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Proceedings of the 15th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, Nice, France, 24-27 July 2024.
In this Special Issue on human health engineering, we invited submissions exploring recent contributions to the field of human health engineering, which is the technology used for monitoring the physical or mental health status of individuals in a variety of applications. Contributions focused on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main aspects of the monitoring and control engineering scheme applied to human health applications, including papers focusing on measuring/sensing physiological variables, contributions describing research on the modelling of biological signals, papers highlighting health monitoring applications, and finally examples of control applications for human health. In comparison to biomedical engineering, the field of human health engineering also covers applications on healthy humans (e.g., sports, sleep, and stress) and thus not only contributes to develop technology for curing patients or supporting chronically ill people, but also more generally for disease prevention and optimizing human well-being.
Artificial intelligence (AI) in the form of machine learning and nature-inspired optimization algorithms are vastly used in material science. These techniques improve many quality metrics, such as reliability and ergonomics. This book highlights the recent challenges in this field and helps readers to understand the subject and develop future works. It reviews the latest methods and applications of AI in material science. It covers a wide range of topics, including Material processing; Properties prediction; Conventional machining, such as turning, boring, grinding, and milling; non-conventional machining, such as electrical discharge machining, electrochemical machining, laser machining, plasma machining, ultrasonic machining, chemical machining, and water-jet machining; Machine tools, such as programming, design, and maintenance. AI techniques reviewed in the book include Machine learning, Fuzzy logic, Genetic algorithms, Particle swarm optimization, Cuckoo search, Grey wolf optimizer, and Ant colony optimization.
Empirical methods are means to answering methodological questions of empirical sciences by statistical techniques. The methodological questions addressed in this book include the problems of validity, reliability, and significance. In the case of machine learning, these correspond to the questions of whether a model predicts what it purports to predict, whether a model's performance is consistent across replications, and whether a performance difference between two models is due to chance, respectively. The goal of this book is to answer these questions by concrete statistical tests that can be applied to assess validity, reliability, and significance of data annotation and machine learning prediction in the fields of NLP and data science. Our focus is on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs). Based on the interpretable parameters of the trained GAMs or LMEMs, the book presents model-based statistical tests such as a validity test that allows detecting circular features that circumvent learning. Furthermore, the book discusses a reliability coefficient using variance decomposition based on random effect parameters of LMEMs. Last, a significance test based on the likelihood ratio of nested LMEMs trained on the performance scores of two machine learning models is shown to naturally allow the inclusion of variations in meta-parameter settings into hypothesis testing, and further facilitates a refined system comparison conditional on properties of input data. This book can be used as an introduction to empirical methods for machine learning in general, with a special focus on applications in NLP and data science. The book is self-contained, with an appendix on the mathematical background on GAMs and LMEMs, and with an accompanying webpage including R code to replicate experiments presented in the book.
This book presents the emerging developments in intelligent computing, machine learning, and data mining. It also provides insights on communications, network technologies, and the Internet of things. It offers various insights on the role of the Internet of things against COVID-19 and its potential applications. It provides the latest cloud computing improvements and advanced computing and addresses data security and privacy to secure COVID-19 data.
Today’s business world is changing with the adoption of the internet of things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. Realizing the future and full potential of IoT devices will require an investment in new technologies. The Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT demonstrates how the computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based internet of things. Covering key topics such as data, cybersecurity, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, engineers, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.
To explore how mobile technology can be employed to enhance the lives of older adults, the Board on Behavioral, Cognitive, and Sensory Sciences of the National Academies of Sciences, Engineering, and Medicine commissioned 6 papers, which were presented at a workshop held on December 11 and 12, 2019. These papers review research on mobile technologies and aging, and highlight promising avenues for further research.
Proceedings of the 10th International Conference on Human Interaction and Emerging Technologies, IHIET 2023, August 22-24, 2023, Université Côte d'Azur, Nice, France.
The proceeding presents best selected papers presented at 5th International Conference on Smart Computing and Informatics (SCI 2020), held at Department of Computer Science and Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India, during 17 – 18 September 2021. It presents advanced and multi-disciplinary research towards the design of smart computing and informatics. The theme is on a broader front focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solutions to varied problems in society, environment and industries. The scope is also extended towards the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and healthcare. The work is published in two volumes.
Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects. The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. This volume focuses on relevant activities in three main subject areas: Healthcare and Emotion, Mental Health, and Nurse Care Records. The editors are experts in these arenas and the contributing authors are drawn from high-impact research groups around the world. This book will be of great interest to academics, students, and professionals working and researching in the field of human activity and behavior analysis.