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

Unfortunately, some of the corrections are not updated on the original article and the corrections are given below. 1. The following affiliations need to be included for author Vaishali U. Gongane Affiliation 1: E and TC Department, SCTR’s Pune Institute of Computer Technology, SPPU, Pune 411043, India. Affiliation 2: E and TC Department, Pune Vidyarthi Griha’s College of Engineering and Technology and G K Pate (Wani) Institute of Management, SPPU, Pune 411009, India. 2. The following affiliation needs to be included for author Mousami V. Munot Affiliation: E and TC Department, SCTR’s Pune Institute of Computer Technology, SPPU, Pune 411043, India. 3. The following affiliation needs to be included for author Alwin D. Anuse Affiliation: School of ECE, Dr Vishwanath Karad MITWPU, Kothrud, Pune, 411038, India Figure 6 is not changed in the manuscript. Figure 6 is shown below. © Springer-Verlag GmbH Austria, part of Springer Nature 2022.

World University root node

School of Electronics & Communication Engineering

Social Network Analysis and Mining

Correction to: Detection and moderation of detrimental content on social media platforms: current status and future directions (Social Network Analysis and Mining, (2022), 12, 1, (129), 10.1007/s13278-022-00951-3)

The global coronavirus pandemic and lockdown has had negative impacts on individuals’ mental health and well-being. The crisis has generated symptoms of depression in many, which may last even after the lockdown is over. To provide support to individuals in terms of counseling and psychiatric treatment, it is necessary to identify such depressive symptoms in a timely fashion. To address this problem, an artificial intelligence-based system is proposed to assess the changes, if any, in the mental health of an individual as a function of time, starting from the pre-lockdown period (in India from 20 April 2020). A Mental Health Analyzer has been implemented to automatically detect whether an individual is trending toward a state of depression based on his or her tweets over time. The deep learning models of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM have been implemented and compared for the emotion classification task, specifically to detect the emotions of sadness, fear, anger, and joy present in a person’s tweets. The system identifies the emotion of sadness present in tweets to detect depression. An ensemble maximizing model using CNN, LSTM, and Bidirectional LSTM is proposed to maximize the recall metric to improve the performance for the task of depression detection. The implemented system was tested using the dataset provided for the SemEval-2018 semantic evaluation tasks and achieves better results than previous models for the task of emotion classification and, further, can detect depression when tested on real Twitter data. © 2022 J. Adv. Inf. Technol.

School of Computer Engineering & Technology

Journal of Advances in Information Technology

Mental Health Analyzer for Depression Detection Based on Textual Analysis

Aluminium alloys are widely used to manufacture alloy wheels for automobiles. Alloy wheels differ from steel wheels in that they possess high strength to weight ratio, better heat conductivity as well as these wheels have a superior aesthetic look. The present work focusses on the application of corner fatigue test and the simulation of wheel rim in order to reduce its the weight and increase the fatigue life of an aluminium alloy A356 wheel. CATIA V5R19 was used to model the wheel rim, and the fatigue analysis was carried out using Ansys 17. The results of the finite element analysis study indicated that the stress generated in the optimised alloy wheel rim was significantly below the actual yield stress of the aluminium alloy. A mass reduction of 200 gm was achieved through the finite element analysis by proper application of stresses on the wheel rim. The overall weight reduction of 1 kg has contributed to the cost reduction as well as decreased fuel consumption for the automobile. © 2022

School of Mechanical Engineering

Materials Today: Proceedings

Fatigue analysis of alloy wheel using cornering fatigue test and its weight optimization

Corona viruses are a genus of viruses that infect vertebrate and birds, causing a variety of diseases. They induce a variety of respiratory problems in people. This study investigates COVID-19 infection rates and estimates the pandemic’s scope, recovery rate, and death rate. We used Support Vector Machine (SVM), Random Forest, Decision Tree, K-nearest neighbor, and other well-known machine learning and mathematical modeling approaches. For disease diagnosis, study used three unique disease data sets (Asthma, Diabetes, and AIDS) provided there in UCI machine learning repository. After getting positive results, we applied these algorithms on COVID-19 data set. We used several categorization algorithms, each with its own set of benefits. The study’s findings support the use of machine learning in disease early detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

Applying ML on COVID-19 Data to Understand Significant Patterns

Cyber-security is unavoidable in present days due to the security importance of each and every digital device part of a global network among millions of computing nodes. Industry 4.0 created much more impact on the automation of industrial micro functionalities to the manufacturing level, with more sensitive data to handle at every stage of organization, then security features of such platform are more important for a complete smooth working environment. This article will elaborate more on an understanding of Incremental learning with data analysis of huge Cyber-attack detection datasets which consist of Adware, Ransomware, SMS malware, scareware, Benign..etc. Incremental learning refers to a group of scalable various algorithms that as the ability to learn in terms of sequentially and can continuously update their models from huge infinite data streams. This working model helps to understand by adopting dynamic learning for varying incoming data inputs based on huge dataset inputs with scalable learning models of algorithms without discarding learned model outcomes. Number of incremental classification model were used where as ARFC gives very good results with higher accuracy, precision, recall, and f1-score values. The results revealed that the proposed model training generates a complete result by adopting variation in dynamic inputs which helps to improve classification higher accuracy by comparing with other training models. © 2022 IEEE.

Proceedings - 2022 2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

Streamed Incremental Learning for Cyber Attack Classification using Machine Learning

Most of the real-time circuits are designed using the sequential logic, and designing sequential logic is slightly more complex than the combinational logic design. Alongside, memory devices play a very critical role in digital systems. A flip flop is a basic element in the designing of any sequential logic like the adders in the combinational logic design. In this paper, sequential logic circuits are designed using different technologies. This paper aims to design high-performance and highly efficient T flip flop and 4-bit asynchronous counter using the gate diffusion input (GDI), CMOS, and transmission gate-based (TGB) techniques and provides a comparison with each other for different parameters. In this paper, comparisons are shown for major VLSI aspects like area, power, delay, and power-delay product (PDP). Simulations are performed, and results are obtained for 180 nm technology with 1.8 V as input voltage using the Cadence virtuoso tool. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Design and Comparison of Low-Power, High-Speed T Flip Flop, and 4-Bit Asynchronous Counter Using Various Design Techniques

Breast Cancer occurs when alterations called mutations to take place in the genes that cause anomalous cell advancement in the breast. One of the ways to achieve success in this field of cancer is by digging deep into machine learning techniques to diagnose the disease better as well as attempt to cure it. This paper aims at identifying breast cancer tumors fast and efficiently. The system suggested in the research uses the Wisconsin Breast Cancer Dataset, which was downloaded from the UCI repository, and allows binary classification, classifying tumors as malignant or benign. Techniques used to implement classification are Support Vector Machines and Random Forest. To comprehend the trends and patterns in the Wisconsin Breast Cancer Dataset, a thorough data visualization of the dataset has been conducted. The system employs data processing techniques to retrieve useful data, followed by Principal Component Analysis to carry out feature extraction. For SVM, to reiterate through the predefined hyperparameters, Grid Search CV has been implemented. For the Random Forest algorithm, k-fold cross-validation has been applied to achieve a unique set of results. The highest accuracy achieved using the random forest algorithm is 99.7% and the same for SVM is 98.2%. The following algorithms have been highlighted since their implementation has helped to retrieve significant accuracy levels. The models have been evaluated by computing the precision, recall score, f1 score, and confusion matrix. Models have also been compared using truepositive rate, true negative rate, false positive rate, and false negative rate. © 2022 IEEE.

2022 IEEE Pune Section International Conference, PuneCon 2022

Effective utilization of Machine Learning Techniques to Classify Breast Cancer Tumors

Land cover data is very significant for designing the earth system, managing the natural resources, and also for performing conservation planning. Time-series data are captured with their dynamic vegetation behavior using remote sensing technology, which is broadly utilized in land cover mapping. Most of the Vegetation Index (VI) such as the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) comprises commonly employed features that are obtained from the time-series spectral data. But, these VIs are not validated as the optimal techniques for generating the temporal profiles. Recent researches highly depend on optical satellite imagery for performing these above-mentioned tasks. Dynamic Time Warping (DTW) is said to be an effective optimal solution for solving the existing challenges, especially the improved version of DTW named Time-Weighted Dynamic Time Warping (TWDTW) is used for time-series analysis regarding the time-series vegetation classification. Yet, the TWDTW efficiency is not shown with other comparative machine learning approaches owing to the classification of vegetation type in the mountain areas. The major goal of this paper is to research and create a novel approach for distinguishing the kind of vegetation in a farm region near Ujani Dam in Solapur District, Maharashtra using time-series analysis. For time-series analysis employing satellite images, the suggested model offers a unique Adaptive Time-Weighted Dynamic Time Warping (ATWDTW). The farm's satellite images are first pre-processed before being sent to ATWDTW for examination. The TWDTW idea is optimized for classification performance using a new hybrid metaheuristic technique named Adaptive Coyote Crow Search Optimization (ACCSO). From the experimental results, the performance of the suggested ACCSO-ATWDTW correspondingly provides superior performance to the traditional approaches, where the designed model using ACCSO-ATWDTW provides 7.2%, 5.2%, 9.9%, 4.55%, and 2.33% higher MCC than the MFO-ATWDTW, BSA-ATWDTW, MF-BSA-ATWDTW, CSA-ATWDTW, and COA-ATWDTW at the maximum iteration of 200. This proved the robustness and less sensitivity to training samples of the TWDTW method when applied to mountain vegetation-type classifications. © 2024 World Scientific Publishing Company.

International Journal of Image and Graphics

Time-Variant Satellite Vegetation Classification Enabled by Hybrid Metaheuristic-Based Adaptive Time-Weighted Dynamic Time Warping

Bone age assessment investigates the ossification improvement for estimating the skeletal age of the pediatrics for analyzing their skeletal growth and forecast their future adult height. The main intent of this paper is to contribute a novel deep learning-based bone segmentation for bone age assessment. Here, the datasets are gathered from both the manual as well as the RSNA database. The segmentation of 5 regions “Distal Phalanx of thumb, middle phalanx, third metacarpal, radius, and ulna” is performed by the optimized U-Net model. As an improvement in the existing U-Net architecture, tuning of the activation function is adopted by the hybridization of two meta-heuristic algorithms such as Class Topper Optimization (CTO) and Whale Optimization Algorithm (WOA) termed as Whale-based Class Topper Optimization (W-CTO). This improved model is developed with the intention of solving the multi-objective segmentation that concerns the parameters like entropy and variance. Moreover, the effect of the proposed segmentation is analyzed by estimating the bone age with the deep Convolutional Neural Network (Deep CNN). From the analysis, the overall MASE of W-CTO-U-Net+CNN is 14.66%, 22.06%, and 5.53% higher than RNN, CNN, and NN, respectively, and RMSE of W-CTO-U-Net+CNN is 53.28%, 22.02%, and 32.87% better than RNN, CNN, and NN, respectively. The performance comparison of the proposed segmentation model over the conventional approaches confirms its effective performance with relatively high accuracy. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.