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

The biggest challenge for knowledge management is how to make sure that right knowledge could be available to the right people on the right time for any purposes. Traditional search engines return relevant Web documents to keyword queries, where users have to read tedious documents to find the exact information they need. In collaborative environment where people work together with others to achieve a common goal, knowledge sharing plays a very important role. With the help of knowledge sharing one can get faster access to the information. But to find a person who can give the right knowledge is a challenging part due to variety of information needs. This proposed system finds an expert who provides a most relevant knowledge acquired by them to the user in collaborative environment. When the knowledge mining is integrated with expert search, the accuracy improves as compared to traditional methods. This knowledge sharing system helps to get the exact knowledge in less time and to save a lot of repeating efforts for the same task. Thus, it increases the performance of the system by using efficient techniques.

School of Computer Engineering & Technology

Engineering and Technology

International Journal of Engineering Trends and Technology (IJETT)

Expert Finding in Web Based Collaborative Knowledge Sharing Environment

'Live Energy-Live Life' is the green solution GS using crowd computing for the survival in future. Crowd computing is one of the upcoming research areas allowing the people around the world to work together in assistance with the machines and contribute for the best feasible solution. In near future, the rapid industrial revolution (IR) will extinct our lives; making the machines are bosses. The ever-increasing volume of data and its advance techniques, if not controlled and handled properly will result in the increase of energy consumption (EC) and e-wastes. The data centers are the main contributors for EC and thus, the environment is at risk due to the continuous emission of harmful carbon products. The proposed approach describes the importance of a new emerging technology known as crowd computing. It is an amalgamation of software and social interactions which provide cost saving benefits and ease of use for the ultimate user. The power of crowd computing dwells in utilizing humans' perception, and their innovation to solve non-Algorithmic problems. Here, a team of participants proactively work together to solve the real life problems that are tough to be resolved either by the individuals or computers alone. Crowd computing is a mass phenomena of Computer Assisted Cooperative Activity (CACA) community. The analysis of various fields of crowd computing has contributed to approximately 40-50% of energy savings and power savings, making it a crucial factor for the better survival of future-'GREEN SOLUTION (GS): A new initiative for Energy Efficient Computing where Humans and Machines work together.' It is observed that GS is completely dependent on the rise in volume of energy savings (ES) and power savings (PS), fulfilling the future demands for a period of nearly four decades. © 2015 IEEE.

World University root node

2015 International Conference on Energy Systems and Applications

GREEN SOLUTION (GS): A new initiative for Energy Efficient Computing where Humans and Machines work together

Our planet is blessed with various species of fauna and flora. It’s well known that plants play a crucial role in preserving the earth’s ecology and environment by maintaining a healthy atmosphere and providing sustenance and shelter to innumerable insect and animal species. Plants are also important for their medicinal properties and as alternative energy sources like bio-fuel. Today as many types of plants are at the brink of extinction. In our day to day life herbal plays vital role on human physic maintenance. Plant classification has a broad application perspective in agriculture medicine and is especially significant to the biology diversity research. In recent time’s computer vision have been successfully applied towards automated systems of plant cataloguing. The proposed system tries to bring atomization in the process of plant leaf classification such that without any precious knowledge of the leaf species, we are trying to detect the leaf. This wi ll help the botanists in their study and speed up the process of identifying the species of plant. India is enriching source of plant species which is the base of ‘Ayurveda’ so the classification and identification of various plant species is one of the important phase. Our idea is to develop an automated tool which would detect and classify the plant leaf species after comparing with the trained sets. These trained sets are used by Artificial Neural Network (ANN) after image processing. The Neural Network would be trained for detection of edge and vein analysis. Earlier atomization was difficult, but now due to drastic development in technological field it is possible in just few steps. This paper proposes the automated tool for plant leaf recognition of leaf its digital image. Manual identification requires prior knowledge of species and is a lengthy process, thus the atomization technique helps t

International Journal Of Engineering And Computer Science

Automated Tool for Plant Leaf Classification Using Morphological Features

Since last two decades, researchers and practitioners came up with a variety of software tools to automate the testing process. The present paper discusses the proposed work/technique, which can be used to develop a tool to automate test case generation process. It proposes to build an Automation tool that records and replays the use cases, thus providing a simple application for a layman (Non-Technical Person) who is an internal/external consumer. The protocol used for recording is HTTPS, so the encryption is performed while recording the calls. Server/Mid-tier calls will be recorded in .java file which is compiled and executed using command line interface and a Graphical User Interface (GUI). When there is repetition of same scenario, the tool prevents redundancy of use cases. If there is a change in format of input or response, Auto parameterization of input is performed

International Journal of Advance Research in Computer Science and Management Studies

Automation Tool for a Layman to Test Remedy Applications

Data clustering is considered as one of the most promising data analysis methods in data mining and on the other side K-Means is the well known partitional clustering technique. Nevertheless, K-Means and other partitional clustering techniques struggle with some challenges where dimension is the core concern. The different challenges associated with clustering techniques are preknowledge of initial centers of clusters, problem of stagnation, multiple cluster membership problem, dead unit problem, and slow or premature convergence to local search space. So as to resolve these clustering limitations, an eminent choice is to adapt the Swarm Intelligence (SI) inspired optimization algorithms. This paper presents an overview of the research on an applicability of different Particle Swarm Optimization (PSO) variants for clustering multidimensional data along with the basic concepts of PSO as well as data clustering. It also puts forward an idea of new and advance PSO variant in order to deal with multidimensional data clustering.

International Journal of Computer Applications

PSO based Multidimensional Data Clustering: A Survey

CoRR

Clustering Multidimensional Data with PSO based Algorithm

This paper proposes an advanced PSO variant using Subtractive Clustering methodology for data clustering. The implementation of this algorithm will be used to provide fast, efficient and appropriate solution for any complex clustering problem. This algorithm addresses the basic challenges faced with the existing PSO based clustering techniques i.e. preknowledge of initial cluster centers, dead unit problem, premature convergence to local optima, stagnation problem, etc. The proposed algorithm proved that the use of Subtractive Clustering methodology at the start of any PSO approach can improve the clustering process by suggesting good initial cluster centers and number of clusters in advance and then fasten the further clustering with the use of adaptive inertia weight factor and boundary restriction strategy. The performance of proposed algorithm is tested against well know clustering techniques over three datasets, where the results showed a better or comparable performance with respect to accuracy of clustering and convergence rate. © 2014 IEEE.

2014 Annual IEEE India Conference (INDICON)

Data clustering using an advanced PSO variant

School of Civil Engineering

Novel Method for Graphical Passwords using CAPTCHA

A Review on Clustering Web Data using PSO

An Overview of the Research on Texture Based Plant Leaf Classification

A vision-based driver assistance system using image processing technology consists of three main modules: road detection, traffic sign detection/identification, obstacle detection. Traffic Sign Recognition is an upcoming research field under applied Computer Vision and Machine Learning which deals with the automatic detection and classification of traffic signs in traffic scene images. In the current paper, we discuss the methodology used to detect the road sign from a traffic sign scene image and present a technique that can be used to construct a system that recognizes road signs in images. Such a system will ensure that each driver is aware of the rules &amp; hazards on the road &amp; will thus help in improving road safety. Also, the location of the road sign in the traffic sign image is unknown. Once these obstacles are overcome, such a system could be integrated in a Driver Assistance System. The primary objective is to develop an algorithm, which will identify various types of road signs from streaming video of real time road scenes in a reasonable time frame. The algorithm has two main parts: the first one, detection which uses the color and shape information to detect a road sign in a traffic scene picture. The second one, prediction &amp; classification based on Neural Network concept. Our algorithm proposes a variety of MATLAB Image Processing Toolbox commands to determine if a road sign is present in current image. If present, the sign is resized and certain features from the Region of Interest (RoI) are fed as a feature vector to a neural net. This trained Neural Classifier then predicts the class of the road sign. Genetic Algorithms (GA) with built-in intelligence could be incorporated into the current system which will try to look for traffic signs only in environments where there is probability of finding/ detecting one. Copyright 2012 ACM.

ACM International Conference Proceeding Series

Neural network based smart vision system for driver assistance in extracting traffic signposts

GPGPU Processing in CUDA Architecture

Today’s world requires maximum computing speed. The progress that the CPU has achieved over the past 2 decades, though tremendous, has now reached a point of stagnation. To overcome this, a new highly parallel and multithreading processor optimized for high degree of computation was introduced, which was named as the Graphics Processing Unit (GPU) by NVIDIA or the Visual Processing Unit (VPU). A Graphics Processing Unit (GPU) is a single-chip processor primarily used to manage and boost the performance of video and graphics. This paper talks about the reasons for choosing GPU to accelerate the computation. This paper also states where GPU will work more efficiently than the CPU