Výsledky vyhľadávania - "supervised and unsupervised learning"

Most influential feature form for supervised learning in voltage sag source localization

Autori: Mohammadi, Younes Polajžer, Boštjan Leborgne, Roberto Chouhy a ďalší

Zdroj: Engineering applications of artificial intelligence. 133(Part D)

Predmety: Voltage sag (dip), Source localization, Supervised and unsupervised learning, Convolutional neural network, Time-sample-based features

Popis súboru: electronic

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-223198
https://doi.org/10.1016/j.engappai.2024.108331

Detecting Mule Customer Accounts

Autori: DAFALLAH MOHAMMED, MOHAMMED

Predmety: Mule Accounts, Financial Crime, Anti-Money Laundering (AML), Machine Learning, Artificial Intelligence, Graph Analytics, Behavioral Modeling, Supervised Learning, Unsupervised Learning, Fraud Detection, Transaction Monitoring

Predicting Underwater Noise Spectra Dominated by Wind Turbine Contributions

Autori: Trucco, Andrea

Zdroj: IEEE Journal of Oceanic Engineering. 49:1675-1694

Predmety: Noise, Wind speed, Principal component analysis, Wind turbines, Vectors, Recording, Supervised learning, Offshore wind turbine, principal component analysis (PCA), spectral prediction, supervised and unsupervised learning, underwater noise

Prístupová URL adresa: https://hdl.handle.net/11567/1212835
https://doi.org/10.1109/joe.2024.3415753

ROLE OF ALGORITHMS IN MACHINE LEARNING

Autori: Choriyev, Anvar

Predmety: Machine Learning, Algorithms, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Performance, AutoML, Algorithm Selection, Neural Networks, Decision Trees, Clustering, Algorithmic Fairness, Explainable AI (XAI), Data Science

Detection of malware attacks in memory dump system by using machine learning technologies

Autori: Suresh Kumar V C Anbarasi

Predmety: Malware Detection, Memory Dump Analysis, Machine Learning, Cybersecurity, Digital Forensics, Pattern Recognition, Feature Extraction, Anomaly Detection, Supervised Learning, Unsupervised Learning

A Review of Machine Learning Approaches for Loan Approval in Banking

Autori: Journal of Global Research in Mathematical Archives

Predmety: Deep learning, supervised learning, unsupervised learning, reinforcement learning, fairness, data quality, model interpretability, explainable AI, automated systems, financial inclusion, machine learning, loan approval, banking, and ethical artificial intelligence

THEORETICAL ASPECTS OF MACHINE LEARNING IN STATISTICAL DATA PROCESSING

Autori: M.Mukhitdinova

Predmety: Abstract: Machine learning (ML) has emerged as a powerful tool in the field of statistical data processing, enabling the analysis and extraction of insights from complex datasets. This theoretical study explores the fundamental concepts of machine learning in the context of statistical data processing, highlighting its application in various domains such as data mining, predictive analytics, and pattern recognition. Key principles of ML, including supervised and unsupervised learning, model selection, overfitting, and validation techniques, are discussed in relation to statistical methods. The integration of machine learning algorithms with statistical models provides a robust framework for analyzing large-scale data, enhancing predictive accuracy and decision-making processes. Furthermore, the paper examines challenges associated with the implementation of ML in statistical data processing, such as data quality, computational complexity, and interpretability of results. Theoretical advancements in ML, such as deep learning and ensemble methods, are also reviewed for their impact on improving statistical models. The study concludes by emphasizing the growing importance of machine learning in modern statistical analysis and its potential to revolutionize data-driven decision-making. Keywords: machine learning, statistical learning, supervised learning, unsupervised learning, reinforcement learning, risk minimization, regularization, optimization, probabilistic models, deep learning

Applying Machine Learning in Science Education Research

Prispievatelia: Wulff, Peter Kubsch, Marcus Krist, Christina

Predmety: Machine learning, Natural language processing, science education research, supervised and unsupervised learning, Probabilistic modeling, artificial intelligence in Science education, Machine learning models, Human-machine interactions, Pattern recognition, computational grounded theory, reinforcement learning, deep neural networks, multimodal learning, transfer learning, qualitative and quantitative research methods, computer-aided tutoring, human-machine interaction, big data, complex systems theory, Teaching of a specific subject, Science: general issues, Study and learning skills: general

Popis súboru: application/pdf

Relation: Springer Texts in Education

Prístupová URL adresa: https://library.oapen.org/handle/20.500.12657/99864

Healthcare Predictive Modeling for Identifying Fraud in Medical Insurance Claims

Autori: N. Pegu*1, S. Seth2, S. Ramakrishnan3, A. Jangili4

Predmety: Fraud Detection, Medical Insurance, Medical Insurance Fraud, Explainable AI (XAI), Supervised and Unsupervised Learning, Predictive Modeling, SMOTE

AI driven fraud detection in digital payments using big data and machine learning

Autori: Kishore Challa

Predmety: Fraud Detection,Machine Learning,Digital Payments,Anomaly Detection,Big Data Analytics,Artificial Intelligence,Transaction Monitoring,Behavioral Analytics,Real-Time Detection,Predictive Modeling,Risk Scoring,Neural Networks,Supervised Learning,Unsupervised Learning,Data Stream Processing

A Review on the Integration of Machine Learning in Cloud Computing Resource Management

Autori: Thafzy V. M.

Predmety: computing, resource manage- ment, supervised learning, unsupervised learning, semisupervised learning, machine learning

Towards Attribution in Network Attacks: A Deep Learning-Based Robust Framework for Intrusion Detection and Adversarial Toolchain Identification

Autori: Bakht, Ahtesham Shah, Ankit Bastian, Nathaniel

Predmety: Cybersecurity in the Age of Artificial Intelligence, AI for Cybersecurity, and Cybersecurity for AI, adversarial attacks and evasion techniques, cyber threat detection and attribution, multi-line cyber defense framework, network intrusion detection systems (nids), supervised and unsupervised learning paradigms

Popis súboru: application/pdf

Relation: Proceedings of the 58th Hawaii International Conference on System Sciences; https://hdl.handle.net/10125/108882

Dostupnosť: https://hdl.handle.net/10125/108882
https://doi.org/10.24251/HICSS.2025.046

Clustering of precipitation features by a new validity index over Iran

Autori: Sanaz Moghim Reza Rajabi

Zdroj: Journal of Hydrology: Regional Studies, Vol 59, Iss , Pp 102421- (2025)

Predmety: Clustering, Supervised and unsupervised learning, Validity indices, SV index, Climate classification, Precipitation, Physical geography, GB3-5030, Geology, QE1-996.5

Popis súboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2214581825002460; https://doaj.org/toc/2214-5818

Prístupová URL adresa: https://doaj.org/article/0930f4426534424d98fab331b5288161

'Low Supervision' Deep Cluster Change Detection (CDCluster) On Remote Sensing RGB Data: Towards The Unsupervising Clustering Framework

Autori: Garcia, Guglielmo Fernandez de Gelis, Iris Corpetti, Thomas a ďalší

Prispievatelia: Garcia, Guglielmo Fernandez de Gelis, Iris Corpetti, Thomas a ďalší

Zdroj: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. :6656-6659

Predmety: [SDE] Environmental Sciences, Change Detection, Unsupervised Learning, Deep learning, Semi-supervised Learning, Change Detection Semi-supervised Learning Unsupervised Learning Clustering Multitemporal images Deep learning, Multitemporal images, Clustering

Popis súboru: application/pdf

Prístupová URL adresa: https://hal.science/hal-04309802v1/document
https://doi.org/10.1109/igarss52108.2023.10282898
https://hal.science/hal-04309802v1

Study of the relationship between competitiveness and digital footprint indicators in Valencian wineries

Autori: Castro, Leonardo Debón, Ana Domenech, Josep a ďalší

Prispievatelia: Castro, Leonardo Debón, Ana Domenech, Josep a ďalší

Zdroj: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Universitat Politècnica de València (UPV)

Predmety: Supervised and unsupervised learning, Digital footprint, Wineries, Web scraping, Competitiveness

Popis súboru: application/pdf

Prístupová URL adresa: https://riunet.upv.es/handle/10251/201684
https://hdl.handle.net/10251/201684
https://doi.org/10.4995/carma2023.2023.16434

Comparing supervised and unsupervised approaches to multimodal emotion recognition

Autori: Fernández Carbonell, Marcos Boman, Magnus Laukka, Petri

Zdroj: PeerJ Computer Science. 7

Predmety: affective computing, facial expression, multimodal emotion recognition, supervised and unsupervised learning, vocal expression, Psychology, psykologi

Popis súboru: print

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-200256
https://doi.org/10.7717/peerj-cs.804

Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters

Autori: Karen Rischmüller Vanessa Caton Markus Wolfien a ďalší

Zdroj: Front Nutr
Frontiers in Nutrition, Vol 11 (2024)

Predmety: 0301 basic medicine, 0303 health sciences, decision trees, Nutrition. Foods and food supply, GLIM criteria, supervised and unsupervised learning, malnutrition, ddc, 03 medical and health sciences, machine learning, gastrointestinal diseases, Nutrition, liver cirrhosis, TX341-641

Popis súboru: application/pdf

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39726873
https://doaj.org/article/6eb34c57c0f74e03991a7c9157902fa5
https://mediatum.ub.tum.de/1766283

Machine learning applications in stroke medicine: advancements, challenges, and future prospectives

Autori: Daidone, Mario Ferrantelli, Sergio Tuttolomondo, Antonino

Zdroj: Neural Regen Res

Predmety: cerebrovascular disease, machine learning, reinforcement learning, stroke, stroke therapy, supervised learning, unsupervised learning, reinforcement learning, machine learning, stroke therapy, Review, unsupervised learning, stroke, supervised learning, 3. Good health, cerebrovascular disease

Popis súboru: application/pdf

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/37843210
https://hdl.handle.net/10447/665678
https://hdl.handle.net/10447/665678
https://doi.org/10.4103/1673-5374.382228

Efficient Hybrid Model for Intrusion Detection Systems

Autori: Kaaniche, Nesrine Boudguiga, Aymen Gonzalez-Granadillo, Gustavo

Prispievatelia: Kaaniche, Nesrine Boudguiga, Aymen Gonzalez-Granadillo, Gustavo

Zdroj: INSTICC Conference, Proceedings of the 19th International Conference on Security and Cryptography
Proceedings of the 19th International Conference on Security and Cryptography

Predmety: IDS K-Means Bayesian Model Hybrid Approach Supervised and Unsupervised Learning, Bayesian Model, Supervised and Unsupervised Learning, K-Means, Hybrid Approach, IDS, [INFO] Computer Science [cs]

Popis súboru: application/pdf

Prístupová URL adresa: https://hal.science/hal-04137867v1/document
https://doi.org/10.5220/0011328300003283
https://hal.science/hal-04137867v1

Machine Learning-Driven Intrusion Detection and Identification in Industrial Control Systems

Autori: Dehlaghi Ghadim, Alireza Moslemzade, Mona Dharmapal, Nima Pattiyampully a ďalší

Zdroj: Proceedings - 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025. :552-559

Predmety: Computer control systems, Network intrusion, Parameter estimation, Self-supervised learning, State estimation, Zero-day attack, Anomaly detection, Attack detection, Industrial control systems, Intrusion Detection Systems, Intrusion-Detection, Machine-learning, Semi-supervised, Semi-supervised learning, Sequence-based anomaly detection, Supervised and unsupervised learning

Popis súboru: print

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-78563