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

Engineering and Technology

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International Journal of Research and Analytical Reviews

E-Recruitment System using NLP

Today’s world is witnessing great increase in the use of Social Media. People use them as a platform to share their feelings, emotions and experiences along with lot of personal information. All such information could be used in advantageous ways to help increase the Business and understand the user need. Personality prediction has gained lot of focus nowadays. It studies behavior of users and reflects the thinking, feelings etc. Traditional ways to take survey that was time consuming, for large number of users there is need of automatic prediction. Users are dynamic and can have their account on multiple platforms as they can have multi-context information. This survey gives an overview of different strategies used to predict the personality and behavior using the content available on social sites. Ability to predict user’s personality traits can help to build many customized services or products. Finally, the last section gives the Future trends and directions.

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International Journal of Engineering Research and Technology

Personality prediction from social media text:An overview

Automatic number plate recognition recent the recent Trend in current environment, various systems has already developed by existing authors to detect the license plate as well as number plate using machine learning and deep learning algorithms. Internet of things (IoT) and some machine learning techniques for image processing is also contributed many classical systems. In this paper we proposed automatic number plate recognition for smart toll system, in traditional approach it is very tedious to perform the entire process with manual human interfaces. Sometime it also generate long traffic queue on toll plaza, to eliminate such a problem with this propose systems. This system initially deals with some IoT devices which contains camera sensor and arduino based microcontroller. The deep learning libraries have used to process the capture image by IoT environment with OCR library. This system also deals with user applications to auto debit toll charges. The state of art provides the conceptual model of entire system execution with real time as well as synthetic dataset, and theoretical experiment analysis shows the effectiveness of system.

CLIO Annual Inter disciplinary Journal of History

Vechile Number Plate Recognition for automatic toll tax collection using IoT and Machine Learning

Cardiac Arrhythmia is a condition in which the heartbeat is may be fast or slow than the normal ones. Nowadays, a consistent electrocardiogram (ECG) analysis and classification play an important role in the diagnosis of cardiac abnormalities. ECG provides useful information about the functional status of the heart. Analysis of ECG is of great importance in the detection of cardiac anomalies. This paper provides different medical data mining and machine learning techniques are being implemented to extract valuable information from ECG signal for prediction of heart disease. But, the accuracy of the desired results is not yet satisfactory.

Journal of Emerging Technologies and Innovative Research

Heart Disease Detection Using ECG Waves: A Survey

Blockchain is very emerging trend in recent years; it is basically decentralized approach whichprovides transparency to transactional data. While data security is the most essential requirement inthe 21st century, distributed as well as the centralized environment. Identifying or validate the user'sidentity based on educational histories is a very tedious task. Many commercial applications validateby respective organizations based on physical verification. Search systems do not provide trustworthyinformation when declared on a centralized data basis, due to two database security concerns. Variousdatabase and software base intrusion attacks can harm centralized data and change the actualinformation. This paper, the system illustrates blockchain base e certificate generation for theeducational field. In a real- time scenario, many organizations make document verification ofparticular employees on traditional approaches like physical verification, etc. The system proposes isE-certification and token generation using a strategic process which provides hassle-free e verificationfor the organization of specific employee. This system also carried out custom blockchainimplementation with dynamic smart contract, and mining policy, which generate the UniqueIdentification (UID) number and QR code for each user for online verification. The final phase providesvarious consensus algorithms for drastic data verification and majority voting in the P2P network. Thepartial implementation of the system shows the effectiveness of the proposed system over the traditionalapproaches.

CLIO An Annual Interdisciplinary Journal of History (CLIO AIJH), ISSN: 0976-075X

Generation of E-Certificates using Custom Blockchain for Peer to Peer Network

Aesthetic&nbsp;assessment&nbsp;of&nbsp;images&nbsp;has&nbsp;been&nbsp;getting&nbsp;a&nbsp;lot&nbsp;of&nbsp;attention&nbsp;for&nbsp;the&nbsp;past decade&nbsp;in&nbsp;the&nbsp;field&nbsp;of&nbsp;computer&nbsp;vision.&nbsp;Large&nbsp;amounts&nbsp;of&nbsp;social&nbsp;media&nbsp;and&nbsp;advertising&nbsp;data&nbsp;in the&nbsp;form&nbsp;of&nbsp;images&nbsp;is&nbsp;continuously&nbsp;analysed&nbsp;to&nbsp;assign&nbsp;it&nbsp;an&nbsp;aesthetic&nbsp;quality&nbsp;value&nbsp;to&nbsp;improve businesses&nbsp;as&nbsp;well&nbsp;as&nbsp;for&nbsp;gaining&nbsp;more&nbsp;popularity&nbsp;across&nbsp;the&nbsp;web.&nbsp;Visual&nbsp;perception&nbsp;by&nbsp;humans cannot&nbsp;be&nbsp;fully&nbsp;replicated&nbsp;by&nbsp;a&nbsp;machine&nbsp;and&nbsp;continuously&nbsp;more&nbsp;work&nbsp;is&nbsp;being&nbsp;published&nbsp;on&nbsp;aes- thetic&nbsp;classification&nbsp;of&nbsp;images.&nbsp;In&nbsp;this&nbsp;paper,&nbsp;we&nbsp;have&nbsp;presented&nbsp;a&nbsp;convolutional&nbsp;neural&nbsp;network model&nbsp;which&nbsp;automatically&nbsp;extracts&nbsp;high&nbsp;level&nbsp;features&nbsp;and&nbsp;distinguishes&nbsp;a&nbsp;set&nbsp;of&nbsp;images&nbsp;into pleasing&nbsp;and&nbsp;non-pleasing&nbsp;categories.&nbsp;Our&nbsp;dataset&nbsp;has&nbsp;been&nbsp;compiled&nbsp;from&nbsp;a&nbsp;variety&nbsp;of&nbsp;sources on&nbsp;the&nbsp;web&nbsp;to&nbsp;make&nbsp;it&nbsp;as&nbsp;diverse&nbsp;as&nbsp;possible.&nbsp;Compared&nbsp;to&nbsp;the&nbsp;traditional&nbsp;handcrafted&nbsp;meth- ods&nbsp;and&nbsp;other&nbsp;machine&nbsp;learning&nbsp;models,&nbsp;our&nbsp;CNN&nbsp;model&nbsp;has&nbsp;provided&nbsp;a&nbsp;better&nbsp;classification accuracy of 68% on our&nbsp;dataset.

International Journal for Research in Applied Science and Engineering Technology

Assessing High level Aesthetic Image Features using Deep Learning

Nowadays, there is a rapid growth in the number of video cameras at public and private sector because of the monitoring and security purposes. As video surveillance using Closed Circuit Television (CCTV) is in boom nowadays, it has got more research attention due to increased global security concerns. This rapidly growing data can be used to automatically detect the anomalous activities which are going around in our surrounding. Anomalous activity is something that deviates from its normal nature or something that opposes the normal events. This research mainly focuses on detecting anomalous activities in crowded scenes by using video data. Automatically detecting the anomalous activity without using the handcrafted feature has become the need of the hour. This paper contains a survey of different approaches used for anomaly detection in the past. Different incremental and transfer learning approaches are discussed in this paper and it was found that incremental learning has not been used for video-based anomalous activity detection.

European Journal of Engineering Research and Science (EJERS)

Anomalous Activity Detection in Videos Using Increment Learning A Survey

Recommendation system has become a requirement in today’s world. This recommendation system is spread over large sectors right from education, entertainment, health, business etc. The University Recommendation System is a system which recommend right university for the students based on their GRE, TOFEL score. Due to rapid increase in data and lack of learning in education filed, students often halt selecting wrong universities. Thus, there is a need of recommendation system which is trained to understand the user’s requirements and suggests them that suitable university which they need. Typically, recommendation mechanisms are used based on user’s historical data. It helps the user to discover information and settle on right choices where they do not have the required learning to judge a specific item. This paper explores the flow, types and uses of recommendation system

International Journal of Recent Technology and Engineering (IJRTE)

The University Recommendation System for Higher Education

This article provides research on sleep apnoea. Sleep apnoea is a capable for suspending breath or frequently pausing in period of deep sleep. This symptoms may leads to an unappropriate death that makes it a critical sleeping disorder. Periods of apnoea generally lasts for five seconds or hardly a minute which affects the sleeping pattern due to breathing. This probably happens five times of an hour or even more. Obstructive sleep apnoea (OSA),central sleep apnoea (CSA) and mixed/complex sleep apnoea(MSA) are common three types of apnoea, where mixed/complex sleep apnoea is combination of other two apnoea. Airway obstruction is caused in OSA, while in CSA airway is not blocked, but the brain dosn’t sends proper signals to the muscles that cause instability of the respiratory center. The study includes the sleep disorders, types, cause, signs and symptoms and methods of Sleep Apnoea. Considering the study; it is very much required to detection of sleep apnoea using noninvasive techniques. Machine learning algorithms based detection of sleep apnoea is a feasible solution which provides more than 90% accuracy. The study surveys the similar techniques based on machine learning.

JERS, European Journal of Engineering Research and Science

Sleep Apnoea Disorder A Review

Dementia and its forms like Mild Cognitive Impairment and Alzheimer’s Disease are posing great defiance to societyclaiming many lives. Alzheimer’s Disease is the fifth leading cause of death in the world. The most frequently observed form of thisdementia is Alzheimer’s Disease. Diagnosis of this disease is a very tedious task for the doctors, which can lead to errors in thejudgment. This paper proposes multi-class classification by using MRI Images of subjects into 3 different classes such as Normal, MCI(Mild Cognitive Impairment) and Alzheimer’s Disease. The presented 3D CNN model can accurately classify Cognitive Normal patientsand also MCI (Mild Cognitive Impairment) which may morph into Alzheimer’s Disease to prevent the further disintegration andseverity of the patient’s condition. This paper also presents the comparative study of the performance of different Machine Learningmodels on the test diagnosis data of the subjects and suggests the most efficient approach to classify into 3 classes by data preprocessingand Hyperparameter optimization.