Suchergebnisse - Computing methodologies Machine learning Machine learning algorithms Feature section*

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

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

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

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

3D Reconstruction based on multi-view stereo in the deep learning era: a survey and comparison of methods.

Autoren: Song, Boyang Wu, Bing Xiao, Jin et al.

Quelle: Visual Computer. Dec2025, Vol. 41 Issue 15, p12765-12810. 46p.

Schlagwörter: *DEEP learning, *DEPTH maps (Digital image processing), *EVALUATION methodology, *FEATURE extraction, *THREE-dimensional imaging, *TRANSFORMER models

Quantum machine learning-driven surrogate modeling for efficient multi-objective optimization of CO2 storage and geothermal energy extraction.

Autoren: Mohammadi, Babak Chen, Mingjie Nikoo, Mohammad Reza et al.

Quelle: Advances in Geo-Energy Research. Nov2025, Vol. 18 Issue 2, p137-152. 16p.

Schlagwörter: *MULTI-objective optimization, *QUANTUM computing, *CARBON sequestration, *OPTIMIZATION algorithms, *THERMAL gradient measurment, *GEOTHERMAL resources, *APPROXIMATION theory, *FEATURE selection

Unsupervised Machine Learning for Data-Driven Rock Mass Classification: Addressing Limitations in Existing Systems Using Drilling Data.

Autoren: Hansen, Tom F. Aarset, Arnstein

Quelle: Rock Mechanics & Rock Engineering. Oct2025, Vol. 58 Issue 10, p11261-11291. 31p.

Schlagwörter: *CLUSTERING algorithms, *DIMENSIONAL reduction algorithms, *GEOTECHNICAL engineering, *UNDERGROUND construction, *QUANTITATIVE research, *MATHEMATICAL optimization, *EMPIRICAL research

Precision Unveiled in Unborn: A Cutting-Edge Hybrid Machine Learning Approach for Fetal Health State Classification.

Autoren: Prachi Sabherwal P Agrawal M et al.

Quelle: Cardiovascular engineering and technology [Cardiovasc Eng Technol] 2025 Dec; Vol. 16 (6), pp. 641-662. Date of Electronic Publication: 2025 Aug 27.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer Country of Publication: United States NLM ID: 101531846 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1869-4098 (Electronic) Linking ISSN: 1869408X NLM ISO Abbreviation: Cardiovasc Eng Technol Subsets: MEDLINE

MeSH-Schlagworte: Machine Learning* , Heart Rate, Fetal* , Electrocardiography* , Fetal Monitoring*/methods , Signal Processing, Computer-Assisted* , Cardiotocography* , Deep Learning* , Heart Defects, Congenital*/physiopathology , Heart Defects, Congenital*/diagnosis , Fetal Heart*/physiopathology, Humans ; Pregnancy ; Female ; Predictive Value of Tests ; Reproducibility of Results

FUZZY SEA HORSE OPTIMIZATION ALGORITHM (FSHOA) AND QUINE MCCLUSKEY ENSEMBLE CLASSIFIER (QMEC) FOR HEART DISEASE PREDICTION.

Autoren: Manimozhi, V. Chitra, K.

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 4, p5310-5338, 29p

Schlagwörter: SEA horses, OPTIMIZATION algorithms, FEATURE selection, SUPPORT vector machines, MACHINE learning

Integrated Data-Driven Framework for Forecasting Tight Gas Production Based on Machine Learning Algorithms, Feature Selection and Fracturing Optimization.

Autoren: Yao, Fuyu Hui, Gang Meng, Dewei et al.

Quelle: Processes; Apr2025, Vol. 13 Issue 4, p1162, 23p

Schlagwörter: MACHINE learning, YOUNG'S modulus, DECISION making in investments, FEATURE selection, FLUID injection

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

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

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

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

Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review.

Autoren: Paz, Álex Crawford, Broderick Monfroy, Eric et al.

Quelle: Biomimetics (2313-7673); May2025, Vol. 10 Issue 5, p326, 46p

Schlagwörter: CREDIT analysis, CREDIT risk, MACHINE learning, METAHEURISTIC algorithms, PARTICLE swarm optimization, BIOLOGICALLY inspired computing

GENE CANCER CLASSIFICATION USING ENHANCED TRANSFERABLE REINFORCEMENT LEARNING AND ENHANCED RESNET ALGORITHM.

Autoren: B., Sanjay Krishna C., Agjelia Lydia

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 3, p4693-4721, 29p

Schlagwörter: MACHINE learning, REINFORCEMENT learning, DEEP learning, FEATURE selection, TUMOR classification

College student mental health assessment: Predictive models based on machine learning and feature importance analysis.

Autoren: Liu, Cheng Ji, Lin Lu, Juncheng et al.

Quelle: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 3, p1-21, 21p

Schlagwörter: MENTAL health, MACHINE learning, PSYCHOTHERAPY, COLLEGE students, PREDICTION models, QUANTITATIVE research, AFFECTIVE disorders, SYMPTOM Checklist-90-Revised

Development and Validation of a Machine Learning-Based Predictive Model for Assessing the Risk of Comorbid Depression in Patients With Asthma.

Autoren: Nie, Qiu Deng, Xu Chen, Xin et al.

Quelle: Alpha Psychiatry; Oct2025, Vol. 26 Issue 5, p1-14, 14p

Schlagwörter: ASTHMA, COMORBIDITY, CLINICAL medicine, PREDICTION models, MACHINE learning, MEDICAL records, FEATURE selection, ENSEMBLE learning

Deep Learning Espoused Imaging Modalities for Skin Cancer Diagnosis: A Review.

Autoren: Sumaiya, Noor Ali, Anooja

Quelle: Frontiers in Biomedical Technologies; Autumn2025, Vol. 12 Issue 4, p902-917, 16p

Schlagwörter: SKIN cancer, DIAGNOSIS, DIAGNOSTIC imaging, GENERATIVE adversarial networks, FEDERATED learning, DEEP learning, ARTIFICIAL neural networks, DERMATOLOGY

A Review of Machine Learning Algorithms for Biomedical Applications.

Autoren: Binson VA Thomas S Subramoniam M et al.

Quelle: Annals of biomedical engineering [Ann Biomed Eng] 2024 May; Vol. 52 (5), pp. 1159-1183. Date of Electronic Publication: 2024 Feb 21.

Publikationsart: Journal Article; Review

Info zur Zeitschrift: Publisher: Springer Science + Business Media Country of Publication: United States NLM ID: 0361512 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-9686 (Electronic) Linking ISSN: 00906964 NLM ISO Abbreviation: Ann Biomed Eng Subsets: MEDLINE

MeSH-Schlagworte: Algorithms* , Machine Learning*, Humans ; Neural Networks, Computer ; Support Vector Machine

DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging.

Autoren: Thanya, T. Jeslin, T.

Quelle: Abdominal Radiology. Sep2025, Vol. 50 Issue 9, p4181-4211. 31p.

Schlagwörter: *PANCREATIC tumors, *COMPUTED tomography, *DEEP learning, *FEATURE extraction, *MACHINE learning, *TUMOR classification, *OPTIMIZATION algorithms, *CLASSIFICATION

EfficientNet-based Cloud Network Infrastructure for Improved Early Detection of Breast Cancer.

Autoren: Sandhu, Jasjeet Kaur Kaur, Amandeep Sharma, Chetna

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 3, p4493-4531, 39p

Schlagwörter: MACHINE learning, CANCER diagnosis, EARLY detection of cancer, FEATURE extraction, INFRASTRUCTURE (Economics)

Predicting Diabetes Risk Using Machine Learning: A Comparative Study on the Yazd Health Study (YaHS).

Autoren: Sefid, Fateme Norouzi-Ghahjavarestani, Nazanin Soleymani-Tabasi, Malihe et al.

Quelle: Iranian Journal of Diabetes & Obesity (IJDO); Autumn2025, Vol. 17 Issue 3, p182-192, 11p

Schlagwörter: DIABETES, MACHINE learning, RANDOM forest algorithms, DATA scrubbing, SUPPORT vector machines, MEDICAL databases, RISK assessment, PUBLIC health research

Predictive Modelling Of Stress Levels: A Comparative Analysis Of Machine Learning Algorithms.

Autoren: Sasane, Sumit Mulla, Zameer Ahmed S.

Quelle: Journal of Advanced Zoology. 2024 Supplement, Vol. 45, p153-158. 6p.

Speech pattern disorders in verbally fluent individuals with autism spectrum disorder: a machine learning analysis.

Autoren: Hu, Chuanbo Thrasher, Jacob Li, Wenqi et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-13, 13p

Schlagwörter: AUTISM spectrum disorders, MACHINE learning, VOICE analysis, EVALUATION methodology, SPEECH disorders, SUPPORT vector machines, PROSODIC analysis (Linguistics), PHONETICS

Oscillometric blood pressure estimation using machine learning-based mapping of waveform features.

Autoren: Ezeddin, Maymouna Chowdhury, Moajjem Hossain Khandakar, Amith et al.

Quelle: Biomedical Engineering Letters; Nov2025, Vol. 15 Issue 6, p1123-1134, 12p