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Testing the LCC hypothesis by considering environmental sustainability and economic development:Role of green energy and resource management

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Geoscience Frontiers 2024年第3期15卷 419-430页

作者： Sunil Tiwari Syed Ali Raza Shiv Kumar Gupta Irum Shahzadi mahendra babu kuruva Department of Tourism Studies School of Business StudiesCentral University of KeralaIndia Department of Business Administration IQRA UniversityKarachi 75300Pakistan Centre for Mountain Tourism and Hospitality Studies HNB Garhwal Central UniversitySrinagarUttarakhand 246174India Department of Production Engineering São Paulo State University(UNESP)BauruBrazil Department of Business Administration IQRA University Karachi-75300Pakistan Department of Business Management HNB Garhwal Central UniversitySrinagar GarhwalUttarakhand 246174India

This study aims to demystify the role of green energy and green technology in establishing the nexus between behavioural intentions of tourists,technologies,and digital payments by using Perceived value(PV),Compatibility(CO),Perceived Enjoyment(PE),and Social Influence(SI)as a predictor variables,Trust(TR)and Satisfaction(SA)as a mediating variables and Behavioural Intentions(BI)as an outcome *** the empirical estimation,we employ smart PLS-SEM,TAM(Technology Acceptance Model)and SPSS and Tested the LCC *** findings suggest that green energy and perceived value have the highest positive impact on tourists’trust towards digital payments followed by compatibility,social influence and perceived ***,tourists’satisfaction and green technology is one of the important determinants of choosing any digital mode of payment,is mostly influence by perceived value,perceived enjoyment,compatibility and social ***,if we choose between trust and satisfaction,trust plays a significant role in exploring the behavioural intentions of tourists about green energy and green technology followed by tourists’*** addition,Tourists’trust and satisfaction are highly correlated and influence each *** study offers novel policy implications in terms of use of green technology and green energy in enhancing trust and satisfaction of tourists in order to deeper understanding of different dimensions of digital payments and M-wallets,and allowing them to explore the long-term value inherent of digital payments and M-wallets.

关键词： Digital payment Green technology Green energy Tourism LCC hypothesis

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Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus

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Computer Modeling in Engineering & Sciences 2023年第12期137卷 2513-2528页

作者： B.Ramesh kuruva Lakshmanna School of Information Technology and Engineering Vellore Institute of TechnologyVelloreTamilnadu632014India

Major chronic diseases such as Cardiovascular Disease(CVD),diabetes,and cancer impose a significant burden on people and healthcare systems around the ***,Deep Learning(DL)has shown great potential for the development of intelligentmobile Health(mHealth)interventions for chronic diseases that could revolutionize the delivery of health care anytime,*** aimof this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis,prognosis,management,and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing *** 2 Diabetes Mellitus(T2DMs)is a regular chronic disorder that is caused by the secretion of insulin,which leads to serious death-related issues and the most complicated *** Heart Disease(CHD)is the most frequent issue related to T2DM *** major concern is recognizing the high possibility of CHD complications,yet the model is not available to identify *** work introduces a deep learning technique that can predict heart disease effectively using a hybrid model,which integrates DNNs(Deep Neural Networks)with a Multi-Head Attention Model called *** scheme canbedesignedtoautomatically learnthe best-quality features fromElectronic Health Records(EHRs),and effectively combine heterogeneous and time-sequencedmedical data for predicting the risk of *** analysis is done using the Kaggle *** outcomes prove that the MADNN has improved accuracy by about 95%and indicates the precise accuracy is higher for the disease compared with SVM,CNN and ANN.

关键词： Diabetes mellitus cardiovascular diseases deep learning coronary heart disease deep neural networkwith amultihead attention model

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High Utility Periodic Frequent Pattern Mining in Multiple Sequences

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 733-759页

作者： Chien-Ming Chen Zhenzhou Zhang Jimmy Ming-Tai Wu kuruva Lakshmanna College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Technology Vellore Institute of TechnologyVellore632014India

Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several *** experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns.

关键词： Decision making frequent periodic pattern multi-sequence database sequential rules utility mining

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