Results 21 to 30 of about 73,912 (246)

Towards Interpretable Deep Learning Models for Knowledge Tracing

, 2020
As an important technique for modeling the knowledge states of learners, the traditional knowledge tracing (KT) models have been widely used to support intelligent tutoring systems and MOOC platforms.
F Arbabzadah, H Yang, L Arras, M Feng, M Grégoire, M Schuster, RSJ Baker, S Bach, S Hochreiter   +8 more
core   +1 more source

Instantiating the onEEGwaveLAD Framework for Real-Time Muscle Artefact Identification and Mitigation in EEG Signals

Sensors
While electroencephalography is extremely useful for studying brain activity, EEG data is always contaminated by a wide range of artefacts. Many techniques exist to identify and remove such artefacts, primarily offline, with and without human supervision
Luca Longo, Richard Reilly
doaj   +1 more source

Carbon price interval prediction by bidirectional long short-term memory and multi-objective optimization with an asymmetric scaling approach

Energy Reports
Accurate carbon price prediction is essential for decision-making and risk management. Most existing predictive models produce deterministic results and fail to account for uncertainties in carbon prices. To address this limitation, this study introduces
Di Sha, Arne Johannssen, Xianyi Zeng, Zhenglei He, Hanhan Wu, Kim Phuc Tran   +5 more
doaj   +1 more source

A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners

IEEE Access
Due to market deregulation and globalisation, competitive environments in various sectors continuously evolve, leading to increased customer churn. Effectively anticipating and mitigating customer churn is vital for businesses to retain their customer ...
Awais Manzoor, M. Atif Qureshi, Etain Kidney, Luca Longo   +3 more
doaj   +1 more source

Artificial Intelligence Explained for Nonexperts

Seminars in Musculoskeletal Radiology, 2020
AbstractArtificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging.
Narges, Razavian, Florian, Knoll, Krzysztof J, Geras   +2 more
openaire   +3 more sources

Advancing Deliberative Discourse Measurement: The Intersection with Computational Abstract Argumentation in Discourse Quality Evaluations

Systems
This research investigates the potential of computational argumentation, specifically the application of the Abstract Argumentation Framework (AAF), to enhance the evaluation of deliberative quality in public discourse.
Sanjay Kumar, Jane Suiter, Luca Longo
doaj   +1 more source

Explainable Artificial Intelligence in Echocardiography [PDF]

Advanced Ultrasound in Diagnosis and Therapy
Recent advancements in artificial intelligence (AI) have generated novel opportunities and challenges in ultrasound imaging. Deep learning algorithms exhibit significant potential in analyzing echocardiographic images, encompassing tasks such as view ...
Hu Xuelin, Zhu Ye, Zhang Zisang, Quan Yuanting, Chen Wenwen, Chen Leichong, Xu Guangyu, Qin Luning, Xie Mingxing, Zhang Li
doaj   +1 more source

Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics

Healthcare Analytics, 2023
This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care. We develop and compare four machine learning models, including multinomial logistic regression, decision tree, random ...
Alireza Kasaie, Suchithra Rajendran
doaj   +1 more source

Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning‐Based Approaches

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Objectives To identify predictors of chronic ITP (cITP) and to develop a model based on several machine learning (ML) methods to estimate the individual risk of chronicity at the timepoint of diagnosis. Methods We analyzed a longitudinal cohort of 944 children enrolled in the Intercontinental Cooperative immune thrombocytopenia (ITP) Study ...
Severin Kasser, Alice Bizeul, Meera Chitlur, Hugo Donato, Jelena Roganovic, Julia E. Vogt, Thomas Kühne   +6 more
wiley   +1 more source

Exploring the clinical value of concept-based AI explanations in gastrointestinal disease detection

Scientific Reports
Complex artificial intelligence models, like deep neural networks, have shown exceptional capabilities to detect early-stage polyps and tumors in the gastrointestinal tract.
Andrea M. Storås, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Wojciech Samek, Pål Halvorsen, Thomas de Lange, Yuichi Mori, Alexander Hann, Tyler M. Berzin, Sravanthi Parasa, Michael A. Riegler   +11 more
doaj   +1 more source