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

Feature selection algorithms play a crucial role in any machine learning problem. Choice of the best algorithm yields optimal subset of features thereby increasing the accuracy and reducing the time required for training. In the case of high dimensional datasets it is also advantageous in removing the irrelevant features. This paper presents a novel approach of surveying the popular feature selection algorithms specifically used in medical data classification, by considering the following types of medical data—signals, images and numerical. This work shall be very useful to researchers in collecting first hand information since we have reviewed the various aspects such as—available medical datasets, feature selection techniques, choice of classifier, issues in identifying the feature selection technique, analysis of major feature selection methodologies and detailed mechanisms thereof. We have also performed sample experimentation on the standard medical datasets from UCI and analyzed the effects on time and performance by employing 12 popular classifiers. The results demonstrate improved accuracy and lowered computation times. © 2020, Springer Nature Singapore Pte Ltd.

Engineering and Technology

World University root node

Lecture Notes in Electrical Engineering

Feature Selection Algorithms in Medical Data Classification: A Brief Survey and Experimentation

Wireless Sensor Networks: QoS Perspective

Characterization of Reliability in Wireless Sensor Network

Smart technology is being used in every possible field to make our life easier. To connect humans with the virtual world, computing devices like Smart Glasses are vital. Smart Glasses are very useful in medical, entertainment, education, and many other fields. We proposed smart glasses to assist a person with a Monocular vision in carrying out his day-to-day activities. Understanding depth is the challenge faced by such a person. To overcome this problem, we designed glasses using the Convolution Neural Network and YOLOv3. Recording videos and AR are one of the most important features of Wearable smart glasses. Our glasses assist in measuring the depth of objects (Distance between camera and objects) using a camera module and providing real-time support. It will be useful while judging the distance between approaching barriers. In congested or crowded areas, these glasses help individuals to collect the surrounding information by measuring the distance between different objects. © 2020 IEEE.

2020 IEEE International Conference for Innovation in Technology, INOCON 2020

Smart Glasses to Assist Monocular Vision to Estimate Depth

Pregnancy-induced hypertension (PIH) is a foremost reason for disease and death in maternal, fetal, and neonatal babies. Women having PIH are at greater risk of intrauterine growth retardation in fetuses, premature delivery of a baby, and intrauterine death. Machine Learning has been widely used in an array of applications in the healthcare domain for analyzing data. The aim of this study by the authors is to predict the PIH levels using supervised learning algorithms with an aim to prevent PIH-related complications. The study works on a data set of about 100 pregnant women between the age group of 18–32. The data set uses 19 predictor variables like body surface area (BSA), pulse rate (PR), systolic blood pressure (SBP), and diastolic blood pressure (DBP). SBP and DBP variables are considered to predict the PIH level of the pregnant woman. This work shows that the accuracy achieved by the use of decision tree (90%) is better than that of support vector machine (86.667%) and logistics regression (83.334%) algorithms used in earlier work. © 2020, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Prediction of Pregnancy-Induced Hypertension Levels Using Machine Learning Algorithms

Multi-label classification is a generalization of a multi-class classification problem where one entity can belong to more than one class from the class set. Recent works have proposed multiple methods of solving this problem that involves both statistical and deep learning methods. While methods exist for using deep learning models for this problem, most of them require the model to have a high dimension output vector and the property of inter-dependency of classes has not been explored. An ensemble of statistical models called the chain classifiers can be used to address these issues. This study explores methods of using neural network classifiers in the classifier chain model and tries to address some problems with such architecture while compare their performance on different types of data using different metrics with each other and with other well performing multi-label classification methods. © 2020 IEEE.

Multi-label classification with classifier chains of ANN models

Smart Detection of Parking Rates and Determining the Occupancy

International Journal of Research and Analytical Reviews (IJRAR)

A survey on Vehicle Diagnostic with OBD2-II Telematics for Insurance Company

Fake Email and Spam Detection: User Feedback with Naives Bayesian Approach

Bioscience Biotechnology Research Communications

PCOcare: PCOS Detection and Prediction using Machine Learning Algorithms

Nowadays, population is growing on large scale along with the problems faced by the people are also increasing. Thus, healthcare industry is making lot of technological advancements to provide effective, faster and cheaper treatment to people. Digitization of health records are also expanding in zettabytes. Electronic Health Record (EHR) containing all the patient’s medical history, demographics and other clinical data is also used in hospitals for improved care co-ordination. To avoid critical conditions of people from chronic diseases like hypertension, diabetes, hyperlipidemia etc. there is a need for building a health risk prediction model. But, when whole EHR data is provided to this risk prediction model causes overfitting of features. Overfitting is caused when model learns the details & noise from dataset, thus having negative impact on the performance. Hence, a feature selection approach is proposed for discarding redundant features from EHR. Improved sparse logistic regression method selects the best suitable parameters and forwards to risk prediction model. This regression method improvises the model with the use of logistic loss function that controls the sparsity factor. Neural network is used as a risk prediction model. This paper describes the risk prediction of hypertension disease. Thus, people could take preventive measures for the disease. © Springer Nature Switzerland AG 2020.