Artificial Intelligence and Machine Learning Based Methods and Applications

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supervised classification; meta-learning; associative classification; finances; remaining useful life (RUL) prediction; broad learning system (BLS); long short-term memory (LSTM); feature extraction; speech emotion recognition; child speech; younger school age; online learning; support vector machine; minimum enclosing ball; research gap; natural language processing; co-occurrence matrix; double-thresholding method; resistance value; M5P; support vector machine; Gaussian process regression; expansive soil; subgrade; speaker embeddings; x-vectors; deep representations; deep embeddings; speaker disentanglement; speaker recognition; residual information; neural architectures; deep learning; artificial intelligence; VANET; support vector machine; rider optimization algorithm; cuckoo search; whale optimization algorithm; dragonfly; DDoS attack; explainable artificial intelligence; interpretable deep learning; convolutional networks; vision transformers; COVID-19; ultrasound image; online knowledge distillation; ensemble learning; attention aggregation; deep learning; mixture models; parameter estimation; clustering; unsupervised image segmentation; Deep Q-Network; Deep Recurrent Q-Network; Dark Souls III; video games; visual-based reinforcement learning; neural networks; polynomial fuzzy information granule; fuzzy inference system; time series prediction; authorship attribution; artificial neural networks; multi-expression programming; k-nearest neighbour; support vector machines; decision trees; breast density classification; mammography; craniocaudal (CC) view; mediolateral oblique (MLO) view; BI-RADS; convolutional neural network (CNN); loss function; long short-term memory (LSTM); recurrent neural network (RNN); teacher forcing; prediction; performance analysis; benchmarking; machine learning; Tennessee Eastman process; time series; melody generation; conditional generation; diffusion model; transformer; network data; federated learning; unlabeled data; heterogeneous data; greenhouse; temperature; relative humidity; optimal sensor locations; multi-channel regression; dense neural network; adversarial domain adaption; cross-domain sentiment analysis; global-local dynamic adversarial learning; pre-classification; BIRCH; Autoencoder; anomaly detection; text-based item difficulty prediction; text features and item wording; machine learning; regularization methods; elastic net regression; support vector machines; regression and decision trees; random forests; neural networks; algorithm vs. domain expert’s prediction performance; large language models; recommendation systems; human-readable explanations; rating prediction task; explanation generation task; prompt engineering; predictive prompt; hand gesture recognition; image augmentation; geometric transformation; ResNet; MobileNet; inception; static datasets; Centella Asiatica; linn; urban; ensemble deep learning; image segmentation; image enhancement; medicinal plants; traditional medicine; pharmaceuticals; code similarity; linter analysis; comments analysis; software plagiarism; plagiarism detection; universal sentence encoder