Suchergebnisse - 2012 ACM Subject Classification Design and analysis of algorithms Average Complexity

On the average complexity of Berge Algorithm ; Sur la complexité moyenne de l'algorithme de Berge

Autoren: David, Julien Gholami, Mostafa Lhote, Loïck et al.

Weitere Verfasser: David, Julien Gholami, Mostafa Lhote, Loïck et al.

Quelle: https://hal.science/hal-04637413 ; 2025.

Schlagwörter: 2012 ACM Subject Classification Design and analysis of algorithms Average Complexity, Minimal Transversal, Berge Algorithm, Hypergraphs, Analytic Combinatorics Digital Object Identifier, [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]

Verfügbarkeit: https://hal.science/hal-04637413
https://hal.science/hal-04637413v1/document
https://hal.science/hal-04637413v1/file/Soumis%20%C3%A0%20SODA%20-%20On%20the%20average%20complexity%20of%20Berge%20Algorithm%20%281%29.pdf

Research on the classification of EEG signals for dementia and its interpretability using the GWOCS agorithm.

Autoren: Wang, Ruofan Xu, Haojie Ma, Yijia et al.

Quelle: Cognitive Neurodynamics; 11/10/2025, Vol. 20 Issue 1, p1-30, 30p

Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms.

Autoren: Şengül, Ömer Çelik, Şenol

Quelle: Archives Animal Breeding / Archiv Tierzucht. 2025, Vol. 68 Issue 2, p325-337. 13p.

Schlagworte: Standard deviations, Regression trees, Random forest algorithms, Data mining, Lambs

The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review.

Autoren: Grzesiak, Wilhelm Zaborski, Daniel Pluciński, Marcin et al.

Quelle: Animals (2076-2615); Jul2025, Vol. 15 Issue 14, p2033, 38p

Schlagwörter: MACHINE learning, DAIRY cattle, AGRICULTURE, DATA analysis, CLASSIFICATION algorithms, REGRESSION analysis, ANIMAL culture, AGRICULTURAL technology

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Autoren: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Quelle: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

Deep-learning-based buffalo identification through muzzle pattern images.

Autoren: Ermetin, Orhan Örnek, Humar Kahramanlı

Quelle: Archives Animal Breeding / Archiv Tierzucht. 2025, Vol. 68 Issue 3, p473-484. 12p.

Schlagworte: Artificial intelligence, Water buffalo, Artificial neural networks, Biometric identification, Livestock, Convolutional neural networks, Deep learning, Identification of animals

Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data.

Autoren: Deniz, Sencer Melih Ademoglu, Ahmet Duru, Adil Deniz et al.

Quelle: Brain Sciences (2076-3425); Jul2025, Vol. 15 Issue 7, p714, 24p

Schlagwörter: EMOTIONS, COGNITION, WAVELETS (Mathematics), ELECTROENCEPHALOGRAPHY, SUPERVISED learning, EVOKED potentials (Electrophysiology), ELECTROPHYSIOLOGY, GRAPH theory

DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.

Autoren: Chang, Liang Yang, Banghua Zhang, Jiayang et al.

Quelle: Cognitive Neurodynamics; 7/23/2025, Vol. 19 Issue 1, p1-14, 14p

Hybrid EEG Feature Learning Method for Cross-Session Human Mental Attention State Classification.

Autoren: Chen, Xu Bao, Xingtong Jitian, Kailun et al.

Quelle: Brain Sciences (2076-3425); Aug2025, Vol. 15 Issue 8, p805, 23p

Schlagwörter: ELECTROENCEPHALOGRAPHY, ATTENTION, SUPPORT vector machines, FEATURE extraction, PATTERN perception, BRAIN-computer interfaces, CLASSIFICATION

Enhancing Alzheimer's Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis.

Autoren: Senkaya, Yeliz Kurnaz, Cetin Ozbilgin, Ferdi

Quelle: Diagnostics (2075-4418); Sep2025, Vol. 15 Issue 17, p2190, 23p

Schlagwörter: ALZHEIMER'S disease, MACHINE learning, SUPPORT vector machines, DIAGNOSIS, K-nearest neighbor classification, ELECTROENCEPHALOGRAPHY, SPECTRAL energy distribution, NEURODEGENERATION

Stack Coupling Machine Learning Model Could Enhance the Accuracy in Short-Term Water Quality Prediction.

Autoren: Zhang, Kai Xia, Rui Wang, Yao et al.

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 19, p2868, 29p

Schlagwörter: WATER quality, MACHINE learning, PREDICTION models, NONPOINT source pollution, LONG short-term memory, BOOSTING algorithms, WATERSHEDS, RUNOFF

Geografische Kategorien: SOUTHEAST China

MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition †.

Autoren: Mao, Qi Zhu, Hongke Yan, Wenyao et al.

Quelle: Brain Sciences (2076-3425); May2025, Vol. 15 Issue 5, p460, 21p

Schlagwörter: MOTOR imagery (Cognition), TRANSFORMER models, SENSORIMOTOR cortex, MOTOR learning, COMPUTATIONAL complexity

ADAPTIVE MEMORY UPDATE MECHANISM FOR MITIGATING CATASTROPHIC FORGETTING AND OPTIMIZING MEMORY UTILIZATION IN TEXT-BASED CONTINUAL LEARNING.

Autoren: Ranjith, J. Baskaran, Santhi

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 1, p363-391, 29p

Schlagwörter: NATURAL language processing, SEQUENTIAL learning, DEEP learning, TASK analysis, MODULAR design

A Multi-Epiphysiological Indicator Dog Emotion Classification System Integrating Skin and Muscle Potential Signals.

Autoren: Jia, Wenqi Hu, Yanzhi Wang, Zimeng et al.

Quelle: Animals (2076-2615); Jul2025, Vol. 15 Issue 13, p1984, 21p

Schlagwörter: EMOTIONS in animals, GALVANIC skin response, ELECTROMYOGRAPHY, ANIMAL welfare, REAL-time computing, ENSEMBLE learning, MACHINE learning, PHYSIOLOGY

Personalized clothing recommendation framework based on the fusion of sports biomechanics and computer vision.

Autoren: Liu, Xiaoyang Sun, Yan

Quelle: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 5, p1-26, 26p

Schlagwörter: SPORTS biomechanics, COMPUTER vision, RECOMMENDER systems, CUSTOM-made clothing, ARTIFICIAL neural networks, MULTISENSOR data fusion, PRESSURE sensors

Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks.

Autoren: Fafrowicz, Magdalena Tutajewski, Marcin Sieradzki, Igor et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-18, 18p

Schlagwörter: CONVOLUTIONAL neural networks, RECOLLECTION (Psychology), SHORT-term memory, INFORMATION processing, VISUAL perception

An Innovative Approach for Extraction of Smoking Addiction Levels Using Physiological Parameters Based on Machine Learning: Proof of Concept.

Autoren: Bascil, Muhammet Serdar Iscanli, Irem Nur

Quelle: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 22, p2839, 22p

Schlagwörter: NICOTINE addiction, MACHINE learning, PATIENT monitoring, DECISION trees, SUPPORT vector machines, SCIENTIFIC observation, K-nearest neighbor classification

The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.

Autoren: Ngo, Kaitlyn Mekhail, Simone Chan, Virginia et al.

Quelle: Nutrients. Nov2025, Vol. 17 Issue 22, p3515. 45p.

Attention-guided deep learning–machine learning and statistical feature fusion for interpretable mental workload classification from EEG.

Autoren: Majumder, Sukanta Patra, Dibyendu Gorai, Subhajit et al.

Quelle: Cognitive Neurodynamics; 12/6/2025, Vol. 20 Issue 1, p1-23, 23p

A HYBRID RECOGNITION METHOD VIA KELM WITH CPSO FOR MMG-BASED UPPER-LIMB MOVEMENTS CLASSIFICATION.

Autoren: CAO, GANGSHENG ZHANG, YUE ZHANG, HANYANG et al.

Quelle: Journal of Mechanics in Medicine & Biology; Aug2024, Vol. 24 Issue 6, p1-24, 24p

Schlagwörter: PATTERN recognition systems, FEATURE extraction, PARTICLE swarm optimization, MACHINE learning, PRINCIPAL components analysis