Predicting Career Transitions Through Insights from Academic Background and Workforce Dynamics Using Machine Learning Rajneesh Kler, Gurinder Singh, Naina Chaudhary, Bobur Abdullaev, Mohit Bhandwal, et al. Proceedings 4th International Conference on Technological Advancements in Computational Sciences Ictacs 2024, 2024 The increased dynamism in modern employment landscapes has emanated to the frequent career changes and, therefore, the importance of accustoming to the factors explaining occupational change is growing. The paper discusses the various machine learning models in the prediction of career changes with the help of the data set that contains record number of 38444 and has 22 features including the academic background of the person, job satisfaction, skills gap, and the growth of the industry. Therefore, the investigations conducted regarding the connections between these features and career mobility offer practical recommendations for various applications of HR analytics, career guidance, and workforce planning. Logistic regression, decision tree and random forests algorithms were used from the family of machine learning algorithms. Of the models presented, the random forest model was found to be most effective with an accuracy of 85%. In the feature correlation analysis we determined other factors that deem to influence this factors for example field of study, levels of job satisfaction and growth in the industry. Demographic factors are also identified as critical drivers of career choices in the study. The research is useful to identify opportunities for the support of predictive analytics in context to current issues pertaining to talent management, talent development, and the overall talent retention. This research extends prior literature in the field of HR analytics by providing a quantitative approach for analyzing career mobility.
The Role of Artificial Intelligence in Advancing Fracture Mechanics: Modeling, Prediction, and Data Analysis Vivek Srivastava, Nitin Kumar Gupta, Ojas Raturi, Nalin Somani, Mohit Bhandwal, et al. Proceedings 4th International Conference on Technological Advancements in Computational Sciences Ictacs 2024, 2024 With the Internet of Things (loT) and the Industrial Internet of Things (1IoT) as key paradigms, industries and societies have been advanced by allowing systems to interconnect and making intelligent decisions. This paper examines the progress of the loT and IloT by specifically define the taxonomy, communication protocols and system integration for the two technologies. Whenever loT solutions are deployed, taxonomies provide the categorization of diverse constituents, functional relationships, and compositional hierarchies within these arrangements. MQTT, DDS, and OPC UA are discussed as important protocols and their functions, differences, and performance characteristics are compared and analyzed. The paper also aims to look at the integration framework of the IloT as this has the key function of ensure the compatibility between industrial operations and smart technologies. The concepts garnered through loT and IloT are explained by applying them in fields such as Smart Cities, Healthcare, Manufacturing and logistics etc. Nevertheless, some issues still present major questions such as scalability, interoperability and security. That is why this work underscores that there is a need to develop and improve protocols, which are necessary to overcome these challenges. As a synthesis of the current knowledge regarding loT and IloT, this work offers a valuable guide to researchers and practitioners trying to meet the great challenges of building reliable and efficient solutions to next generation interconnected systems.
Refining Large Language Model Query Optimization: An Adaptive Semantic Approach Ayush Thakur, Naina Chaudhary, Astha Gupta, Gurinder Singh, Amresh Kumar Choubey, et al. Proceedings 4th International Conference on Technological Advancements in Computational Sciences Ictacs 2024, 2024 This paper presents a novel approach to improve how questions interact with LLMs in this paper is presented. To this end, we developed an index called Query Semantic Complexity (QSC) that quantifies how challenging a question is. We also developed a method by the name Adaptive Semantic Query Optimization (ASQO) which alters the manner that it processes questions depending on their level of difficulty. Our approach attempts to try striking the middle ground between providing exact responses and employing the computer resources. Our concepts were tried on various LLMs such as GPT-3, T511B, as well as BERT-large that we discussed in this work. The results included great enhancements in speed in its response to questions, and precision of the answers provided. We also applied our method to examples from science and technical writing in practice. It turned out to be most effective when dealing with challenging questions and with large language generators. Overall this research provide a promising approach to making the LLMs perform better when responding to questions.
Effect of creating turbulence on the performance of catalytic converter International Journal of Performability Engineering, 2016
Ecofriendly catalytic converter to reduce biochemical effect of exhaust gases Der Pharma Chemica, 2015
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Modelling and simulation of brake disc for thermal analysis N Gupta, M Bhandwal, BS Sikarwar Indian Journal of Science and Technology 10 (17), 1-5 , 2017 2017 Citations: 17
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The effect of using the turbulence enhancement unit before the catalytic converter in diesel engine emissions M Bhandwal, M Kumar, M Sharma, U Srivastava, A Verma, RK Tyagi International Journal of Ambient Energy 39 (1), 73-77 , 2018 2018 Citations: 12
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Estimate the performance of catalytic converter using turbulence induce devices M Bhandwal, RK Tyagi, BS Sikarwar IJE Trans. B 31, 696-705 , 2018 2018 Citations: 3
Tailoring the thermal conductivity of paraffin wax by nano-fillers for thermal storage applications BS Sikarwar, A Chopra, M Bhandwal, M Kumar, DK Avasthi Proceedings of the 24th National and 2nd International ISHMT-ASTFE Heat and … , 2017 2017 Citations: 3
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Disambiguation of catalytic converter with fluid compounds using automation and reducing cold start time with PCM M Bhandwal, RK Tyagi J. Eng. Res 10 , 2022 2022 Citations: 1