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 Electronics & Communication Engineering

Lecture Notes in Electrical Engineering

Application of Cohort Intelligence Algorithm for Numerical Integration

School of Civil Engineering

Lecture Notes in Civil Engineering

Pavement Analysis and Measurement of Distress on Concrete and Bituminous Roads Using Mobile LiDAR Technology

School of Polymer Engineering

Separation of Heavy Metals from Metal Industry Effluent for Acid Recovery

Lecture Notes in Networks and Systems

Vehicle Cluster Development

Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world’s urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting. © 2023 by the authors.

School of Computer Engineering & Technology

School of Electrical Engineering

Algorithms

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Automating human activity recognition is one of computer vision's most appealing and pragmatic research areas. In this article, we have addressed the problem of video-based student activity detection. The student’s activity detection using YOLO (SADY) aims to recognize the normal and abnormal student activities to ensure immediate intervention in case of any risk or necessity. We created our classroom data set of around 220 recordings depicting seven student classroom activities. The YOLOv4 Tiny model was retrained using 5000 labeled keyframes extracted from the train videos. The model was then tested for single or multiple activity detections. We presented the evaluated results for various values of hyperparameters like confidence threshold and Intersection Over Union (IoU) thresholds for the proposed model. The model assigns a unique confidence score and action label to each frame for the test videos by positioning recurrent activity labels. The proposed approach achieved a mean average precision (mAP) of 95% and a frame per second rate (FPS) of 45 for the student activity Class Room (CR) dataset and mAP of 95.18 % for the LIRIS dataset. The experimental findings using the Class Room recorded and LIRIS publicly accessible dataset show that our proposed approach outperforms existing approaches regarding recognition accuracy and speed. The comparable results obtained in this research work imply that the proposed framework could effectively monitor student’s activities in schools, colleges, and universities. © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

International Journal on Advanced Science, Engineering and Information Technology

SADY: Student Activity Detection Using YOLO-based Deep Learning Approach

With lockdowns and overburdened medical facilities during the Covid-19 pandemic, technology and computing paradigms play a vital role in providing remote healthcare solutions. We assess as how the existing computing paradigms could be deployed to prevent the spread of the disease, expedite the diagnosis, and facilitate remote monitoring of patients to reduce the burden on the overstretched medical facilities. The chapter will include a literature survey based on the articles published in but not limited to Science Direct, Google Scholar, Research Gate, and PubMed. This study weighs the pros and cons of using different paradigms in diverse scenarios and provides recommendations for efficient healthcare solutions. The chapter also focuses on the issues related to edge computing, such as resource provisioning, energy preservation, etc. In this era of technology, edge computing can be used to enhance the efficacy of healthcare solutions without burdening healthcare professionals and facilities. In this chapter, experimentation will focus on deploying intelligent techniques in the edge computing paradigm. © 2023 Elsevier Inc. All rights reserved.

Intelligent Edge Computing for Cyber Physical Applications

Applicability of edge computing paradigm for Covid-19 mitigation

In today’s era, stock prediction has become one of the dominant real world application. Most of the times, scientists attempted to establish a direct connection between information macroeconomic factors and stock returns; however, with the revelation of nonlinear slants in financial exchange record returns, there has been a significant shift in the scientists’ focus toward the nonlinear expectation of stock returns. Even though various articles on nonlinear measurable presenting of stock returns have appeared since then, the huge demand is the nonlinear model which is specified before the estimation is performed. Predicting stock value is a difficult task that necessitates a solid algorithmic structure to calculate returns. Because stock prices are volatile and depend on the market up and down, forecasting stock prices becomes difficult. It has never been easy to invest in a portfolio of assets; the abnormalities of the financial market prevent simple models from accurately predicting future asset values. Machine learning, which is teaching computers to execute activities that would ordinarily need human intelligence, is the current scientific study hot topic. This paper explores gated recurrent units (GRU), simple recurrent neural network (Simple RNN) and long short term memory (LSTM) models for stock price prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Stock Price Prediction Using GRU, SimpleRNN and LSTM

The sudden impact during a collision of an automobile may cost the life of passengers. Several researchers are working to design materials and structures which can absorb impact-energy before passing it to the passengers. One such solution is the crash-box-structure which is placed primarily on the frontal side, in-between the bumper and main part of an automobile. The crash-box-structure's ability to absorb energy is influenced by its deformation-properties. Both, the crash-box-structure's shape and material will decide how it affects it's deformation. The impact of various geometries and materials on the crash-box's deformation characteristics is discussed in this article. Moreover, the use of fiber-reinforced composites in the crash-box is also discussed. The surface modification may affect the impact behavior, which is also discussed in this review. © Published under licence by IOP Publishing Ltd.

School of Mechanical Engineering

Journal of Physics: Conference Series

A Review Thin-Wall-Tube Energy-Absorbing Structure : Crash-Box

Electroencephalogram (EEG) recordings are analyzed to make a diagnosis of neurological diseases like epilepsy before surgical intervention. It is essential to examine the EEG signals of individuals with epilepsy to separate between focal and non-focal seizure sources. In this research, a train-optimized hidden Markov model (HMM) based on human learning optimization is proposed to automatically detect the epileptic seizure by distinguishing the focal and non-focal epileptic EEG signals. Human learning optimization (HLO) plays a crucial part in electrode selection, which aids in the subsequent feature extraction process, reduces the model's complexity, and chooses the features with the best discriminating properties. The HLO-train optimized HMM model receives the feature vector made up of frequency band-based features, which is then tuned to optimum using the proposed model to improve detection performance. The searching and learning skills of the human is included in the development of the HLO algorithm, that helps in boosting the convergence of the model adds value to the research. The Epileptic seizure is effectively recognized using the model and the efficiency of the research is proved by measuring the evaluation metrices. The HLO-train optimized HMM model attained for the CHB-MIT scalp EEG database with an accuracy of 99.158 %, sensitivity of 98.086 %, and specificity of 99.155 %, while the accuracy of 94.231 %, sensitivity of 92.075 %, and specificity of 99.926 % with the Siena scalp dataset, which is efficient, when compared with state of art methods. © 2023 Elsevier Ltd