Suchergebnisse

"Climate change has far-reaching consequences beyond its environmental impact. It also significantly affects social and mental well-being, both at individual and community levels. Addressing the social and mental well-being impacts of climate change requires a multi-faceted approach that includes both mitigation and adaptation strategies"--

Intro -- -- Preface -- Acknowledgments -- Contents -- About the Authors -- 1 Introduction to Intelligent IoT -- 1.1 Introduction -- 1.2 Background of Intelligent IoT -- 1.3 Big Data with Intelligent IoT -- 1.3.1 Big Data Analytics in IoT -- 1.4 Conclusion -- References -- 2 ML and Information Advancement Platform in Intelligent IoT -- 2.1 Introduction -- 2.2 Training Through Contact with Humans and Machines -- 2.3 Epistemologies -- 2.4 A Computer Awareness System -- 2.5 Case Studies -- 2.5.1 Initialization -- 2.5.2 Key Information Sharing with Other SOs -- 2.5.3 Getting Key Knowledge from Supplementary Knowledge -- 2.5.4 Pioneered Knowledge from Principle Knowledge -- 2.5.5 Being Secondary Knowledge from Fabricated Information -- 2.6 Analysis -- 2.6.1 Latency -- 2.6.2 Machine-to-Machine Correspondence in NLP -- 2.6.3 Inconveniences -- 2.7 Prospective Study -- 2.8 Improved Cyber Security -- 2.9 At the Edge, Machine Learning -- 2.10 Scalability -- 2.11 Hyperconvergence -- 2.12 Reaching the Inference -- 2.13 Conclusion -- References -- 3 Application of Machine Intelligence and Data Science for Intelligent IoT -- 3.1 Introduction -- 3.2 The Combination of Intelligent IoT and ML -- 3.3 Overview of Different Approaches for IoT Analytics -- 3.3.1 Descriptive Analysis -- 3.3.2 Predictive Analysis -- 3.3.3 Prescriptive Analysis -- 3.3.4 IoT Adaptive Analysis -- 3.4 Information Analysis Grouping in IoT Established on Technical Foundation -- 3.4.1 Computation in the Cloud -- 3.4.2 Edge Computing -- 3.5 Utilizations of Data in IoT Analysis -- 3.5.1 Analysis in IoT in Intelligent Transportation -- 3.5.2 Smart Healthcare IoT Data Analytics -- 3.5.3 IoT in Agriculture -- 3.5.4 Analysis of Data in IoT for Energy Applications -- 3.6 An Essential Analysis for IoT for Data Mining with an Exploration of Information -- 3.6.1 Translating Information to Data.

Electronic development is the process of systematic evolution for mankind and society at large that ensures the overall progress of the electronic mode of learning, education, healthcare, society, and corporate governance. The main objective of the chapter was to address the impacts of e-development and sustainable management education for effective leadership that leads to constructing a sustainable society. The required data were collected both from primary and secondary sources. Primary data were collected from 120 respondents. The secondary data sources included Official Websites. The study is empirical and various statistical tools like Mean, Standard Deviation, and t-test were executed for data analysis. The results of the research study were indicated the high degree and low degree of contribution from E-development and Sustainable Management Education are not significant between Effective Leadership and Sustainable Society. E-development can be effective for creating a Sustainable Society with the goal-setting of improving Effective Leadership Skills.

PurposeElectric Vehicles (EVs), owing to their low carbon emissions, have gained immense importance in achieving net-zero emissions by 2070. They have also appeared as viable substitute to conventional vehicles. Aligning with global initiatives, India has created a favourable ecosystem and has implemented several policies since 2011 to achieve its target. Consequently, the market share of EVs has surged, both globally and in India, over the past decade. Taking this into account, this study aims to identify the factors that influence EVs in a developing economy using the context of India.Design/methodology/approachThis study identified important determinants of EV adoption from global literature and employed a multiple linear regression model (MLRM) using the ordinary least squares (OLS) technique. Secondary data were utilised to identify determinants in the Indian context, sourced from the Ministry, NITI Aayog, AQI, the Lok Sabha Question, and the Economic Survey of India.FindingsThis study found that the number of charging stations and local pollution levels significantly influence EV adoption in India. The insignificance of the other variables may be due to the emerging state of the Indian EV market.Originality/valueThis study adds to the growing body of literature on EV adoption in developing economies by analysing the factors that impact its adoption using regional data. In addition, this study provides a unique perspective on a developing economy and advocates a comprehensive policy for EV adoption that reflects long-term sustainability.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0479.