Suchergebnisse - "Supervised learning"

Exploring the dynamics of the β2-adrenergic receptor: insights from explainable AI in GPCR research

Autoren: Gutiérrez Mondragón, Mario Alberto

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament de Ciències de la Computació

Thesis Advisors: Vellido Alcacena, Alfredo, König, Caroline

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: GPCRs, G protein-coupled receptors, beta2-adrenergic receptor, molecular dynamics, deep learning, machine learning, supervised learning, explainable artificial intelligence, convolutional neural networks, residue interaction networks, conformational states, model explanations, knowledge discovery, ANOVA, feature relevance, class imbalance, bioinformatics, structural bioinformatics, proteomics, pharmacology, Àrees temàtiques de la UPC::Informàtica, Àrees temàtiques de la UPC::Ciències de la salut, 004 - Informàtica, 577 - Bioquímica. Biologia molecular. Biofísica

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/695469
https://dx.doi.org/10.5821/dissertation-2117-443474

From pixels to graphs: cell-based digital pathology image analysis

Autoren: Pina Benages, Òscar

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions

Thesis Advisors: Vilaplana Besler, Verónica

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Computational Pathology, Digital Pathology, Whole Slide Images, Cell Detection, Cell Classification, Transformers, Graph Neural Networks, Self-Supervised Learning, Domain Adaptation, Weakly Supervised Learning, Àrees temàtiques de la UPC::Informàtica, Àrees temàtiques de la UPC::Enginyeria biomèdica, 004 - Informàtica, 616 - Patologia. Medicina clínica. Oncologia

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/695086
https://dx.doi.org/10.5821/dissertation-2117-439958

Supervised learning for optimal investment planning in active distribution networks

Autoren: Saldaña González, Antonio Emmanuel

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica

Thesis Advisors: Sumper, Andreas, Aragüés Peñalba, Mònica

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Distribution Network Planning, Supervised Learning, Reinforcement actions, Optimization of size and location of Li-ion Battery, Integration of Electric Vehicle Charging Stations, Àrees temàtiques de la UPC::Enginyeria elèctrica, 621.3 - Enginyeria elèctrica. Electrotècnia. Telecomunicacions

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/694784
https://dx.doi.org/10.5821/dissertation-2117-433360

A FixMatch Framework for Alzheimer’s Disease Classification: Exploring the Trade-Off Between Supervision and Performance

Autoren: Hossain, Al Konok, Umme Hani Tahsin, Md et al.

Quelle: IEEE Access. 13:59531-59543

Schlagwörter: Alzheimer’s diseases, computer vision, supervised learning, semi-supervised learning, FixMatch, VGG-19, Cyber Security, Cybersäkerhet

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-112467
https://doi.org/10.1109/ACCESS.2025.3556964

Music identification with audio fingerprinting an industrial perspective

Autoren: Cortès Sebastià, Guillem

Weitere Verfasser: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: Molina Martínez, Emilio, Serra Casals, Xavier

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Audio fingerprinting, Music identification, Self-supervised learning, Identificació de música, Aprenentatge auto-supervisat, Empremtes digitals d’àudio

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/694234

Deepfake Audio Detection Using Generative Adversarial Network-Based Spatial–Temporal Feature Learning.

Autoren: Ranjan, Vivek Singh, Dayashankar

Quelle: International Journal of Software Engineering & Knowledge Engineering. Nov2025, p1-17. 17p. 6 Illustrations.

Schlagwörter: *CYBERTERRORISM, GENERATIVE adversarial networks, SUPERVISED learning, SPEECH synthesis, MACHINE learning, DEEPFAKES, FALSE personation, SPATIOTEMPORAL processes

Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem.

Autoren: Kang, Parminder Singh Bhawna, Bhawna

Quelle: Business Process Management Journal. 2025, Vol. 31 Issue 7, p2825-2848. 24p.

Schlagwörter: *RISK assessment, *SUPPLIERS, *SUPPLY chain management, *RESOURCE allocation, *SUPPLY chain disruptions, SUPERVISED learning, CLASSIFICATION

Combining Semi-Supervised Learning of Visual-Tactile Feature Fusion Shared Network for Object Recognition.

Autoren: Zhang, Peng Tang, Wenjing Chen, Zhenxue et al.

Quelle: International Journal of Image & Graphics. Oct2025, p1. 25p.

Schlagwörter: *ARTIFICIAL neural networks, SUPERVISED learning, RECOGNITION (Psychology), FEATURE extraction

Critical comparison of potential machine learning methods for lightning thermal damage assessment of composite laminates.

Autoren: Lee, Juhyeong Millen, Scott L.J. Xu, Xiaodong

Quelle: Advanced Composite Materials. Oct2025, Vol. 34 Issue 5, p809-829. 21p.

Schlagwörter: SUPERVISED learning, DAMAGE models, FIBROUS composites, ELECTRIC conductivity, MACHINE learning, LAMINATED materials

Lead Time Prediction for Inventory Optimization With Machine Learning.

Autoren: Reiners, Robin Haubitz, Christiane B Thonemann, Ulrich W

Quelle: Production & Operations Management. Oct2025, Vol. 34 Issue 10, p3010-3025. 16p.

Schlagwörter: *MANUFACTURING industries, *INVENTORY control, *DATA integrity, MACHINE learning, SUPERVISED learning, NUMERICAL analysis, PATTERN recognition systems

Fractional Cross-Validation for Optimizing Hyperparameters of Supervised Learning Algorithms.

Autoren: Yerramilli, Suraj Apley, Daniel W.

Quelle: Technometrics. Nov2025, Vol. 67 Issue 4, p683-692. 10p.

Schlagwörter: *MATHEMATICAL optimization, SUPERVISED learning, GAUSSIAN processes, MODEL validation

Supervised Learning with Inter- and Intra-Dependence in Multilayer Networks with Applications in Security Systems Analysis.

Autoren: Rodriguez-Acosta, Jose Guha, Sharmistha Gailliot, Samuel et al.

Quelle: Technometrics. Oct2025, p1-14. 14p. 5 Illustrations.

Schlagwörter: *SECURITY systems, *BAYESIAN analysis, SUPERVISED learning, SOCIAL network theory, PREDICTION models

Firma/Körperschaft: SANDIA National Laboratories

Few-shot learning for defect detection in manufacturing.

Autoren: Zajec, Patrik Rožanec, Jože M. Theodoropoulos, Spyros et al.

Quelle: International Journal of Production Research. Oct2024, Vol. 62 Issue 19, p6979-6998. 20p.

Schlagwörter: *ARTIFICIAL intelligence, *MANUFACTURING defects, MACHINE learning, SUPERVISED learning, INSPECTION & review, ACTIVE learning

A Gaussian Process-Regularized Graphical Learning Method for Distribution System State Estimation Using Extremely Scarce State Variable Labels

Autoren: Hu, Jiaxiang Xu, Qianwen

Quelle: IEEE Transactions on Smart Grid. 16(4):3359-3376

Schlagwörter: Estimation, Learning systems, Data models, State estimation, Training, Topology, Biological system modeling, Adaptation models, Uncertainty, Kernel, Distribution system state estimation, semi-supervised learning, Gaussian process, interval state estimation

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-370251
https://doi.org/10.1109/TSG.2025.3552958

A note on two novel easy-to-interpret feature effect measures for partial dependence plots in a classification setting

Autoren: Rosenblad, Andreas, fil. dr, docent, 1973

Quelle: Journal of Applied Statistics. :1-13

Schlagwörter: Black box model, effect measure, interpretable machine learning, odds ratio, relative risk, supervised learning models, Statistics, Statistik

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-566660
https://doi.org/10.1080/02664763.2025.2554823

An empirical evaluation of deep semi-supervised learning

Autoren: Fredriksson, Teodor Bosch, Jan Olsson, Helena H.

Quelle: International Journal of Data Science and Analytics Bayesian-Data-Analysis-of-Universal-Semi-Supervised-Benchmark. 20(4):4127-4148

Schlagwörter: Semi-supervised learning, Software engineering, Data labeling, Bayesian data analysis

Dateibeschreibung: electronic

Zugangs-URL: https://research.chalmers.se/publication/544964
https://research.chalmers.se/publication/544964/file/544964_Fulltext.pdf

Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction

Autoren: Orbay, Raik Wikner, Evelina

Quelle: IEEE Open Journal of Vehicular Technology. 6:348-358

Schlagwörter: Feature engineering, Energy consumption, Battery electric vehicles, Unsupervised/supervised learning, Driving style classification, Machine learning

Dateibeschreibung: electronic

Zugangs-URL: https://research.chalmers.se/publication/543996
https://research.chalmers.se/publication/543996/file/543996_Fulltext.pdf

Violin perfomance analysis using weak supervision

Autoren: Tamer, Nazif Can

Weitere Verfasser: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: Serra Casals, Xavier

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Music performance analysis, Pitch estimation, Automatic music transcription, Instrument playing technique, Violin, Weakly-supervised learning, Pseudo labeling, Self-training, Weak supervision, Análisis de interpretación musical, Estimación de tono, Transcripción automática de música, Técnica de interpretación instrumental, Violín, Pseudoetiquetado, Autoentrenamiento, Supervisión débil

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/692368

Deep learning: Exploration of models, emerging challenges and transformative applications.

Autoren: Singh, Gurvinder Markan, Rohit Verma, Vikas et al.

Quelle: AIP Conference Proceedings. 10/31/2025, Vol. 3343 Issue 1, p1-5. 5p.

Schlagwörter: *DEEP learning, *SUPERVISED learning, *SELF-organizing systems, *SCIENTIFIC models, *INFORMATION technology, *QUANTITATIVE research, *REINFORCEMENT learning

Exploring machine learning predictions in virtual healthcare: A survey.

Autoren: Dixit, Krishna Kant Gupta, Deepak Kumar Bhatt, Chandradeep et al.

Quelle: AIP Conference Proceedings. 2025, Vol. 3297 Issue 1, p1-8. 8p.

Schlagwörter: *MACHINE learning, *SUPERVISED learning, *PROGNOSIS, *MEDICAL records, *SYMPTOMS, *EARLY diagnosis, *MEDICAL care, *BOOSTING algorithms