Suchergebnisse - Computing methodologies Machine learning Machine learning approaches Kernel methods*

Miscellaneous EEG Preprocessing and Machine Learning for Pilots' Mental States Classification: Implications

Autoren: Ibrahim Mohammad Alreshidi Irene Moulitsas Karl W. Jenkins

Quelle: 2022 The 6th International Conference on Advances in Artificial Intelligence. :29-39

Schlagwörter: Machine learning approaches, 03 medical and health sciences, Support vector machines, Hardware, 0302 clinical medicine, Machine learning, interfaces and storage, Kernel methods, Noise reduction, Communication hardware, Computing methodologies, Neural networks, Signal processing systems

Zugangs-URL: https://dspace.lib.cranfield.ac.uk/handle/1826/18784

Towards Automatic Generation of Performance Models for Dynamic Tuning using Machine Learning

Autoren: Alcaraz Rodriguez, Jordi

Thesis Advisors: jordi_alc@hotmail.com, Sikora, Anna, César Galobardes, Eduardo

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Aprenentatge automàtic, Aprendizaje automático, Machine learning, Eines de rendiment, Herramientas de rendimiento, Performance tools, Computació d'altes prestacions, Computación de altas prestaciones, High performance computing, Ciències Experimentals

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/675104

From Machine Learning to Ensemble Approaches: A Systematic Review of Mammogram Classification Methods.

Autoren: Fajrin, Hanifah Rahmi Min, Se Dong

Quelle: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 22, p2829, 30p

Schlagwörter: MACHINE learning, DEEP learning, EARLY detection of cancer, CLASSIFICATION, MAMMOGRAMS, COMPUTER-aided diagnosis, ENSEMBLE learning

Implementation of a Deep Learning System for Detection and Classification of Lumpy Skin Disease in Cattle: Enhancing Precision and Efficiency in Veterinary Diagnostics.

Autoren: Pal, Madhumita Behera, Soudamini Mohapatra, Ranjan K. et al.

Quelle: Veterinary Medicine & Science. Nov2025, Vol. 11 Issue 6, p1-20. 20p.

Schlagworte: Lumpy skin disease, Deep learning, Ensemble learning, Veterinary clinical pathology, Health of cattle, Transformer models, Machine learning

Enhancing DDoS Attacks Mitigation Using Machine Learning and Blockchain-Based Mobile Edge Computing in IoT.

Autoren: Chaira, Mahmoud Belhenniche, Abdelkader Chertovskih, Roman

Quelle: Computation; Jul2025, Vol. 13 Issue 7, p158, 17p

Schlagwörter: DENIAL of service attacks, MACHINE learning, INTERNET of things, EDGE computing, ELECTRONIC data processing, CYBERTERRORISM, SIGNAL detection, BLOCKCHAINS

Review and Evaluation of existing deep learning methods for Brain Tumor Detection.

Autoren: Nath, Prem Trivedi, Naresh Kumar

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 4, p622-630, 9p

Schlagwörter: MAGNETIC resonance imaging, BRAIN tumors, DEEP learning, MACHINE learning, COMPUTER-assisted image analysis (Medicine)

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

Approaches for Assessment of Soil Moisture with Conventional Methods, Remote Sensing, UAV, and Machine Learning Methods—A Review.

Autoren: Haokip, Songthet Chinnunnem Rajwade, Yogesh A. Rao, K. V. Ramana et al.

Quelle: Water (20734441); Aug2025, Vol. 17 Issue 16, p2388, 35p

Schlagwörter: SOIL moisture, MACHINE learning, HYDROLOGICAL research, REMOTE sensing, AGRICULTURAL drones, EMPIRICAL research, DATA acquisition systems, CROP yields

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 comparative study between time series and soft computing models for river discharge forecasting.

Autoren: Hamzeh, Haghiabi Amir Zahra, Askari Mohammad, Nazeri Tahroudi

Quelle: Applied Water Science; Nov2025, Vol. 15 Issue 11, p1-25, 25p

Schlagwörter: FORECASTING, MACHINE learning, TIME series analysis, RANDOM forest algorithms, STREAM measurements, K-nearest neighbor classification, LONG short-term memory

Geografische Kategorien: IRAN

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

Autoren: Borah K Das HS Seth S et al.

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

Publikationsart: Journal Article; Review

Info zur Zeitschrift: 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-Schlagworte: High-Throughput Nucleotide Sequencing*/methods , Machine Learning*, Humans ; Deep Learning

Predictive Fermentation Control of Lactiplantibacillus plantarum Using Deep Learning Convolutional Neural Networks.

Autoren: Wu, Chien-Chang Chen, Jung-Sheng Lu, Yu-Ching et al.

Quelle: Microorganisms; Nov2025, Vol. 13 Issue 11, p2601, 16p

Schlagwörter: DEEP learning, CONVOLUTIONAL neural networks, PROBIOTICS, CLASSIFICATION, MICROBIAL growth, LACTOBACILLUS plantarum, REAL-time computing, BIOTECHNOLOGICAL process control

CHRONIC KIDNEY DISEASE PREDICTION INTEGRATING CATEGORIZATION AND NOVEL MACHINE LEARNING TECHNIQUES.

Autoren: Lalitha, Ponnam Devika, Potta Manga, Vadthya et al.

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 3, p3588-3597, 10p

Schlagwörter: FISHER discriminant analysis, CHRONIC kidney failure, PRINCIPAL components analysis, SUPPORT vector machines, HAZARDOUS wastes

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

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

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

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

Video-Based Deep Learning Approach for Water Level Monitoring in Reservoirs.

Autoren: Jung, Wallpyo Kim, Jongchan Jo, Hyeontak et al.

Quelle: Water (20734441); Sep2025, Vol. 17 Issue 17, p2525, 16p

Schlagwörter: FLOOD forecasting, DEEP learning, VIDEO surveillance, LIQUID level indicators, WATER in agriculture, REAL-time computing, WATER pollution, IMAGE segmentation

Geografische Kategorien: SOUTH Korea

A multimodal approach for cardiac signals classification using deep learning with explainable AI methods.

Autoren: Alqudah, Ali Mohammad Alfraihat, Ausilah

Quelle: Health Information Science & Systems; 11/22/2025, Vol. 14 Issue 1, p1-30, 30p

Breast Cancer Classification with Various Optimized Deep Learning Methods.

Autoren: Güler, Mustafa Sart, Gamze Algorabi, Ömer et al.

Quelle: Diagnostics (2075-4418); Jul2025, Vol. 15 Issue 14, p1751, 22p

Schlagwörter: BREAST cancer, DEEP learning, IMAGE processing, TUMOR classification, CLASSIFICATION, DIAGNOSTIC specimens, ARTIFICIAL intelligence

Machine Learning‐Enhanced Modeling of Calcium Carbonate Nucleation in Porous Media Under Counter‐Diffusion Conditions.

Autoren: Peng, Haonan Rajyaguru, Ashish Curti, Enzo et al.

Quelle: Water Resources Research; Nov2025, Vol. 61 Issue 11, p1-23, 23p

Schlagwörter: CALCIUM carbonate, NUCLEATION, LATTICE Boltzmann methods, CRYSTAL structure, DIFFUSION coefficients, POROUS materials, MACHINE learning, TRANSPORT theory

Evaluation of corporate financial performance based on bionic algorithm and biomechanical analysis.

Autoren: Xu, Yi

Quelle: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 3, p1-18, 18p

Schlagwörter: FINANCIAL performance, CONVOLUTIONAL neural networks, DEEP learning, BIOLOGICALLY inspired computing, MATHEMATICAL optimization

Predicting Alzheimer's disease from environmental risk factors: An fMRI-based functional connectivity and advanced machine learning approach.

Autoren: Mohammadi, Shahriar Zarei, Soraya

Quelle: Journal of Environmental Health Science & Engineering; 11/8/2025, Vol. 23 Issue 2, p1-15, 15p

Schlagwörter: ALZHEIMER'S disease, MACHINE learning, FUNCTIONAL magnetic resonance imaging, ENSEMBLE learning, PREDICTION models, MEDICAL sciences, ENVIRONMENTAL risk, FUNCTIONAL connectivity