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

In India, the insurance sector is rife with inefficiencies. The entire process, from buying an insurance policy to settling claims, has numerous stumbling blocks. Blockchain is considered as a disruptive technology that could revolutionize and bring huge benefits to the insurance sector. It can be can be a game-changer, making the entire insurance process simpler, secure and efficient. Through this paper, we are proposing a blockchain based system “LifeBlocks” to solve various problems in the insurance sector. The paper talks about the problems in the current insurance process and how our system overcomes these problems. The paper discusses the four major use-cases of the system along with technologies like Parity Ethereum, InterPlanetary File System (IPFS) and Smart Contracts used to develop our insurance platform. © 2020 by SCITEPRESS - Science and Technology Publications, Lda.

World University root node

Engineering and Technology

ICETE 2020 - Proceedings of the 17th International Joint Conference on e-Business and Telecommunications

Introduction to LifeBlocks: A blockchain based insurance platform

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH \& TECHNOLOGY (IJERT)

Study on Network Security Algorithm

An Analysis of Cryptocurrency, Bitcoin

Security Challenges and Attacks in Wireless Sensor Networks

Literature review on Web Crawling

Blockchain and Cryptocurrency- The Journey So Far

Image classification has wide applications in many fields including medical imaging. A major aspect of classification is to extract features that can correctly represent important variations in an image. Global image features commonly used for classification include Intensity Histograms, Haralick's features based on Gray-level co-occurrence matrix, Local Binary Patterns and Gabor filters. A novel feature extraction and image representation technique ‘Pixel N-grams' inspired from ‘Character N-grams' concept in text categorization is described in this chapter. The classification performance of Pixel N-grams is tested on the various datasets including UIUC texture dataset, binary shapes dataset, miniMIAS dataset of mammography and real-world high-resolution mammography dataset provided by an Australian radiology practice. The results are compared with other feature extraction techniques such as co-occurrence matrix features, intensity histogram and bag of visual words. The results demonstrate promising classification accuracy in addition to reduced computational costs, enabling a new way for mammographic classification on low resource computers.

Studies in Computational Intelligence

Pixel N-Grams Representation for Medical Image Classification

Part of the Algorithms for Intelligent Systems book series (AIS)

Effects of Color on Visual Aesthetics Sense

Library Philosophy and Practice

A Bibliometric Survey on Cognitive Document Processing

Diabetic Retinopathy is a primary complication of diabetes which more often than not, affects both eyes and anyone with type-1 or type-2 diabetes can develop it. A Diabetic patient should undergo eye tests periodically as the pace of development of this condition is slow. A Dataset of Fundus Photographs of retina is considered. Thus, there is a notable value in automatically categorizing the Fundus Photographs. Therefore, to get a consolidated and objective medical diagnosis, this paper proposes a transfer learning based approach for Diabetic Retinopathy categorization. The resizing of the dataset is performed, which converts the varied images into 224x224 format. The images are augmented using AUGMIX and pooled using GeM. Then, we have used pretrained models, namely SEResNeXt32x4d and EfficientNetb3. The pretraining of the aforementioned neural networks has been done on the ImageNet dataset. Then, the Diabetic Retinopathy images are migrated to these models. Based on the dataset already available, the output is ultimately split up into 5 levels according to the seriousness of the degree of DR. The experimental results show that the training accuracy of this method can reach as high as 0.91. Hence, the retina images of the Diabetic and Healthy patients can be easily classified using our proposed methodology, consequently reducing the number of reviews by medical professionals. © 2020 IEEE.

2020 IEEE International Conference for Innovation in Technology, INOCON 2020

A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning