Suchergebnisse - Extended fast relevance vector regression algorithm~

Incremental fast relevance vector regression model based multi-pollutant emission prediction of biomass cogeneration systems

Autoren: Wang, Xiuli Sun, Zhifei He, Defeng et al.

Index Begriffe: QA76.87 Neural computers, neural networks, Article, PeerReviewed

URL: https://kar.kent.ac.uk/106172/
https://doi.org/10.1016/j.conengprac.2024.105986
https://kar.kent.ac.uk/106172
https://doi.org/10.1016/j.conengprac.2024.105986
10.1016/j.conengprac.2024.105986

Revealing accuracy in climate dynamics: enhancing evapotranspiration estimation using advanced quantile regression and machine learning models.

Autoren: Sharafi, Saeed Mohammadi Ghaleni, Mehdi

Quelle: Applied Water Science; Jul2024, Vol. 14 Issue 7, p1-22, 22p

Schlagwörter: MACHINE learning, QUANTILE regression, EVAPOTRANSPIRATION, LEAST squares, RANDOM forest algorithms

Geografische Kategorien: IRAN

A Machine Learning Approach to Predicting the Turbidity from Filters in a Water Treatment Plant.

Autoren: Kwarko-Kyei, Joseph Tornyeviadzi, Hoese Michel Seidu, Razak

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 20, p2938, 21p

Schlagwörter: TURBIDITY, WATER quality management, PROCESS optimization, ARTIFICIAL intelligence, SAND filtration (Water purification), WATER purification, MACHINE learning

Photoplethysmography based non-invasive blood glucose estimation using systolic-diastolic framing MFCC features and machine learning regression.

Autoren: Kermani, Ali Esmaeili, Hossein

Quelle: BioImpacts; Apr2025, Vol. 15 Issue 2, p1-18, 18p

Schlagwörter: PHOTOPLETHYSMOGRAPHY, BLOOD sugar analysis, MACHINE learning, HEART beat, SUPPORT vector machines, PATIENT monitoring, FEATURE extraction

Enhancing the Accuracy of NIRS Prediction with Adaptive Machine Learning for Assessing Fermented Citronella Feed Quality.

Autoren: Samadi Kaloudis, E. Wahyudi, I. et al.

Quelle: Tropical Animal Science Journal. Sep2025, Vol. 48 Issue 5, p440-449. 10p.

Estimating body weight and body condition score of mature beef cows using depth images.

Autoren: Xiong, Yijie Condotta, Isabella C F S Musgrave, Jacki A et al.

Quelle: Translational Animal Science. 2023, Vol. 7 Issue 1, p1-14. 14p.

Schlagworte: Body weight, Cows, Animal herds, Feature selection, Agriculture, Machine learning, Beef cattle

Predicting weaning weight in Hair goat kids: a comparative analysis of tree-based machine learning approaches using environmental and herd data.

Autoren: Esener, Necati Kal, Yavuz

Quelle: Animal Production Science. 2025, Vol. 65 Issue 9, p1-12. 12p.

A systematic review linking welfare and automated analysis of acoustic signals in broiler chickens and layers.

Autoren: Soster, Patricia Devos, Paul Smets, Rutger et al.

Quelle: World's Poultry Science Journal. Dec2025, Vol. 81 Issue 4, p1049-1080. 32p.

2025 ACVIM Forum Research Abstract Program.

Quelle: Journal of Veterinary Internal Medicine. Nov2025, Vol. 39 Issue 6, p1-193. 193p.

Preventive Immunology for Livestock and Zoonotic Infectious Diseases in the One Health Era: From Mechanistic Insights to Innovative Interventions.

Autoren: Marzouk, Eman Alajaji, Ahmed I.

Quelle: Veterinary Sciences. Oct2025, Vol. 12 Issue 10, p1014. 42p.

A methodological approach to age estimation of the intra‐puparial period of the forensically relevant blow fly Calliphora vicina via Fourier transform infrared spectroscopy.

Autoren: Thümmel, Luise Tintner‐Olifiers, Johannes Amendt, Jens

Quelle: Medical & Veterinary Entomology. Mar2025, Vol. 39 Issue 1, p22-32. 11p.

Schlagworte: Forensic entomology, Fourier transform infrared spectroscopy, Maggots, Insect metamorphosis, Blowflies, Machine learning, Forensic sciences, Fourier transform spectroscopy

Escherichia coli is implicated in the development and manifestation of host susceptibility to the roundworm Trichostrongylus colubriformis infections in sheep.

Autoren: Liu, Fang McNally, Jody Flemming, Damarius et al.

Quelle: Veterinary Research. 7/1/2025, Vol. 56 Issue 1, p1-14. 14p.

Schlagworte: Escherichia coli, Medical sciences, Medical microbiology, Life sciences, Medical genetics

Nano-carrier-based Delivery of CRISPR-Cas9 for Oncolytic Gene Therapy: Insights from Xenograft Models.

Autoren: Bahmani, Mehran¹ mehranbahmani4521@gmail.com

Quelle: Journal of Lab Animal Research (JLAR). 2025, Vol. 4 Issue 2, p12-21. 10p.

Machine learning algorithms predict canine structural epilepsy with high accuracy.

Autoren: Flegel, Thomas Neumann, Anja Holst, Anna-Lena et al.

Quelle: Frontiers in Veterinary Science. 2024, p1-9. 10p.

Relationship Between Body Composition and Biomarkers in Adult Females with Breast Cancer: 1-Year Follow-Up Prospective Study.

Autoren: Larrad-Sáinz, Angélica Hernández Núñez, María Gemma Barabash Bustelo, Ana et al.

Quelle: Nutrients. Aug2025, Vol. 17 Issue 15, p2487. 21p.

Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine.

Autoren: Bonakdari, Hossein Ebtehaj, Isa Samui, Pijush et al.

Quelle: Water Resources Management; Sep2019, Vol. 33 Issue 11, p3965-3984, 20p

Schlagwörter: WATER levels, KRIGING, MACHINE learning, WATER supply management, INFORMATION resources management, LAKES

Linking ringed seal foraging behaviour to environmental variability.

Autoren: Nykänen, Milaja Niemi, Marja Biard, Vincent et al.

Quelle: Movement Ecology. 4/25/2025, Vol. 13 Issue 1, p1-18. 18p.

Predicting Properties of Imidazolium-Based Ionic Liquids via Atomistica Online: Machine Learning Models and Web Tools.

Autoren: Armaković, Stevan Armaković, Sanja J.

Quelle: Computation; Sep2025, Vol. 13 Issue 9, p216, 21p

Schlagwörter: IONIC liquids, MACHINE learning, OPEN scholarship, DESCRIPTOR systems, IMIDAZOLES, VISCOSITY

Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.

Autoren: Alberts, Famke Berke, Olaf Rocha, Leilani et al.

Quelle: Frontiers in Veterinary Science. 2024, p1-14. 14p.

Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models.

Autoren: Deo, Ravinesh Samui, Pijush Kim, Dookie

Quelle: Stochastic Environmental Research & Risk Assessment; Aug2016, Vol. 30 Issue 6, p1769-1784, 16p

Schlagwörter: EVAPORATION (Meteorology), HUMIDITY research, FORECASTING, MACHINE learning, SPLINE theory