3篇
您的检索式：作者名="Sunil Chandel"
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| 1 | An experimental and numerical approach-characterisation of power cartridge for water-jet application显示文摘Power Cartridges are pyrotechnic devices where hot combustion gases utilized to do mechanical work for disruption of suspected Improvised Explosive Devices(IEDs). It plays a vital role either in destroying the suspicious object or making them non-functional by generating the gas pressure on burning of propellant against the water column inside the barrel, Present work is focused on characterisation,numerical solution such as deformation; strain; stress using FEM(Finite Element Method), design qualification, performance and evaluation of power cartridge for disruptor application. Experimental trials for pressure-time(P-t) measurement in closed vessel(CV), various electrical parameters like all fire current(AFC), no fire current(NFC) and ignition delay have been measured. Further, mechanical properties for brass material have been determined. An attempt has been made to characterise the power cartridge by FEM and carrying out the experiments for water-jet application. | Bhupesh Ambadas Parate Sunil Chandel Himanshu Shekhar | 2018 | Defence Technology(防务技术)2018，14，6： | 0 |
| 2 | AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector显示文摘Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting requires knowledge of these machine learning models and their distinguishing features.The datasets we use as input for each of these different types of ML models,yielding different results.The choice of an ML model for a dataset is critical.A loan risk model is used to show how ML models for a dataset can be linked together.The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk.This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields.If credit risk is considered,a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried.Various approaches to credit risk calculation will be examined,ranging from the most basic to the most complex.In addition,we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches,logistic regression,decision tree,and gradient boost to see which one produced the most commercially useful insights. | Vikas Kumar Shaiku Shahida Saheb Preeti Atif Ghayas Sunil Kumari Jai Kishan Chandel Saroj Kumar Pandey Santosh Kumar | 2023 | Big Data Mining and Analytics2023，6，4： | 0 |
| 3 | A PLS-SEM Based Approach: Analyzing Generation Z Purchase Intention Through Facebook’s Big Data显示文摘The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook.The role of antecedents of attitude,namely enjoyment,credibility,and peer communication was also explored.The main purpose was to analyze the F-commerce pervasiveness(retail purchases through Facebook)among Generation Z in India and how could it be materialized effectively.A conceptual fac¸ade was proposed after trotting out germane and urbane literature.The study focused exclusively on Generation Z population.The data were statistically analyzed using partial least squares structural equation modelling.The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce.Enjoyment,credibility,and peer communication were proved to be good predictors of attitude(R^(2)=0.589)and furthermore attitude was found to be a stellar antecedent to purchase intentions(R^(2)=0.540). | Vikas Kumar Preeti Shaiku Shahida Saheb Sunil Kumari Kanishka Pathak Jai Kishan Chandel Neeraj Varshney Ankit Kumar | 2023 | Big Data Mining and Analytics2023，6，4： | 0 |
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