Motilal Nehru National Institute of Technology Allahabad

About: Motilal Nehru National Institute of Technology Allahabad is a education organization based out in Allahabad, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 2475 authors who have published 5067 publications receiving 61891 citations. The organization is also known as: NIT Allahabad & Motilal Nehru Regional Engineering College.

Effect of ER4043 and ER5356 filler wire on mechanical properties and microstructure of dissimilar aluminium alloys, 5083-O and 6061-T6 joint, welded by the metal inert gas welding

Abstract: This work studies the effect of filler metal on the mechanical and microstructure properties of dissimilar aluminium alloys of 5083-O and 6061-T6 welded using metal inert gas welding. The metal ine...

Enhanced public auditability & secure data storage in cloud computing

Abstract: Cloud computing is the most envisioned paradigm shift in the computing world. Its services are being applied in several IT scenarios. This unique platform has brought new security issues to contemplate. This paper proposes a homomorphic encryption scheme based on the Elliptic curve cryptography. It implements a provable data possession scheme to support dynamic operation on data. The application of proof of retrievability scheme provisioned the client to challenge integrity of the data stored. The notion of a third party auditor (TPA) is considered, who verifies and modifies the data on behalf of the client. Data storage at the server is done using a Merkle hash tree (MHT) accomplishing faster data access. This proffered scheme not only checks the data storage correctness but also identifies misbehaving servers. The initial results demonstrate its effectiveness as an improved security system for data storage compared to the existing ones in most prospects.

A Machine Learning based Depression Analysis and Suicidal Ideation Detection System using Questionnaires and Twitter

Abstract: Depression as a disorder has been a great concern in our society and has been perpetually a hot topic for researchers in the world. Despite the massive quantity of analysis on understanding individual moods together with depression, anxiety, and stress supported activity logs collected by pervasive computing devices like smartphones, foretelling depressed moods continues to be an open question. In this paper, we have proposed a depression analysis and suicidal ideation detection system, for predicting the suicidal acts based on the level of depression. We collected real time data from students and parents by making them fill questionnaires similar to PHQ-9 (Parent health questionnaire) consisting of questions like What’s your age? or Are you regular in school/college? and processed it into meaningful data with related features like age, sex, regularity in the school, etc. Then, classification machine algorithms are used to train and classify it in five stages of depression depending on severity - Minimal or none, mild, moderate, moderately severe and severe. Maximum accuracy i.e. 83.87 % was achieved by using XGBoost classifier in this dataset. Also, data was collected in the form of tweets and were classified into whether the person who tweeted is in depression or not using classification algorithms. Logistic Regression classifier gave the maximum accuracy i.e. 86.45 % for the same.

An empirical investigation of nuclear energy and environmental pollution nexus in India: fresh evidence using NARDL approach

Abstract: Environmental pollution caused by carbon emissions is an emerging issue to study among researchers. The nexus between environmental pollution and carbon emissions has motivated researchers to evaluate the impact of cleaner energy on carbon emissions. This study further contributes to the research by examining the above relationship by studying the asymmetric influence of nuclear energy (NE), industrial productivity (IP), and economic growth rate (GDP) on carbon emissions (CO2) in India from 1975 to 2018 using the asymmetric autoregressive distribution lag approach. The results of the bound test and the Wald test show that in the long run, there is an asymmetric relationship between CO2, NE, GDP, and IP, and in the short run, there is a symmetric relationship between CO2, GDP, and IP. The result implies that increased usage of nuclear energy over fossil fuels and improvement in industrial productivity, in the long run, reduces CO2 emission in India. Furthermore, the study concludes that the government should consider the asymmetric relationship between the variables and devise appropriate policies to increased nuclear energy and industrial productivity in India for environmental sustainability.