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

Engineering and Technology

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.

World University root node

School of Computer Engineering & Technology

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.

2020 IEEE International Conference for Innovation in Technology, INOCON 2020

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

Weld bead geometry inspection by non-destructive testing techniques is the major challenge of today's welding industries. As compared to similar materials, application of dissimilar materials demanded all over. Generally, feature extraction by radiographic images sensed geometric features and categories for classification of weld imperfections. Whereas diverse understanding for human intelligence grasp beneficial evidence from non-geometric features of images. To overcome this difficulty by exploring features and recognize imperfections of two-dimensional digital image wherever geometric features state presence of weld. Mostly, classification accuracy significantly influences by weld imperfection region segmentation and imperfection texture feature extraction. The proposed techniques of local binary pattern in which local binary code describing region, generating by multiplying threshold with specified weight to conforming pixel and summing up by grey-level co-occurrence matrix to extract statistical texture features. At last, support vector machine and k-nearest neighbours compared to discover finest classifier and uppermost accuracy of 96% attained through grouping of local binary pattern features and support vector machine.

School of Mechanical Engineering

Smart Innovation, Systems and Technologies

Weld Imperfection Classification by Texture Features Extraction &amp; Local Binary Pattern

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.

Improved logistic regression approach in feature selection for EHR

Materials Today: Proceedings

Surface modification of titanium and titanium alloy by plasma electrolytic oxidation process for biomedical applications: A review

School of Civil Engineering

Integration of Lean and Sustainable Construction for Residential Project