Výsledky vyhľadávania - "Machine learning models"

Sample Selection Methods for Federated Continual Learning in a Time Series Context

Autori: Ickin, Selim Lan, Xiaoyu Angelova, Milena a ďalší

Zdroj: IEEE Access. 13:189057-189073

Predmety: AI-native networks, AI/ML model lifecycle management, catastrophic forgetting, concept drift, Federated continual learning, future mobile networks, sample selection, time series data, Complex networks, Data aggregation, Data reduction, Federated learning, Information management, Learning systems, Life cycle, Mobile telecommunication systems, Reconfigurable hardware, Sampling, Spatio-temporal data, Time series, Time varying networks, AI-native network, AI/machine learning model lifecycle management, Concept drifts, Continual learning, Future mobile network, Lifecycle management, Machine learning models, Samples selection, Time-series data, Behavioral research, Digital storage

Popis súboru: electronic

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-28916
https://doi.org/10.1109/ACCESS.2025.3628353

Seasonal heatwave forecasting with explainable machine learning and remote sensing data

Autori: Kan, Jung-Ching Vieira Passos, Marlon Destouni, Georgia a ďalší

Zdroj: Stochastic environmental research and risk assessment (Print). 39:3333-3352

Predmety: Atmospheric climate factors, Explanatory-predictive factors, Geopotential height, Landscape factors, Machine-learning models, Summer heatwaves

Popis súboru: print

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-244395
https://doi.org/10.1007/s00477-025-03020-1

Integrating environmental clustering to enhance epidemic forecasting with machine learning models

Autori: Yosra Didi Ahlam Walha Ali Wali

Zdroj: International Journal of Cognitive Computing in Engineering, Vol 6, Iss, Pp 628-642 (2025)

Predmety: COVID-19 forecasting, Electronic computers. Computer science, Science, Climatic variables, Air pollution features, Machine learning models, QA75.5-76.95, Prediction, Clustering

Prístupová URL adresa: https://doaj.org/article/0c220924a38b4ca285dd225bf268a1db

Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda

Autori: Jean Marie Vianney Ntamwiza Hannibal Bwire

Zdroj: Transport Economics and Management, Vol 3, Iss, Pp 35-45 (2025)

Predmety: Transportation engineering, TA1001-1280, Docked bike-sharing, Machine learning models, Spatial analysis, Prediction, Weather, Transportation and communications, Urban transport, HE1-9990

Prístupová URL adresa: https://doaj.org/article/223424800dab4b91910d85485adda45e

A GIS-portal platform from the data perspective to energy hub digitalization solutions- A review and a case study

Autori: Majidi Nezhad, Meysam Moradian, Sogol Guezgouz, Mohammed a ďalší

Zdroj: Renewable & sustainable energy reviews. 223

Predmety: Coupled Model Intercomparison Project Phase 6 (CMIP6), Digital elevation model, Digitalization, Energy hub, Flexible platforms, Geospatial machine learning models, GIS web portal platform, Innovative actions, Wind energy digitalization

Popis súboru: print

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-245532
https://doi.org/10.1016/j.rser.2025.116019

Enhancing the accuracy of wind power projections under climate change using geospatial machine learning models

Autori: Moradian, S. Gharbia, S. Majidi Nezhad, Meysam a ďalší

Zdroj: Energy Reports. 12:3353-3363

Predmety: Artificial intelligence, Climate change, Machine learning, Wind energy, Wind power, Adversarial machine learning, Wind forecasting, Energy, Geo-spatial, Machine learning models, Machine-learning, Neural-networks, Power, Power projection, Projection maps, Support vector regression models, Wind speed, Windmill

Popis súboru: print

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-68528
https://doi.org/10.1016/j.egyr.2024.09.007

Assessing the efficacy of various predictive models in simulating monthly reference evapotranspiration patterns and its impact on water resource management for agriculture in the Kebir-West watershed, North-East of Algeria

Autori: Rayane Saci Mehdi Keblouti Okan Mert Katipoğlu a ďalší

Zdroj: Journal of Hydrology and Hydromechanics. 73:284-294

Predmety: Penman-Monteith FAO-56, Algeria, Machine learning models, Reference evapotranspiration ET0, Kebir-West watershed

Prístupová URL adresa: https://sciendo.com/article/10.2478/johh-2025-0022
https://doi.org/10.2478/johh-2025-0022

A comparative analysis of perceptions of insecurity in Milan and Beijing metro stations

Autori: Jiaxin Liu Hongming Yan Marcus White a ďalší

Zdroj: Frontiers of Architectural Research, Vol 14, Iss 4, Pp 863-884 (2025)

Predmety: Metro station, Architecture, Machine learning models, Perceptions of insecurity, Underground public space, Visual factors, NA1-9428, XGBoost

Prístupová URL adresa: https://doaj.org/article/3fb2b0d3e58d45d0b82e12966815699a

Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique

Autori: Gülüzar Çit Azhar Alyahya

Zdroj: Sakarya University Journal of Computer and Information Sciences, Vol 8, Iss 2, Pp 212-222 (2025)
Volume: 8, Issue: 2212-222
Sakarya University Journal of Computer and Information Sciences

Predmety: Computer Software, Machine learning models, Random Forest regressor, Extremely Randomized Trees Regressor, K- Nearest Neighbors, Gradient boosting regressor, Stacking model, stacking model, Electronic computers. Computer science, gradient boosting regressor, extremely randomized trees regressor, QA75.5-76.95, Information technology, machine learning models, k- nearest neighbors, T58.5-58.64, Bilgisayar Yazılımı, random forest regressor

Popis súboru: application/pdf

Prístupová URL adresa: https://doaj.org/article/475620fbcd5d4233a36666b64ac43d3d
https://dergipark.org.tr/tr/pub/saucis/issue/92436/1579599

Verifiable Boosted Tree Ensembles

Autori: Calzavara S. Cazzaro L. Lucchese C. a ďalší

Zdroj: 2025 IEEE Symposium on Security and Privacy (SP). :2057-2075

Predmety: Computer Science - Machine Learning, Computer Science - Logic in Computer Science, Computer Science - Cryptography and Security, Machine Learning and Security, Program Analysis for Machine Learning Models, Robustness, Verification, Statistics - Machine Learning

Popis súboru: application/pdf

Prístupová URL adresa: http://arxiv.org/abs/2402.14988

Makine Öğrenimi ve Çok Boyutlu Anket Verileri Kullanılarak Öğrenci Başarısının Tahmini: Eğitim Programı Üzerine Bir Uygulama

Autori: Behsi, Zeynep Dereli, Serhan Çakar, Tuna a ďalší

Predmety: Survey Data, Adaboost, K-Means++ Clustering, K-Means Clustering, Machine-Learning, Learning Systems, Student Success, LightGBM, Smote, Lightgbm, Student Success Prediction, Machine Learning, Case-Studies, Educational Program, Education Computing, Logistic Regression, Students, SMOTE, Machine Learning Models, Forecasting

Prístupová URL adresa: https://hdl.handle.net/20.500.11779/3102

Assessing the color status and daylight chromaticity of road signs through machine learning approaches

Autori: Saleh, Roxan Fleyeh, Hasan, Associate professor Alam, Moudud a ďalší

Zdroj: IATSS Research. 47(3):305-317

Predmety: Classification, Daylight chromaticity, Machine learning algorithms, Prediction, Regression, Road signs, Accident prevention, Color, Forecasting, Forestry, Learning algorithms, Learning systems, Motor transportation, Regression analysis, Roads and streets, Support vector machines, Color levels, Machine learning models, Random forests, Regression modelling, Road safety, Supervised machine learning, Neural networks

Popis súboru: electronic

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:du-46627
https://doi.org/10.1016/j.iatssr.2023.06.003

EXPLORING THE POTENTIAL OF ARTIFICIAL INTELLIGENCE TO PREDICT CYBER ATTACKS: CREATION, EVALUATION AND COMPARATIVE ANALYSIS OF EFFECTIVE MODELS OF FINE-TUNING, RANDOM FORESTS, AND NEURAL NETWORKS

Autori: Stefanov, Miroslav Jekov, Boyan Titov, Tito a ďalší

Zdroj: ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference; Vol. 5 (2025): Environment. Technology. Resources. Proceedings of the 16th International Scientific and Practical Conference. Volume 5; 255-263

Predmety: Cybersecurity, Artificial Intelligence, Cyber Attack Prediction, Anomaly Detection, Machine Learning Models

Popis súboru: application/pdf

Prístupová URL adresa: https://journals.ru.lv/index.php/ETR/article/view/8504
https://journals.rta.lv/index.php/ETR/article/view/8504

On exploring features engineering and machine learning algorithms in the domain of clinical-psychological

Autori: Baiyang Liu Da He Jia Li a ďalší

Zdroj: Alexandria Engineering Journal, Vol 123, Iss, Pp 488-496 (2025)

Predmety: Feature engineering, Machine learning models, Clinical-psychological, TA1-2040, Engineering (General). Civil engineering (General), Predictive modeling

Prístupová URL adresa: https://doaj.org/article/6df44b9ff86c46b28aef2188360429f4

Flood hazard zones prediction using machine-learning-based geospatial approach in lower Niger River basin, Nigeria

Autori: Adedoyin Benson Adeyemi Akinola Adesuji Komolafe

Zdroj: Natural Hazards Research, Vol 5, Iss 2, Pp 399-412 (2025)

Predmety: QE1-996.5, Flooding, Lower Niger river basin, QC801-809, Flood hazard prediction, Geophysics. Cosmic physics, Machine learning models, Geology, Geographic information system

Prístupová URL adresa: https://doaj.org/article/07ba912bde784531b05756bf7dc23311

A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests

Autori: Subhadip Bandyopadhyay Joy Bose Sujoy Roy Chowdhury

Zdroj: International Journal of Mathematical, Engineering and Management Sciences, Vol 10, Iss 3, Pp 777-796 (2025)

Predmety: telecom network monitoring, FOS: Computer and information sciences, Technology, Computer Science - Machine Learning, real-time anomaly detection, I.2.6, I.2.7, G.3, 62M10, 62P30, 68T07, data drift detection, H.2.8, H.3.3, Machine Learning (cs.LG), hybrid machine learning models, ai powered data drift detection, QA1-939, hierarchical temporal memory (htm), time series, Mathematics, sequential probability ratio test (sprt), streaming data analysis

Prístupová URL adresa: http://arxiv.org/abs/2504.18599
https://doaj.org/article/64d306aa2fe347eb8fa0d5c9f0c2af63
https://doi.org/10.33889/IJMEMS.2025.10.3.039

Financial engineering and the digital economy: The implementations of machine learning algorithms

Autori: Pingping Fu Honghao Yang Wenhao Qian a ďalší

Zdroj: Alexandria Engineering Journal, Vol 125, Iss, Pp 311-319 (2025)

Predmety: Stock market, Machine learning models, Financial engineering, TA1-2040, Engineering (General). Civil engineering (General), Digital economy, Forecasting

Prístupová URL adresa: https://doaj.org/article/f8e3546cffd144e9bbad8c62e90fe5cf

Generative AI and mental health: A new frontier of possibilities

Autori: Khaitan, Aryan Gupta, Shubham Maheshwari, Arun a ďalší

Zdroj: International Journal of Science and Research Archive. 15:139-145

Predmety: Cognitive Behavioral Therapy (CBT), AI-Based Mental Health Support, Conversational Agents (Cas), Google Dialogflow, Personalized Interactions, Machine Learning Models

A Comparative Study of Machine Learning Models for Short-Term Load Forecasting

Autori: Etna Vianita Henri Tantyoko

Zdroj: Jurnal Masyarakat Informatika, Vol 16, Iss 1, Pp 91-103 (2025)

Predmety: Information technology, T58.5-58.64, short-term load forecasting, machine learning models, lag features, electricity demand prediction, model evaluation

Prístupová URL adresa: https://doaj.org/article/87565b8bd41641aa8b96e28c396d17d4

Predicting Financial Market Volatility with Modern Model and Traditional Model

Autori: R. G. Aldeki

Zdroj: Финансы: теория и практика, Vol 29, Iss 2, Pp 154-165 (2025)

Predmety: arch, garch-svr, garch, volatility forecasting, HG1-9999, classical volatility models, hybrid model, machine learning models, support vector regression, Finance

Prístupová URL adresa: https://doaj.org/article/44b1f653ebd345abb08e103c58213788