Machine learning (ML algorithms can be used to better understand human behavior in its various developmental stages and to assist in addressing psychological issues. Additionally, in the realm of mental health and well-being, algorithms can assist with early detection of disorders and customization of treatments as well as personalize recommendations and suggestions based on user behavior. By focusing on user experience and usability, ML may be used to address challenges faced by private enterprises and social issues. Exploring Psychology, Social Innovation and Advanced Applications of Machine Learning explores the relationships between human psychology and machine learning technology, enabling researchers to delve into areas such as user interface design, ethics in artificial intelligence, and the social impact of algorithms. Furthermore, it promotes interdisciplinary collaboration by bringing together perspectives from different fields, which could stimulate new research and innovative approaches in the field of machine learning. Covering topics such as industrial processes, digital therapy, and machine vision, this book is an excellent resource for psychologists, computer scientists, engineers, healthcare practitioners, educators, business leaders, policymakers, professionals, researchers, scholars, academicians, and more.
Mercado-Herrera, Abelardo: - Abelardo Mercado Herrera is a Doctor of Science and Master of Science from the National Institute of Astrophysics, Optics and Electronics, specializing in Astrophysics, Postdoctorate in Astrophysics from the Institute of Astronomy of the National Autonomous University of Mexico, Electronics Engineer from the Autonomous University of Baja California . He is a specialist in the mathematical-statistical description of stochastic and/or deterministic processes, nonlinear systems, complex systems, chaos theory, among others, as well as their application to physical phenomena such as astronomy, economics, finance, telecommunications, science social etc., in order to determine the underlying dynamics in such processes, and if necessary, their connection with real physical variables and possible prediction. He has worked on the development of various electronic circuits, interfaces and programs to carry out electrical tests in the industry, as well as in scientific instrumentation, applied to telemetry, infrared polarimetry, optics and spectroscopy. He has also specialized in image analysis, measurement techniques and noise reduction.
Orduño-Osuna, Jesus Heriberto: - Jesús Heriberto Orduño-Osuna obtained a Bachelor's degree in Mechatronics Engineering from the Universidad Politécnica de Baja California (UPBC) and a Master's degree in Computational Sciences and Applied Mathematics from Universidad Internacional de La Rioja, Mexico (UNIR Mexico), where he developed projects in application and research in the area of Machine Learning applied to dynamic systems. He is partially studying a Master's degree in Strategic Administration. He has worked as an engineer in the industry, mainly in the Post-Harvest Automation industry as well as in the manufacturing sector, focusing primarily on automation processes, vision systems, and industrial robotics, among others. Currently, he is a part-time lecturer at the Universidad Politécnica de Baja California and the Universidad del Valle de México, teaching courses in Mechatronics, Manufacturing, Energy, and Computing, contributing to the education of engineers with an innovative and technological focus. His research interests are focused on Machine Learning, Automation, and control of industrial processes, Microcontrollers, and digital signal processing.
