Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Multimodal Assessment of Cognitive Decline: Applications in Alzheimer’s Disease and Depression

The initial diagnosis and assessment of cognitive decline are generally based around the judgement of clinicians, and commonly used semi-structured interviews, guided by pre-determined sets of topics, in a clinical set-up. Publicly available multimodal datasets have provided an opportunity to explore a range of experiments in the automatic detecting of cognitive decline. Drawing on the latest developments in representation learning, machine learning, and natural language processing, we seek to develop models capable of identifying cognitive decline with an eye to discovering the differences and commonalities that should be considered in computational treatment of mental health disorders. We present models that learn the indicators of cognitive decline from audio and visual modalities as well as lexical, syntactic, disfluency and pause information. Our study is carried out in two parts: moderation analysis and predictive modelling. We do some experiments with different fusion techniques. Our approaches are motivated by some of the recent efforts in multimodal fusion for classifying cognitive states to capture the interaction between modalities and maximise the use and combination of each modality. We create tools for detecting cognitive decline and use them to analyze three major datasets containing speech produced by people with and without cognitive decline. These findings are being used to develop multimodal models for the detection of depression and Alzheimer’s dementia

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Explainable AI and Interpretable Computer Vision:From Oversight to Insight

The increasing availability of big data and computational power has facilitated unprecedented progress in Artificial Intelligence (AI) and Machine Learning (ML). However, complex model architectures have resulted in high-performing yet uninterpretable ‘black boxes’. This prevents users from verifying that the reasoning process aligns with expectations and intentions. This thesis posits that the sole focus on predictive performance is an unsustainable trajectory, since a model can make right predictions for the wrong reasons. The research field of Explainable AI (XAI) addresses the black-box nature of AI by generating explanations that present (aspects of) a model's behaviour in human-understandable terms. This thesis supports the transition from oversight to insight, and shows that explainability can give users more insight into every part of the machine learning pipeline: from the training data to the prediction model and the resulting explanations. When relying on explanations for judging a model's reasoning process, it is important that the explanations are truthful, relevant and understandable. Part I of this thesis reflects upon explanation quality and identifies 12 desirable properties, including compactness, completeness and correctness. Additionally, it provides an extensive collection of quantitative XAI evaluation methods, and analyses their availabilities in open-source toolkits. As alternative to common post-model explainability that reverse-engineers an already trained prediction model, Part II of this thesis presents in-model explainability for interpretable computer vision. These image classifiers learn prototypical parts, which are used in an interpretable decision tree or scoring sheet. The models are explainable by design since their reasoning depends on the extent to which an image patch “looks like” a learned part-prototype. Part III of this thesis shows that ML can also explain characteristics of a dataset. Because of a model's ability to analyse large amounts of data in little time, extracting hidden patterns can contribute to the validation and potential discovery of domain knowledge, and allows to detect sources of bias and shortcuts early on. Concluding, neither the prediction model nor the data nor the explanation method should be handled as a black box. The way forward? AI with a human touch: developing powerful models that learn interpretable features, and using these meaningful features in a decision process that users can understand, validate and adapt. This in-model explainability, such as the part-prototype models from Part II, opens up the opportunity to ‘re-educate’ models with our desired norms, values and reasoning. Enabling human decision-makers to detect and correct undesired model behaviour will contribute towards an effective but also reliable and responsible usage of AI

Information and Communication Technologies in Tourism 2022

This open access book presents the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 29th Annual International eTourism Conference, which assembles the latest research presented at the ENTER2022 conference, which will be held on January 11–14, 2022. The book provides an extensive overview of how information and communication technologies can be used to develop tourism and hospitality. It covers the latest research on various topics within the field, including augmented and virtual reality, website development, social media use, e-learning, big data, analytics, and recommendation systems. The readers will gain insights and ideas on how information and communication technologies can be used in tourism and hospitality. Academics working in the eTourism field, as well as students and practitioners, will find up-to-date information on the status of research

Information and Communication Technologies in Tourism 2022

This open access book presents the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 29th Annual International eTourism Conference, which assembles the latest research presented at the ENTER2022 conference, which will be held on January 11–14, 2022. The book provides an extensive overview of how information and communication technologies can be used to develop tourism and hospitality. It covers the latest research on various topics within the field, including augmented and virtual reality, website development, social media use, e-learning, big data, analytics, and recommendation systems. The readers will gain insights and ideas on how information and communication technologies can be used in tourism and hospitality. Academics working in the eTourism field, as well as students and practitioners, will find up-to-date information on the status of research