A critical review of machine learning algorithms in maritime, offshore, and oil & gas corrosion research: A comprehensive analysis of ANN and RF models

Ocean Engineering, Journal Year: 2024, Volume and Issue: 295, P. 116796 - 116796

Published: Jan. 30, 2024

Language: Английский

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FedStack: Personalized activity monitoring using stacked federated learning

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 257, P. 109929 - 109929

Published: Sept. 23, 2022

Language: Английский

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Trends in using deep learning algorithms in biomedical prediction systems

Frontiers in Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: Nov. 9, 2023

In the domain of using DL-based methods in medical and healthcare prediction systems, utilization state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy this context. The integration with health systems enables real-time analysis vast intricate datasets, yielding insights that significantly enhance outcomes operational efficiency industry. This comprehensive literature review systematically investigates latest solutions for challenges encountered healthcare, a specific emphasis on applications domain. By categorizing cutting-edge approaches into distinct categories, including convolutional neural networks (CNNs), recurrent (RNNs), generative adversarial (GANs), long short-term memory (LSTM) models, support vector machine (SVM), hybrid study delves their underlying principles, merits, limitations, methodologies, simulation environments, datasets. Notably, majority scrutinized articles were published 2022, underscoring contemporaneous nature research. Moreover, accentuates forefront advancements techniques practical within realm while simultaneously addressing hinder widespread implementation image segmentation domains. These discerned serve as compelling impetuses future studies aimed at progressive advancement systems. evaluation metrics employed reviewed encompass broad spectrum features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, scalability.

Language: Английский

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Advances in materials informatics: a review

Journal of Materials Science, Journal Year: 2024, Volume and Issue: 59(7), P. 2602 - 2643

Published: Feb. 1, 2024

Language: Английский

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Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

Brain Informatics, Journal Year: 2022, Volume and Issue: 9(1)

Published: July 25, 2022

Autism spectrum is a brain development condition that impairs an individual's capacity to communicate socially and manifests through strict routines obsessive-compulsive behavior. Applied behavior analysis (ABA) the gold-standard treatment for autism disorder (ASD). However, as number of ASD cases increases, there substantial shortage licensed ABA practitioners, limiting timely formulation, revision, implementation plans goals. Additionally, subjectivity clinician lack data-driven decision-making affect quality. We address these obstacles by applying two machine learning algorithms recommend personalize goals 29 study participants with ASD. The patient similarity collaborative filtering methods predicted average accuracy 81-84%, normalized discounted cumulative gain 79-81% (NDCG) compared clinician-prepared recommendations. we assess models' efficacy (TE) measuring percentage recommended mastered participants. proposed recommendation personalization strategy are generalizable other intervention in addition disorders. This was registered clinical trial on November 5, 2020 registration CTRI/2020/11/028933.

Language: Английский

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Vision, status, and research topics of Natural Language Processing

Natural Language Processing Journal, Journal Year: 2022, Volume and Issue: 1, P. 100001 - 100001

Published: Jan. 1, 2022

The field of Natural Language Processing (NLP) has evolved with, and as well influenced, recent advances in Artificial Intelligence (AI) computing technologies, opening up new applications novel interactions with humans. Modern NLP involves machines' interaction human languages for the study patterns obtaining meaningful insights. is increasingly receiving attention across academia industry demonstrates extraordinary opportunities AI (e.g., question answering, information retrieval, sentiment analysis, recommender systems) helps to deal tasks such machine translation reading comprehension, real world performance improving all time. This editorial first provides an overview terms research grants, publication venues, topics. We then introduce mission Journal, a NLP-focused Elsevier journal intended forum researchers practitioners publish theoretical, practical, methodological achievements related trustworthy development analyzing, processing, modeling languages.

Language: Английский

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Predicting Behavior Change in Students With Special Education Needs Using Multimodal Learning Analytics

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 63238 - 63251

Published: Jan. 1, 2023

The availability of educational data in novel ways and formats brings new opportunities to students with special education needs (SEN), whose behaviour learning are highly sensitive their body conditions surrounding environments. Multimodal analytics (MMLA) captures learner environment various modalities analyses them explain the underlying insights. In this work, we applied MMLA predict SEN students' change upon participation analysis (ABA) therapies, where ABA therapy is an intervention that aims at treating behavioural problems fostering positive changes. Here show by inputting multimodal data, our machine models deep neural network can optimum performance 98% accuracy 97% precision. We also demonstrate how environmental, psychological, motion sensor significantly improve statistical predictive only traditional data. Our work has been Integrated Intelligent Intervention Learning (3I Learning) System, enhancing intensive therapies for over 500 Hong Kong Singapore since 2020.

Language: Английский

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Combining human and AI could predict nephrologies future, but should be handled with care

Acta Paediatrica, Journal Year: 2023, Volume and Issue: 112(9), P. 1844 - 1848

Published: June 7, 2023

The authors have no conflicts of interest to declare.

Language: Английский

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Interdisciplinary framework for the development of artificial intelligence solutions in one health

Progress in Artificial Intelligence, Journal Year: 2025, Volume and Issue: unknown

Published: May 10, 2025

Language: Английский

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GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

Frontiers in Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: April 13, 2023

Steady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), novel electroencephalography (EEG)-oriented learning model tailored learn regional characteristics and of EEG-based brain activity perform SSVEPs-based recognition.Group convolution is proposed extract temporal spectral features from the EEG signal each region represent as diverse possible. Furthermore, attention consisting channel-wise specialized network-wise designed identify essential regions form significant feature maps functional networks. Two publicly SSVEPs datasets (large-scale benchmark BETA dataset) their combined dataset are utilized validate classification performance our model.Based on input sample with length 1 s, GDNet-EEG achieves average accuracies 84.11, 85.93, 93.35% benchmark, BETA, combination datasets, respectively. Compared achieved by comparison baselines, trained increased 1.96 18.2%.Our approach can be potentially suitable for providing SSVEP recognition being used in diagnosis.

Language: Английский

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