, sensitivity, squared error, precision etc. Traditionally, evolutionary algorithms were used to solve multi-objective classification problem by considering it as single-objective problem, but this approach gives single solution to problem. Therefore, multi-objective evolutionary algorithms are used to solve classification problem. In this paper, we have used Pareto approach to optimize neural network to solve classification problem. Non-dominated sorting genetic algorithm is used to simultaneously optimize accuracy and mean squared error objectives of neural network along with local search. As slow convergence to optimal solutions is major disadvantage of evolutionary algorithm. To speed up convergence to optimal solutions hybrid technique is adopted by augmenting evolutionary technique with local search algorithm. This proposed approach gives set of Pareto optimal solutions which represent different solutions for given classification problem. © 2016 IEEE.

International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016

Classification problem solving using multi-objective optimization approach and local search

Today central topic in science and engineering is parallel and distributed computing, research performing in the area of development of new approaches for the modeling, design, analysis, evaluation, and programming of future parallel and distributed computing systems and its applications. To detect the presence of dependency between tasks, analytic methodology is required. This review theoretically analyzes what is dependency, different types of dependencies, various applications which require dependency analysis and different approaches towards dependency analysis. This research classifies dependency analysis approaches on the basis of input source; for different input source different methodology is available, this review considers an input source as a component base system with dependent components. This research gives review of dependency analysis solutions for various areas like parallel processing, high performance computing, security and vulnerability in software's where various approaches are used for dependency analysis. This review concludes that there are various methods available for dependency analysis. Most of the methods use graph base approach, conceptual graph approach and matrix based approach, out of which this review puts forward the best possible approach. This research proposes a new approach for dependency analysis which results in the reduced dependent analysis time, low memory consumption and less cost sensitive. © 2016 The Authors.

Fulltext

Physics Procedia

Dependency Analysis for Secured Code Level Parallelization

Physician use medical images to find abnormalities in human bodies and to locate the discontinuities. In transmission process sometimes medical images are corrupted due to external noise. Therefore need to improve the image quality, put down the computational complexity and signal to noise ratio. There is need of segmentation to improve performance analysis and image quality. Segmentation method is use to identify important regions in medical images, it is a unique technique for partitioning image into meaningful sub regions or object with same attribute. Proposed method state Principle component analysis and k-means clustering method for segmentation of medical images and extracts meaningful part from medical image in efficient manner. K-means Clustering is the process of extracting meaningful part from image. Adding Principle Component Analysis for feature extraction and formation of precise number of cluster to increase the accuracy. To develop a system which will perform segmentation of MRI images to locate disorder in better way. © 2015 IEEE.