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Sensors

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Recent advances in machine learning, deep learning techniques, and sensors are greatly impacting how humans and computers and robots interact. For instance, surface electromyography sensors combined with deep-learning-based algorithms are currently being used to operate robotic prosthetic limbs or 3D pose estimation methods to control an avatar in Virtual Reality. Thus, the combination of sensors and machine learning techniques is enabling a range of novel and interesting applications. This Special Issue is intended to cover cutting-edge applications and research on new sensors, machine learning methods or their combination to perform human–computer and human–robot interaction. We strongly encourage the submission of papers focusing on the keywords below, but works on related topics will also be considered.


Applied Sciences

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Artificial intelligence (AI) and its applications are now the hottest research areas. In recent years, there have been more and more AI applications in the medical field. AI technology is promoting the development of the medical and health industries. In the medical domain, AI techniques can be used to develop clinical decision support systems to help with medical diagnostics. AI technologies can be also deployed in various medical devices, trackers, and information systems.


Applied Sciences

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This Special Issue is devoted to the new trends in optics applied to Information and Communication Technologies (ICT). This issue aims to host original, unpublished, and breakthrough concepts in optics that make use of new tools and mechanisms, such as artificial intelligence, to solve complex problems for applications in ICT. Optical systems use communication and information processing. To name a few large fields, we enumerate telecommunications (fiber optics, etc.), information processing (optical and quantum computing, etc.), sources of light (VCSEL, etc.). Manuscripts should be submitted online at www.mdpi.com


Applied Sciences

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As is well known, machine learning (ML) is one of the main branches of artificial intelligence (AI). Its primary objective is to use computational methods to extract information from data. Machine learning has a wide spectrum of practical applications. After the first applications concerning topics such as recognition of manual writing, detection of objects in image processing, voice recognition, medical diagnoses, DNA classification, search engines, and stock market analysis, in recent years machine learning algorithms have been increasingly used in environmental sciences due to their high capability for modelling non-linear phenomena. In particular, these algorithms are already widely used in weather and climate forecasts, as well as in the analysis and modelling of hydrological, ecological, and oceanographic data.


Mathematics

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This Special Issue is devoted to the recent advances in prediction models. Novel methods, new applications, comparative analyses of models, case studies, and state-of-the-art review papers are particularly welcomed. Prediction models are essential to many scientific domains and are gaining widespread popularity. Health care, cybersecurity, education, credit card fraud detection, social media, cloud computing, software measurement, quality and defect simulation, cost and effort estimations, software reuse and evaluation, computational mechanics, theoretical physics, astrophysics, materials design innovation, disease diagnosis, hydrological modeling, earth systems, atmospheric sciences, weather and extreme events prediction, hazard mapping, natural disasters warning systems, policy-making, energy systems, time-series forecasting, and climate change modeling are among the popular applications of prediction models in the literature. The beneficial aspects and the generalizability of prediction models in various technological and scientific domains have highly increased the progression, competitiveness, and research impact of different fields.