Prof. Kittisak JERMSITTIPARSERT
University of City Island, Cyprus
Research Area: Political Science, Public & Private Management, International Political Economy, Social Research
Introduction: Kittisak Jermsittiparsert is a Full-Professor at Faculty of Education, University of City Island, Cyprus, an Adjunct Professor at Sekolah Tinggi Ilmu Administrasi Abdul Haris, and an Adjunct Research Professor at Universitas Muhammadiyah Sinjai, Universitas Muhammadiyah Makassar & Universitas Muhammadiyah Sidenreng Rappang, Indonesia. Previously, he was a Full-Professor of Public Administration at Huanghe Business School, Henan University of Economics and Law, China, a Lecturer at Faculty of Political Science, Rangsit University & College of Innovative Business and Accountancy, Dhurakij Pundit University, a Researcher at Chulalongkorn University Social Research Institute, Thailand, and an Adjunct Researcher at Ton Duc Thang University & Duy Tan University, Vietnam. He holds a Ph.D. (Social Sciences), M.A. (Political Science) & B.A. (Political Science) from Kasetsart University, Thailand. Additionally, he earned M.B.A. & M.P.A. from Ramkhamhaeng University and B.Pol.Sc. (International Relations and Comparative Governments and Politics) from Sukhothai Thammathirat Open University. His areas of expertise are Political Science, Public and Private Management, International Political Economy, and Social Research. He is the editor of many famous journals in the world, such as Sustainability,Energy Engineering, Recent Advances in Computer Science and Communications, and Asian Academy of Management Journal. He is named in the world’s top 2% of scientists list by Stanford University, USA since 2020. Up to now, he has published more than 450 Scopus/WoS indexed documents. His h-index is 51 with more than 9000 citations.
Speech Title: The Influence of Job Engagement and Organizational Engagement on Employee Performance with the Mediation of HRM Practices: A Study of Thailand Manufacturing Sector
Abstract: Thailand has recognized the importance of the manufacturing sector for decades and has always been a pacemaker of Thailand’s economic growth. The manufacturing industry will enhance its productivity by 30 percent per person over the next seven years on Thailand's national agenda. Analogically employee performance (EP) plays an important role in the overall survival of the organization and collaterally to the performance of the industry. This paper explores how highly potential employees in the manufacturing sector of Thailand are associated between job engagement (JE), organizational engagement (OE) and human resources management (HRM) practices towards the level of employee performance (EP). This research has employed the purposive sampling method to select the managerial and executive level high potential employees. In addition, cross-sectional method for data collection was used and 265 respondents from 450 survey questionnaires have been returned, which yield a satisfactory level of response rate. Smart PLS software was utilized to test the measurement and structural model. The findings have shown a significant relationship for four direct and indirect hypotheses. The findings of the study therefore have provided valuable perspectives for researchers, organizations and human resources experts, which will therefore be of interest to the manufacturing sector of Thailand.
Prof. Lu Leng
School of Software, Nanchang Hangkong University, China
Research Area: Biometric identification and authentication, biometric template protection, computer vision
Brief introduction: LU LENG received his Ph.D degree from Southwest Jiaotong University, Chengdu, P. R. China, in 2012. He performed his postdoctoral research at Yonsei University, Seoul, South Korea, and Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China. He was a visiting scholar at West Virginia University, USA, and Yonsei University, South Korea. Currently, he is a full professor at Nanchang Hangkong University. Prof. Leng has published more than 100 international journal and conference papers, including about 60 SCI papers and three highly cited papers. He has been granted several scholarships and funding projects, including five projects supported by National Natural Science Foundation of China (NSFC). He serves as a reviewer of more than 100 international journals and conferences. His research interests include computer vision, biometric template protection and biometric recognition. Prof. Leng is an outstanding representative of "Innovation Talent" of Jiangxi Enterprise in "Science and Technology China" in 2021, received "Jiangxi Youth May Fourth Medal" in 2019, "Jiangxi Hundred-Thousand-Ten-thousand Talent Project" in 2018, "Jiangxi Voyage Project" in 2014, etc.
Speech Title: Advanced Palmprint Recognition
Abstract: Biometric recognition is convenient and reliable, so it has been widely used for identification and verification. Palmprint is a promising biometric modality and has several advantages, including high accuracy, good availability, high acceptability, etc. This speech introduces several advanced palmprint recognition technologies. Since palmprint is a typical biometric modality, its recognition technologies can be conveniently extended to other biometric modalities.
A. Prof. Yongquan Yan
School of Statistics, Shanxi University of Finance and Economics
Research Area: Machine learning, dependable computing, software aging and rejuvenation
Speech Title: Software aging prediction using neural network with ridge.
Abstract: Since software systems become more complex than before, software ageing problems have a big impact on the performance of running software systems. To find software ageing in advance, some prediction methods were used to forecast those parameters which can indicate software ageing occurrences. Since the unsuitable parameters can reduce the prediction ability of an algorithm, in this study, multilayer perceptron (MLP) with ridge is proposed to improve the prediction accuracy of MLP and apply in software ageing problems. The proposed approach is a three-step method. First, a pre-processing process needs to be done by using outlier recognition, dispose, and normalisation. Second, MLP with ridge is proposed and used to optimise network structure. Third, a glowworm swarm optimisation method is utilised to automatically find optimal values of model parameters. In the experimental section, the results indicate that the proposed algorithm owns higher forecast accuracy than other state-of-the-art methods on two levels.
A. Prof. Mabel C. Chou
Department of Decision Sciences, National University of Singapore, Singapore
1. Production Scheduling
2. Logistics and Supply Chain Analysis
3. Operations/Manufacturing Flexibility Design and Analysis
Speech Title: Managing Supply Chain Risk with Process Flexibility
Abstract: The global supply chain is undergoing significant changes as a result of recent international tensions and pandemic-related disruptions. To mitigate the uncertainties arising from these supply shocks, supply chain operators need to effectively cushion the supply-side risks while also hedging against demand uncertainties. In both theory and practice, process flexibility has been proven to be an effective supply chain strategy that enables timely and effective responses to demand uncertainties. This talk aims to shed light on how process flexibility can mitigate supply chain uncertainties with a few case studies.
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