Search Results - Computing methodologies Machine learning Machine learning approaches Rule learning

Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records.

Authors: Kong, Chun Yin Vasquez, Picasso Farhoodimoghadam, Makan et al.

Source: BMC Veterinary Research. 10/2/2025, Vol. 21 Issue 1, p1-12. 12p.

Subjects: Machine learning, Electronic health records, Open source software, Patient care, Veterinary medicine, Diagnosis, Classification algorithms, Synchronization

A survey on novel approach to semantic computing for domain specific multi-lingual man-machine interaction.

Authors: Bohra, Anjali Barwar, Nemi Chand

Source: International Journal of Informatics & Communication Technology (IJ-ICT); Apr2025, Vol. 15 Issue 1, p1-10, 10p

Subject Terms: NATURAL language processing, SEMANTIC computing, MACHINE translating, QUESTION answering systems, MACHINE learning

Combined Genome and Protein Statistical Features Improved the Prediction of Genes Encoding Antimicrobial Peptides: A Machine Learning Based Approach.

Authors: Rasani, M. Karami, K. Nassiri, M. et al.

Source: Iranian Journal of Applied Animal Science. Spring2025, Vol. 15 Issue 2, p191-209. 19p.

Subjects: Antimicrobial peptides, Machine learning, Drug development, Naive Bayes classification, Genetic markers, Statistics

Comparison of Tree‐Based Machine Learning Algorithms for Classification of Livestock Breeds Based On Post‐Thaw Spermatological Parameters.

Authors: Özen, Doğukan Özen, Hülya Gül, Elif Bersu et al.

Source: Veterinary Medicine & Science. Sep2025, Vol. 11 Issue 5, p1-10. 10p.

Subjects: Livestock breeds, Machine learning, Semen analysis, CART algorithms, Classification, Cattle breeds

Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms.

Authors: Eroğlu, Mehmet Turgut, Ali Osman Küçük, Mürsel et al.

Source: Veterinary Medicine & Science. Jul2025, Vol. 11 Issue 4, p1-8. 8p.

Subjects: Machine learning, Animal breeding, Animal health, Artificial selection of animals, Random forest algorithms

Deep learning-based evaluation of the severity of mitral regurgitation in canine myxomatous mitral valve disease patients using digital stethoscope recordings.

Authors: Lee, Soh-Yeon Lee, Sully Kim, Se-Hoon et al.

Source: BMC Veterinary Research. 5/8/2025, Vol. 21 Issue 1, p1-15. 15p.

Subjects: Convolutional neural networks, Mitral valve insufficiency, Deep learning, Congestive heart failure, Electronic records

Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.

Authors: Akbarein, Hesameddin Taaghi, Mohammad Hussein Mohebbi, Mahyar et al.

Source: Veterinary Medicine & Science. May2025, Vol. 11 Issue 3, p1-29. 29p.

Subjects: Machine learning, Artificial intelligence, Veterinary medicine, Resource allocation, Medical research

Deep learning framework for vertebral heart size and cardiothoracic ratio estimation in dogs and cats using thoracic radiographs.

Authors: Mekonnen, Habtamu Tilahun Puig, Núria Elson, Alejandro et al.

Source: Frontiers in Veterinary Science. 2025, p1-16. 16p.

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

Authors: Esener, Necati Kal, Yavuz

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

Evaluating the efficacy of bioelectrical impedance analysis using machine learning models for the classification of goats exposed to Haemonchosis.

Authors: Siddique, Aftab Batchu, Phaneendra Shaik, Arshad et al.

Source: Frontiers in Veterinary Science. 2025, p1-11. 11p.

Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count.

Authors: Pan, Lihong Chen, Xiao Han, Ding et al.

Source: Frontiers in Veterinary Science. 2025, p1-9. 9p.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Authors: Borah K Das HS Seth S et al.

Source: Functional & integrative genomics [Funct Integr Genomics] 2024 Aug 19; Vol. 24 (5), pp. 139. Date of Electronic Publication: 2024 Aug 19.

Publication Type: Journal Article; Review

Journal Info: Publisher: Springer Country of Publication: Germany NLM ID: 100939343 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-7948 (Electronic) Linking ISSN: 1438793X NLM ISO Abbreviation: Funct Integr Genomics Subsets: MEDLINE

MeSH Terms: High-Throughput Nucleotide Sequencing*/methods , Machine Learning*, Humans ; Deep Learning

Explainable Machine Learning Framework for Milk Quality Grading.

Alternate Title: Süt Kalitesi Derecelendirmesi için Açıklanabilir Makine Öğrenimi Çerçevesi.

Authors: ÇETİNTAV, Bekir YALÇIN, Ahmet

Source: Kocatepe Veterinary Journal / Kocatepe Veteriner Dergisi. Sep2025, Vol. 18 Issue 3, p227-235. 9p.

Machine-learning for quantitative histopathology of piglet intestinal tissues: challenges with limited training data.

Authors: Becker, Cecilie Brandt Hansen, Mette Sif Nielsen, Søren Saxmose et al.

Source: Frontiers in Veterinary Science. 2025, p1-11. 11p.

AI Reasoning in Deep Learning Era: From Symbolic AI to Neural–Symbolic AI.

Authors: Liang, Baoyu Wang, Yuchen Tong, Chao

Source: Mathematics (2227-7390); Jun2025, Vol. 13 Issue 11, p1707, 42p

Subject Terms: ARTIFICIAL intelligence, LOGIC programming, MATHEMATICAL logic, DEEP learning, BRITTLENESS, SCALABILITY

Deep-learning based morphological segmentation of canine diffuse large B-cell lymphoma.

Authors: Ancheta, Kenneth Psifidi, Androniki Yale, Andrew D. et al.

Source: Frontiers in Veterinary Science. 2025, p1-16. 16p.

2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project.

Authors: Bianco, Rachele Coluccia, Sergio Marinoni, Michela et al.

Source: Nutrients. Jul2025, Vol. 18 Issue 13, p2196. 23p.

Machine learning-based early prediction of growth and morphological traits at yearling age in pure and hybrid goat offspring.

Authors: Erduran, Hakan Esener, Necati Keskin, İsmail et al.

Source: Tropical Animal Health & Production. Nov2024, Vol. 56 Issue 8, p1-13. 13p.

Explainable and interpretable machine learning and data mining.

Authors: Atzmueller, Martin Fürnkranz, Johannes Kliegr, Tomáš et al.

Source: Data Mining & Knowledge Discovery; Sep2024, Vol. 38 Issue 5, p2571-2595, 25p

Subject Terms: MACHINE learning, ARTIFICIAL intelligence, DATA mining, BLENDED learning, AGRICULTURE

Developing diagnostic tools for canine periodontitis: combining molecular techniques and machine learning models.

Authors: Ruparell, Avika Gibbs, Matthew Colyer, Alison et al.

Source: BMC Veterinary Research. 9/18/2023, Vol. 19 Issue 1, p1-12. 12p.

Subjects: Machine learning, Periodontitis, Porphyromonas gingivalis, Periodontal probe, Periodontal disease, Actinobacillus actinomycetemcomitans, Polymerase chain reaction, Nucleotide sequencing