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- Machine Learning Lineage for Trustworthy Machine Learning Systems: Information Framework for MLOps Pipelines.#489498
- Data leakage detection in machine learning code: transfer learning, active learning, or low-shot prompting?#489497
- A comparative study of learning style model using machine learning for an adaptive E-learning.#489496
- Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation.#489495
- On the understandability of machine learning practices in deep learning and reinforcement learning based systems.#489494
- Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.#489493
- Credit card fraud classification using applied machine learning - a comparative study of 24 machine learning algorithms.#489492
- Improved Agricultural Machinery Navigation Algorithm Based on Machine Learning and Machine Vision Technology.#489491
- Machine learning systems as mentors in human learning: A user study on machine bias transmission in medical training.#489490
- A comparative study of traditional machine learning and hybrid fuzzy inference system machine learning models for air quality index forecasting.#489489
- Two outstanding works to harness artificial intelligence in the energy sector 1. Machine Learning Applications for Intelligent Energy Management -Invited Chapters from Experts on the Energy Field. Editors: Haris Doukas; Vangelis Marinakis; Elissaios Sarmas: Springer 2024, pp. 226, ISBN 978-3-031-47908-3. Vol. 35 in Springer Book Series entitled. Learning and Analytics in Intelligent Systems . (https://www.springer.com/series/16172)2. Artificial Intelligence for Energy Systems - Driving Intelligent, Flexible and Optimal Energy Management. Authors: Elissaios Sarmas; Vangelis Marinakis; Haris Doukas. Springer 2025, pp. 266, ISBN 978-3-031-85208-4. Vol. 46 in Springer Book Series entitled. Learning and Analytics in Intelligent Systems . (https://www.springer.com/series/16172).#489488
- Secure Tiny Machine Learning on Edge Devices: A Lightweight Dual Attestation Mechanism for Machine Learning.#489487
- Personalized learning path planning for higher education based on deep generative models and quantum machine learning: a multimodal learning analysis method integrating transformer, adversarial training and quantum state classification.#489486
- Machine learning and human learning: a socio-cultural and -material perspective on their relationship and the implications for researching working and learning.#489485
- Fuzzy Enhanced Kidney Tumor Detection: Integrating Machine Learning Operations for a Fusion of Twin Transferable Network and Weighted Ensemble Machine Learning Classifier.#489484
- Time parallelization and machine learning for optimal control and inverse problems. (Parallélisation en temps et Machine Learning pour le contrôle optimal et les problèmes inverses).#489483
- Graphical machine learning for precision medicine: radiomic analysis of hepatocellular carcinoma. (Machine learning graphique pour la médecine de précision: analyse radiomique du carcinome hépatocellulaire).#489482
- Sensitivity analysis & Machine Learning and their applications toaccelerate the evaluation of analog semiconductor circuits' performances againstmultiple sources of parametric variations. (Analyse de sensibilité et machine learning et leurs applications pour accélérer l'évaluation des performances analogiques de circuits de semi-conducteurs en présence de plusieurs sources de variations paramétriques).#489481
- Use of Machine Learning techniques for flood forecasting. (Utilisation de techniques de Machine Learning pour la prédiction de crue).#489480
- Demystifying machine learning applications in general medicine: an exploration of automated decision support for unscheduled consultations. (Démystifier les applications de l'apprentissage machine en médecine générale / Démystifier les applications de l'apprentissage machine en médecine générale: une exploration de l'aide à la décision automatisée pour les consultations non programmées).#489479
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