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251 results on '"Shukla, Mukul"'

4. Modelling Temperature Distribution in Multi-track Multi-layer Selective Laser Melted Parts: A Finite Element Approach

Author

Kumar, Anuj, Shukla, Mukul, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Tambe, Pankaj, editor, Huang, Peter, editor, and Jhavar, Suyog, editor

Published
2024
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5. Crack Growth Prediction Models for a Pre-defined Semi-elliptical Crack Embedded in a Cantilever Bar Using Supervised Machine Learning Algorithms

Author

Bhardwaj, Harsh Kumar, Shukla, Mukul, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Tambe, Pankaj, editor, Huang, Peter, editor, and Jhavar, Suyog, editor

Published
2024
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13. CFD Analysis of Ventilation of Indian Railway 2 Tier AC Sleeper Coach

Author

Kachhadiya, Jay S., Shukla, Mukul, Acharya, Swastik, Singh, S. K., Cavas-Martínez, Francisco, Editorial Board Member, Chaari, Fakher, Series Editor, di Mare, Francesca, Editorial Board Member, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Editorial Board Member, Ivanov, Vitalii, Series Editor, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Banerjee, Jyotirmay, editor, Shah, Rupesh D., editor, Agarwal, Ramesh K., editor, and Mitra, Sushanta, editor

Published
2023
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16. An Effective Scheme to Mitigate Blackhole Attack in Mobile Ad Hoc Networks

Author

Shukla, Mukul, Joshi, Brijendra Kumar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Patgiri, Ripon, editor, Bandyopadhyay, Sivaji, editor, Borah, Malaya Dutta, editor, and Emilia Balas, Valentina, editor

Published
2022
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18. Development of a 3D Printed Orthopaedic Cast for Wrist Fracture

Author

Islam, Mohd Ahad, Shukla, Mukul, Tripathi, Yogesh, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Sachdeva, Anish, editor, Kumar, Pradeep, editor, Yadav, O. P., editor, and Tyagi, Mohit, editor

Published
2022
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22. A unified creep and fatigue life prediction approach for 316 austenitic stainless steel using machine and deep learning.

Author

Bhardwaj, Harsh Kumar and Shukla, Mukul

Subjects
*ARTIFICIAL neural networks, *AUSTENITIC stainless steel, *FATIGUE life, *CREEP (Materials), *NUCLEAR pressure vessels, *DEEP learning
Abstract

316 Austenitic stainless steel (AusSS) is extensively utilized in high‐temperature industrial applications such as boiler tubes and nuclear reactor pressure vessels. These components commonly experience failure under high‐temperature and high‐pressure conditions, attributed to either creep or fatigue. Existing classical models for creep and fatigue life prediction focus on a singular failure mode (either creep or fatigue) and consider physical features only. This study aims to develop a unified life prediction model for both creep and fatigue phenomena. It synthesizes information from 12 additional unexplored chemical and microstructural features from the National Institute of Materials Science (NIMS), Japan database, and previously published literature. Machine learning (such as decision tree, random forest, and XGBoost) and deep learning (like deep neural network) algorithms are employed in the modeling process. The trained models have been cross‐validated against unseen creep and fatigue life data, demonstrating superior prediction accuracy of 96.1% for deep neural network compared with classical models. Highlights: Unified prediction model for both creep and fatigue life prediction.Investigated the effect of up to 20 physical, chemical, and microstructural features.Multiple ML and DL prediction models were developed and compared with classical ones. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Trust Based Model for Mobile Ad-Hoc Network in Internet of Things

Author

Singh, Upendra, Shukla, Mukul, Jain, Ashish Kumar, Patsariya, Mohan, Itare, Ravikant, Yadav, Sakshi, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Smys, S., editor, Bestak, Robert, editor, and Rocha, Álvaro, editor

Published
2020
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46. Acknowledgment of patient in sense behaviors using bidirectional ConvLSTM.

Author

Singh, Upendra, Gupta, Puja, Shukla, Mukul, Sharma, Varsha, Varma, Sunita, and Sharma, Sumit Kumar

Subjects
COVID-19 pandemic, COVID-19, DATABASES, HUMAN activity recognition
Abstract

Recognizing patient activity in real‐time from video or images collected by a CCTV camera available in the hospital during a Covid‐19 situation has proven challenging. The dilemma of patient activity recognition is identifying and recognizing a patient's various actions in a series of videos. The process presented in our paper needs to achieve unrestricted, generic behavior in videos. Detecting events in any video is often difficult because we use Bidirectional ConvLSTM to create a robust patient in the sense behaviors (PSB) framework capable of eliminating certain barriers. To begin this paper by proposing a new Bidirectional ConvLSTM for establishing a stable PSB scheme. Our proposed model is capable of accurately predicting patient's behaviors like seated, standing, and so on. Using Bidirectional ConvLSTM, learning information from a pre‐trained model is an excellent place to start for rapidly developing a new PSB system using a current PSB database, as both the source and target datasets are critical. All parameters are frozen in a pre‐trained PSB device. Then, using the UCI and HMDB51 dataset to train the model, variables and local relations are progressively fixed. A novel PSB framework is developed using the target dataset. Relevant tests are conducted using commonly used research indices to assess prediction precision accuracy. They acknowledge six patient's behavior with a weighted accuracy rate of 92%. For recognizing novel activity, laying, the precision of a corresponding prediction is the best, 91%, of all six test results. The proposed work uses bidirectional ConvLSTM with modified activation layers to sense the patients' behavior. This article may be a patient activity recognition system to identify an individual. It takes a clip of COVID‐19 patients as input and looks for matches inside the hold‐on images. [ABSTRACT FROM AUTHOR]

Published
2023
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