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Statistical Machine Learning for Human Behaviour Analysis [PDF]
Entropy, 2020 Human behaviour analysis has introduced several challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognition [...]
Thomas B. Moeslund +4 more
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A statistical and machine learning approach to the study of astrochemistry
Faraday Discussions, 2023 We use Bayesian inference together with the MOPED compression algorithm to help determine which species should be prioritised for future detections in order to better constrain the values of binding energies in the ISM.
Johannes Heyl +2 more
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Diagnostics, 2022 The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine ...
S. K. Dhillon +4 more
semanticscholar +1 more source
Aerospace, 2022 The rapid growth of global aviation operations has made its negative environmental impact an international concern. Accurate modeling of aircraft fuel burn, emissions, and noise is the prerequisite for informing new operational procedures, technologies ...
Zhenyu Gao, D. Mavris
semanticscholar +1 more source
NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
Applied Sciences, 2022 NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to
C. Corsaro +5 more
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Proximal Algorithms in Statistics and Machine Learning
Statistical Science, 2015 In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closed-form solutions of proximal operators and envelope representations based on the Moreau, Forward-Backward, Douglas-Rachford ...
Polson, Nicholas G. +2 more
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bioRxiv, 2023 Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (
William DeGroat +5 more
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Predicting the Type of Auditor Opinion: Statistics, Machine Learning, or a Combination of the Two?
European Journal of Applied Economics, 2019 The goal of this study is to overcome the identified methodological limitations of prior studies aimed at predicting the type of auditor opinion and draw definite conclusions on the relative predictive performance of different predictive methods for this
Nemanja Stanišić +2 more
semanticscholar +1 more source
When and How to Apply Statistics, Machine Learning and Deep Learning Techniques
International Conference on Transparent Optical Networks, 2018 Machine Learning has become 'commodity' in engineering and experimental sciences, as calculus and statistics did before. After the hype produced during the 00's, machine learning (statistical learning, neural networks, etc.) has become a solid and ...
Josep Lluis Berral-Garcia
semanticscholar +1 more source
Remote Sensing, 2021 Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu ...
Jun Liu +6 more
semanticscholar +1 more source