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

Image Retrieval systems highly rely on the image signatures stored in database. Constructing Image signatures with optimal size and accurate representation of image is most interesting challenge here. High level image features like object and their characteristic are useful in sentiment image analysis but faces the limitations of domain specific feature vector. Low level image features like color, texture and shape are interesting and useful enough to represent the image in diverse image databases. Targeting to low level image feature: color, the image signatures created are of huge size, as those represents three color planes and their values. Image signatures vary, as the image size varies. Targeting towards creating the optimal image signatures with color feature, to reduce the size of feature vector is possible by considering images in frequency domain. Image transforms converts image in frequency domain, with compressed image data. This data is further reduced form by ignoring the low energy components. This paper discusses the approach to construct image signatures by considering high energy components of transformed image. Further the high energy components are bagged together. Here the intelligent mean-count tree is created based on image information. Performance of Image retrieval is tested using image feature database. © 2018 The Authors. Published by Elsevier Ltd.

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Procedia Computer Science

Color Image Retrieval Using Compacted Feature Vector with Mean-Count Tree

Image and video retrieval by their semantic content has been animportant and challenging task for years, because it ultimately requires bridgingthe symbolic/subsymbolic gap. Recent successes in deep learning enableddetection of objects belonging to many classes greatly outperforming traditionalcomputer vision techniques. However, deep learning solutions capable ofexecuting retrieval queries are still not available. We propose a hybrid solutionconsisting of a deep neural network for object detection and a cognitivearchitecture for query execution. Specifically, we use YOLOv2 and OpenCog.Queries allowing the retrieval of video frames containing objects of specifiedclasses and specified spatial arrangement are implemented.

International Journal of Control Theory and Applications

Semantic Image Retrieval: An approach forImage Retrieval using Object Recognition

Stress is a state of mental or emotional strain or tension resulting from adverse or demanding circumstances. The person’s stress should be measured daily so that the person can come to know about his stress statistics day by day. And will take appropriate measures to reduce it. In this paper, points discussed are efficient ways to analyze stress using Neurosky Sensor and also to examine data from smart phones in order measure stress of an individual.

International Journal of Computer Engineering and Applications

STRESS DATA ANALYSIS

To categorize the data reduces the access time. Nowadays, the Internet is one of the biggest data resources. However, most of the data on the Internet is written in natural language. To use the Internet more efficiently, it needs to be categorized. The amount of data and increment rate is so high that this process can not be done by hand. Hence, the necessity of automatic text categorization systems is increasing. In contrast to other languages, there is not much study on Turkish texts. In this study, a system is developed for automatic text categorization of news articles. The articles are classified into 5 different classes and 76% success ratio is achieved.

International Journal of Engineering and Techniques

Automatic Text Categorization on News Articles

Document clustering has been implemented in innovative ways but has till date refrained from making better use of data and information which can be extracted from the World Wide Web. Most research is dependent on static databases like the well investigated Reuters-21578 news corpus for news articles categorization analysis. This paper presents the use of a dynamic news database which is obtained by using a web crawler and this database is updated daily. On the fly clustering is performed and categories are given as input by the user. To reduce the volume of text being analyzed and avoid misleading results preprocessing techniques has been used. To understand the context of the articles and categorize them further TF-IDF has been performed in this experiment.

International Journal of Emerging Technologies and Innovative Research (www.jetir.org)

A step towards Interactive Document Clustering

International Journal of Computing and Technology

Comparison of Feature Extraction Techniques in Cotton Leaf Disease Classification using CBIR

Document image analysis analyzes the document images to extract the text and graphics information from image. Printed character recognition is important in the context of document image analysis. Machine learning Approach such as pattern recognition and matching can be applied to document image based printed character recognition. In this paper we discussed the Template Matching approach to printed character recognition. Template matching is found to be an effective technique to recognize printed character as compared to neural network and other classification techniques. © 2013 Springer.

Advances in Intelligent Systems and Computing

An approach for document image based printed character recognition

Printed character recognition is important in the context of document image analysis. Document image analysis analyzes the document images to extract the text and graphics information from image. Text extraction is followed by the character recognition .This paper reviews various phases of machine printed character recognition in document image. © 2012 Published by Elsevier Ltd.

Procedia Engineering

Computer assisted printed character recognition in document based images

Data stored in databases keep growing as a result of businesses requirements for more information. A big portion of the cost of keeping large amounts of data is in the cost of disk systems, and the resources utilized in managing that data. This paper introduces various compression techniques for data stored in row oriented as well as column-oriented databases. Keeping data in this compressed format as it is operated upon has been shown to improve query performance by up to an order of magnitude. Intuitively, data stored in columns is more Compressible than data stored in rows. Compression algorithms perform better on data with low information entropy (high data value locality) i.e are used for optimization purpose. © 2010 IEEE.