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

World University root node

Engineering and Technology

Lecture Notes in Electrical Engineering

Probabilistic Harmony Search Algorithm: Fitness Proportionate Selection Variants

Lecture Notes in Networks and Systems

Deep Learning Applications to Detect Alzheimer’s—A Review

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 Electronics & Communication Engineering

School of Electrical Engineering

Algorithms

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

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

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

Biomedical Signal Processing and Control

Effective Epileptic Seizure Detection by Classifying Focal and Non-focal EEG Signals using Human Learning Optimization-based Hidden Markov Model

In recent decades, scholars have proposed different methods to distinguish between original images and forgery images, which are divided into active forensics and passive forensics. Active forensics techniques have the advantages of strong detection ability and are not easy to be avoided. However, in active forensics techniques, the verification information needs to be inserted into carrier images before distribution, which decreases the quality of images. Passive forensics techniques are used to verify the authenticity by analyzing the information and structure of images, which overcome the defects of active forensics techniques. There are mainly two forgeries to alter the content of images: splicing and copy-move. Splicing forgery is a way to copy and paste a part of an image into another image. Copy-move forgery is a way to copy and paste a part of an image into the same image. © 2023 Author(s).

AIP Conference Proceedings

Copy-move forgery detection using SIFT and agglomerative hierarchical clustering

Recognition of Struck Out Words Using a Deep Learning Approach

The data from the mobile devices and internet services plays a significant role now a day. The information shared needs to be original to get the best solution. Without the utilization of privacy preservation techniques, the individual's sensitive information cannot be exposed. The Privacy preserving data publishing (PPDP) is one of the privacy preserving techniques to preserve privacy. However, the reduction of the loss of data and the security enhancement are the major challenges. To solve the issues in secure data publishing, the efficient Cat Swarm Henry Gas Solubility Optimization (CSHGSO) is introduced for achieving effective privacy preservation for secure data publishing. In addition, the proposed CSHGSO is devised by the incorporation of Cat Swarm Optimization (CSO), and Henry Gas Solubility Optimization (HGSO). Here, the procedure of generating secret key is carried out based on the proposed CSHGSO with respect to the objective measures. The experimentation of the developed CSHGSO is performed in JAVA tool using Heart Disease Dataset. Furthermore, the CSHGSO achieved the higher conditional privacy of 1.338 using database-1, maximum accuracy of 0.965 using database-4, and the NMI value of 0.927 using database-4.

Advances in Engineering Software

Optimized key generation-based privacy preserving data mining model for secure data publishing

COVID-19 has caused physical, emotional, and psychological distress for people. Due to COVID-19 norms, people were restricted to their homes and could not interact with other people, due to which they turned to social media to express their state of mind. In this paper, we implemented a system using TensorFlow, which consists of multilayer perceptron (MLP), convolutional neural networks (CNN), and long short-term memory (LSTM), which works on preprocessing, semantic information on our manually extracted dataset using Twint scraper. The models were used for classifying tweets, based upon whether they indicate depressive behavior or not. We experimented for different optimizer algorithms and their related hyperparameters for all the models. The highest accuracy was achieved by MLP using sentence embeddings, which gave an accuracy of 94% over 50 epochs, closely followed by the other two. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.