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

World University root node

School of Computer Engineering & Technology

Cybernetics, Cognition and Machine Learning Applications

Prediction of Stock Indices, Gold Index and Real Estate Index Using Deep Neural Networks

School of Mechanical Engineering

Design and Analysis of Flexure Mechanism Considering Precision Control Strategy

School of Civil Engineering

Innovative infrastructure solutions

Performance of linear and nonlinear damper connected buildings under blast and seismic excitations

Supervised algorithms depend on the given data for categorizing. In present work, we used both parametric and nonparametric types of classifiers. We intend to compare the performance of four popular machine learning classification algorithms—Naïve Bayes, decision trees, logistic regression, and random forest on two popular benchmarked datasets—wine quality dataset and glass identification dataset. To get a wide angle of the performance of these algorithms, we incorporated both binary and multi-class classification which also solved the problem of imbalance in the dataset. In current work, we compare and demonstrate various supervised machine learning classification algorithms on the two well-known datasets. The performance of the algorithms was measured using accuracy, recall, precision, and F1-score. It was observed that nonparametric algorithms like random forest classifier and decision tree classifier bested the parametric algorithms like logistic regression and naïve Bayes. Moreover, as the datasets were imbalanced, we figured out which algorithm performs better under what circumstances. In particular, random forest achieved best performance in terms of all considered metrics, with accuracy of 82 and 83% in wine datasets and 79% in glass identification dataset. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

Investigating the Impact of Data Analysis and Classification on Parametric and Nonparametric Machine Learning Techniques: A Proof of Concept

Determining the image sentiment is a tedious task for classification algorithms, owing to complexities in the raw images as well as the intangible nature of human sentiments. Classifying image sentiments is an evergreen research area, especially in social data analytics. In current times, it is a common practice for majority people to precise their feelings on the web by substituting text with the upload of images via a multiplicity of social media sites like Facebook, Instagram, Twitter as well as any other platform. To identify the emotions from visual cues, some visual features as well as image processing techniques are used. Several existing systems have already introduced emotion detection using machine learning techniques, but the traditional feature extraction strategies do not achieve the required accuracy on random objects. In the entire process, normalization of image, feature extraction, and feature selection are important tasks in the train module. This work articulates the newest developments in the field of image sentiment employing deep learning techniques. Also, the use of conventional machine learning techniques is compared along with deep learning algorithms. It is indicative that a combination of fast recurrent neural networks and CNN may produce high accuracy with minimum time complexity. It is noted from the survey that existing researchers reflect CNN provides around 96.50% average accuracy for sentiment classification on the flicker image dataset. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

Lecture Notes in Networks and Systems

Image sentiment analysis using deep learning

The automobile and transport industries have always tried to reduce downtime by the way of preventive maintenance. In the past decade, electromechanical sensors have become more accurate along with novel innovations in IoT and machine learning, and automobiles have leveraged this. In this paper, an end-to-end open-source predictive maintenance solution is presented to predict the severity of faults in a car using onboard historical and real-time sensor data using IoT and machine learning. Sensor data is collected from a Suzuki Swift VXi Model, and classifiers like logistic regression, random forest, and gradient boosting trees are used to train the data with imputed faults. F1 score and AUC are used as evaluation metrics. An end-to-end onboard diagnostics (OBD) data to the user dashboard pipeline is proposed with final predicted faults visible on a real-time dashboard. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

School of Electronics & Communication Engineering

Maintenance of Automobiles by Predicting System Fault Severity Using Machine Learning

Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 13 {\ldots}

Systematic Analysis and Review of Path Optimization Techniques in WSN

IN Patent App.

MEDICINE DISPENSER

Lecture Notes in Electrical Engineering

Preface

Materials Today: Proceedings

Performance evaluation of hydrocarbon and its blend in split packaged inverter operated air conditioner