This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced machine learning applications, making it an invaluable resource for those eager to explore this rapidly growing field. Key Features: Throughout the book, practicality takes precedence, with hands-on coding examples accompanying each concept to facilitate an interactive learning journey Striking a harmonious balance between theoretical foundations and practical coding, the book caters to a diverse audience, including smart home enthusiasts and researchers The content prioritizes real-world applications, ensuring readers can immediately apply the knowledge gained to enhance smart home functionalities Covering Python basics, feature extraction, deep learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications

This proceedings book constitutes the refereed proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics (AISI 2023), which took place in Port Said University, Port Said, Egypt, during September 20–22, 2023, Egypt, and is an international interdisciplinary conference that presents a spectrum of scientific research on all aspects of informatics and intelligent systems, technologies, and applications.

This book constitutes the thoroughly refereed post-conference proceedings of the 6th IFIP WG 13.6 Working Conference on Human Work Interaction Design, HWID 2021, held in Beijing, China, in May, 2021. The 10 revised and extended full papers presented were carefully selected for inclusion in this volume. The papers deal with the analysis and interaction design of a variety of complex work and life contexts found in different business and application domains. They focus on interaction design for work engagement taking usability of interactive systems to the next level by providing employees pleasurable and meaningful experiences via the tools used at work. The papers are organized the following topical sub-headings: Trends in human Work Interaction Design; Workplace & work experience Analysis for Interaction Design; and Artificial Intelligence (AI) for Human Work.

Emotional AI and Human-AI Interactions in Social Networking makes readers aware of recent progress in this integrated discipline. Filling the existing vacuum in research in artificial intelligence with the application of social science, this book provides in-depth knowledge of human-AI interactions with social networking and increased use of the internet. Chapters integrating Emotional Artificial Intelligence, examining behavioral interventions, compassion, education, and healthcare, as well as social cognitive networking, including social brain networks, play a pivotal role in enhancing interdisciplinary studies in the field of social neuroscience and Emotional AI. This volume is a must for those wanting to dive into this exciting field of social neuroscience AI. - Serves as a guide on social cognitive neuroscience for mental health and emotional AI for behavioral interventions - Details various technologies of human-AI interactions with social networking - Includes sections on emotional AI in behavioral interventions, compassion, education and healthcare

The book presents the proceedings of the 10th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2022), held at NIT Mizoram, Aizawl, Mizoram, India during 18 – 19 June 2022. Researchers, scientists, engineers, and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. These proceedings are divided into two volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This volume is a valuable resource for postgraduate students in various engineering disciplines.

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.

This two-volume set LNCS 11581 and 11582 constitutes the thoroughly refereed proceedings of the 10th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2019, which was held as part of the 21st HCI International Conference, HCII 2019, in Orlando, FL, USA, in July 2019. The total of 1275 papers and 209 posters included in the 35 HCII 2019 proceedings volumes were carefully reviewed and selected from 5029 submissions. DHM 2019 includes a total of 77 papers; they were organized in topical sections named: Part I, Human Body and Motion: Anthropometry and computer aided ergonomics; motion prediction and motion capture; work modelling and industrial applications; risk assessment and safety. Part II, Healthcare Applications: Models in healthcare; quality of life technologies; health dialogues; health games and social communities.

Degeneration of nerve cells that control cognitive, speech, and language processes leading to linguistic impairments at various levels, from verbal utterances to individual speech sounds, could indicate signs of neurological, cognitive and psychiatric disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), dementias, depression, autism spectrum disorder, schizophrenia, etc. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. However, speech-based biomarkers could potentially offer many advantages over current clinical standards. In addition to being objective and naturalistic, they can also be collected remotely with minimal instruction and time requirements. Furthermore, Machine Learning algorithms developed to build automated diagnostic models using linguistic features extracted from speech could aid diagnosis of patients with probable diseases from a group of normal population. To ensure that speech-based biomarkers are providing accurate measurement and can serve as effective clinical tools for detecting and monitoring disease, speech features extracted and analyzed must be systematically and rigorously evaluated. Different machine learning architectures trained to classify different types of disordered speech must also be rigorously tested and systematically compared.

Proceedings of the 15th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, Nice, France, 24-27 July 2024.