Application of Machine Learning and Data Mining
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Beskrivning: 1 electronic resource (264 p.)Innehållstyp: - text
- computer
- online resource
- 9783725819256
- 9783725819263
- Computing and Information Technology
- Applied computing
- Bayesian optimization
- EEG signals
- EPSO
- FIFA World Cup
- FVSI and Lmn
- LSTM
- MT-MSTd
- PSO variants
- SNN
- STDP
- adaptive boosting algorithm
- adversarial machine learning
- aquaculture
- attendee prediction
- automated guided vehicle
- breakthroughs
- classifier robustness
- combined prediction
- convolutional neural network
- defect fixing
- denoising autoencoder
- developer assignment
- diminish power loss
- driving risk field
- driving style recognition
- dynamics
- echo state network
- ensemble classifiers
- environmental data
- epilepsy detection
- evasion attacks
- extended space
- feature extraction
- foundation model
- genetic algorithm
- gradient correlation
- greenhouse gases
- heterogeneous collaborative network
- hidden feature extraction
- identification of weak bus
- intermodal
- linear regression
- machine learning
- maritime safety
- mask learning
- mathematical achievement
- mathematical optimization
- mega sports events
- moth–flame optimization
- multi-domain features
- multi-population genetic algorithm
- multimodal
- navigation aids
- networks
- non-intrusive load recognition
- optic
Open Access Unrestricted online access star
The following special issue contains 14 articles accepted and published in the Special Issue "Mathematics and Computer Science, 2024" of the MDPI journal Mathematics. The included articles cover a wide range of topics related to the theory and applications of Machine Learning and Data Mining, as well as their extensions and generalizations. These topics include, but are not limited to, supervised, unsupervised, and self-learning methods; large-scale data mining; applicable neural networks and artificial intelligence; neural network-based industrial applications; neural models for natural language processing; deep learning for health informatics and biomedical engineering; graph convolutional neural networks and their applications; deep reinforcement learning and its applications; deep sparse and low-rank representation; and computer vision and pattern recognition techniques. In addition to these core areas, the authors of the included articles also delve into advanced methodologies and innovative approaches within these fields. The collection aims to provide comprehensive insights into recent advancements and emerging trends in Machine Learning and Data Mining, making it a valuable resource for researchers, practitioners, and students interested in the latest developments in these dynamic disciplines.
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eng
Freely available e-book