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The integration of artificial intelligence (AI) stands as both a promise and a challenge in the field of healthcare. As technological advancements reshape the industry, academic scholars find themselves at the forefront of a crucial dialogue about the ethical implications and societal repercussions of AI. The accelerating sophistication of AI technologies brings forth a central dilemma: how to maintain the crucial human touch required for compassionate and effective patient care in the face of unprecedented technical progress. This challenge is not only a theoretical concern but a pressing reality as healthcare systems increasingly rely on AI-driven solutions. Approaches to Human-Centered AI in Healthcare emerges as a significant guide, offering a comprehensive exploration of the opportunities and challenges entwined with the integration of AI into healthcare. The book becomes a critical compass, navigating readers through the intricate intersections of AI and patient care. By delving into real-world case studies, cutting-edge research findings, and practical recommendations, it provides a roadmap for scholars to navigate the complexities of healthcare AI. In doing so, it aims not only to inform but to shape the discourse around the responsible integration of AI, ensuring that the fundamental principles of compassionate patient care remain at the forefront. Within the pages of this publication, it emphasizes the moral, patient-centric, and socially responsible aspects of AI in healthcare. The imperative to place humans at the center of AI-driven developments is a guiding principle, urging scholars to consider the societal implications and ethical nuances that accompany the transformative potential of AI in healthcare. By addressing these concerns, the book positions itself as a resource for reshaping the discourse around healthcare AI, contributing to a future where technological innovation enhances patient experiences, improves outcomes, and upholds the core tenets of compassionate healthcare.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are promising tools that can be used to develop algorithms to better understand and predict interactions between food- and nutrition-related data and health outcomes. Understanding that additional research is needed to identify areas where AI/ML is likely to have an impact, the National Academies Food and Nutrition Board hosted a public workshop in October 2023 to explore the future benefits and limitations of integrating big data and AI/ML tools into nutrition research. Participants also discussed issues related to diversity, equity, inclusion, bias, and privacy and the appropriate use of evidence generated from these new methods.

This book constitutes the proceedings of the 24th International Conference on Speech and Computer, SPECOM 2022, held as a hybrid event in Gurugram, India, in November 2022.The 51 full and 9 short papers presented in this volume were carefully reviewed and selected from 99 submissions. The papers present current research in the area of computer speech processing including audio signal processing, automatic speech recognition, speaker recognition, computational paralinguistics, speech synthesis, sign language and multimodal processing, and speech and language resources.

This book constitutes the refereed proceedings of the 7th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2021, held in Havana, Cuba, in October 2021. The 42 full papers presented were carefully reviewed and selected from 73 submissions. The papers promote and disseminate ongoing research on mathematical methods and computing techniques for artificial intelligence and pattern recognition, in particular in bioinformatics, cognitive and humanoid vision, computer vision, image analysis and intelligent data analysis.

Modern medicine and healthcare are highly dependent on engineering, employing instrumentation and computer systems to aid investigation, diagnosis, treatment and patient management. The significant developments in the field of computational intelligence, combined with the emergence of high-performance computing is impacting society in many ways, and the health sector is no exception. The interface of high-performance computing, computational intelligence and medical science, has seen the emergence of intelligent medical systems. These systems can provide a deeper insight into many healthcare and medical problems. It can also aid in controlling, analyzing and the management of medical applications and can provide significant improvement in the quality of life and efficacy of clinical treatment. However, the successful application of high-performance computing in medicine requires in-depth knowledge and understanding of medical systems. This book focuses on the advances and applications of high-performance computing for medical systems and provides an insight into the latest developments in the field. It will help readers to understand the high-performance computing research domain as related to intelligent medical systems, its effect on our lives and its present limitations.

This book constitutes the refereed proceedings of the 13th IFIP WG 5.11 International Symposium on Environmental Software Systems, ISESS 2020, held in Wageningen, The Netherlands, in February 2020. The 22 full papers and 3 short papers were carefully reviewed and selected from 29 submissions. The papers cover a wide range of topics on environmental informatics, including data mining, artificial intelligence, high performance and cloud computing, visualization and smart sensing for environmental, earth, agricultural and food applications.

While social robots participation increases in everyday human life, their presence in diverse contexts and situations is expected. At the same point, users tend to become more demanding regarding their roles, abilities, behaviour and appearance. Thus, designers and developers are confronted with the need to design more sophisticated robots that can produce such a positive reaction from users so as to become well accepted in various cases of use. Like this, Human-Robot Interaction has become a developing area. Emotions are an important part in human life, since they mediate the interaction with other humans, entities and/or products. In recent years, there has been an increase in the importance of emotions applied to the design field, giving rise to the so-called Emotional Design area. In the case of Human-Robot Interaction, the emotional design can help to elicit (e.g., pleasurable) or prevent (e.g., unpleasant) emotional/affective reactions/responses. This book gives a practical introduction to emotional design in human-robot interaction and supports designers with knowledge and research tools to help them take design decisions based on a User-Centred Design approach. It should also be useful to people interested in design processes, even if not directly related to the design of social robots but, instead, to other technology-based artefacts. The text is meant as a reference source with practical guidelines and advice for design issues.

This two-volume set (CCIS 915 and CCIS 916) constitutes the refereed proceedings of the 5th Workshop on Engineering Applications, WEA 2018, held in Medellín, Colombia, in October 2018. The 50 revised full papers presented in this volume were carefully reviewed and selected from126 submissions. The papers are organized in topical sections such as computer science; computational intelligence; simulation systems; software engineering; power and energy applications.

Introduction to EEG- and Speech-Based Emotion Recognition Methods examines the background, methods, and utility of using electroencephalograms (EEGs) to detect and recognize different emotions. By incorporating these methods in brain-computer interface (BCI), we can achieve more natural, efficient communication between humans and computers. This book discusses how emotional states can be recognized in EEG images, and how this is useful for BCI applications. EEG and speech processing methods are explored, as are the technological basics of how to operate and record EEGs. Finally, the authors include information on EEG-based emotion recognition, classification, and a proposed EEG/speech fusion method for how to most accurately detect emotional states in EEG recordings. - Provides detailed insight on the science of emotion and the brain signals underlying this phenomenon - Examines emotions as a multimodal entity, utilizing a bimodal emotion recognition system of EEG and speech data - Details the implementation of techniques used for acquiring as well as analyzing EEG and speech signals for emotion recognition

This book contains the outcome of the 9th IFIP WG 5.5 International Summer Workshop on Multimodal Interfaces, eNTERFACE 2013, held in Lisbon, Portugal, in July/August 2013. The 9 papers included in this book represent the results of a 4-week workshop, where senior and junior researchers worked together on projects tackling new trends in human-machine interaction (HMI). The papers are organized in two topical sections. The first one presents different proposals focused on some fundamental issues regarding multimodal interactions, i.e., telepresence, speech synthesis and interactive modeling. The second is a set of development examples in key areas of HMI applications, i.e., education, entertainment and assistive technologies.

This book introduces approaches that have the potential to transform the daily practice of psychiatrists and psychologists. This includes the asynchronous communication between mental health care providers and clients as well as the automation of assessment and therapy. Speech and language are particularly interesting from the viewpoint of psychological assessment. For instance, depression may change the characteristics of voice in individuals and these changes can be detected by a special form of speech analysis. Computational screening methods that utilize speech and language can detect subtle changes and alert clinicians as well as individuals and caregivers. The use of online technologies in mental health, however, poses ethical problems that will occupy concerned individuals, governments and the wider public for some time. Assuming that these ethical problems can be solved, it should be possible to diagnose and treat mental health disorders online (excluding the use of medication). Speech and language are particularly interesting from the viewpoint of psychological assessment. For instance, depression may change the characteristics of voice in individuals and these changes can be detected by a special form of speech analysis. Computational screening methods that utilize speech and language can detect subtle changes and alert clinicians as well as individuals and caregivers. The use of online technologies in mental health, however, poses ethical problems that will occupy concerned individuals, governments and the wider public for some time. Assuming that these ethical problems can be solved, it should be possible to diagnose and treat mental health disorders online (excluding the use of medication).

th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15–18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability “to learn” by example (a large volume of cases) through several iterations without requiring a priori?xed knowledge of the relationships between process parameters.

We present in this volume a collection of revised selected papers from the ISCA Tutorial and Research Workshop on Nonlinear Speech Processing (NO- LISP 2007)held in Paris,France, 22–25May,2007.NOLISP 2007was organized by the University Pierre and Marie Curie (UPMC) with the generous support of ISCA (International Speech Communication Association), EURASIP and the IEEE. NOLISP 2007 was the?rst follow-on workshop to a series of three e- lier events related to nonlinear speech processing, that were organized within the framework of the European COST action 277 Nonlinear speech processing (2001–2005). The?nancial support of ISCA enabled the attendance of leading researchers from various parts of the world. Theexciting?eldofspeechprocessinghaswitnessedtremendousdevelopment over the past 20 years or so, thanks to both technological progress and to the growing focus of research on a number of key application areas. However, some speci?cities of the speech signal are still not well addressed by the currently availablemodels. Hence, new nonconventionalmodels and processing techniques needtobeinvestigatedinordertofosterand/oraccompanyfutureprogress,even if they do not match immediately the level of performance and understanding of the current state-of-the-art approaches. ThepurposeofNOLISPistopresentanddiscussnovelideas,workandresults related to such alternative techniques for speech processing, which depart from mainstream approaches. It declared its intent to be an interdisciplinary forum, intertwining research in di?erent?elds of speech processing with its growing applications in everyday practice.