Advanced Machine Learning Applications in Big Data Analytics

• Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2023
• Descrizione fisica: 1 online resource (654 p.)
• Soggetto topico: History of engineering & technology; Technology: general issues
• Soggetto non controllato: 1D quadratic chaotic system; adaptive learning; adversarial attacks; ARMA; attentional mechanisms; BaaS system; bagging model; behavior detection; blockchain consensus algorithm; border patrol; BP; butterfly optimization algorithm; capacitated vehicle routing planning; CBAM; CBCFI; classification algorithms; cloud; clustering algorithms; CNN; color image; combined prediction model; community clustering; complementary ensemble empirical mode decomposition (CEEMD); composite multi-scale dispersion entropy; computer vision; concept drift; confidentiality management; convolutional neural networks; coupled map lattice; COVID-19; crisscross search; data stream mining; deep belief network; deep feature; deep learning; design science research; differential evolution; digital archives; disease classification; DNA coding; DNA computing; DNA sequences design; document classification; dual-update strategy; ELM; embedding propagation; emotion-cause pair extraction; energy storage; error coefficient; event extraction; event trigger words; event type; extreme learning machine; fault diagnosis; feature selection; financial time series forecasting; flight safety; forecasting; forex; GA; generative adversarial network; global optimization; GM; graph attention network; graph convolutional network; graph neural network; gravity search; hash function; health; Hemerocallis citrina Baroni; heterogeneous graph; hierarchical clustering; hierarchical model; high-plateau flight; hybrid model; hyperspectral image classification; image classification; image encryption; improved matrix particle swarm optimization algorithm (IMPSO); incremental learning; information system; information systems; infrared; Jaccard distance; KNN; least squares method; lightweight neural networks; long short-term memory network; machine learning; maturity detection; mean absolute error; mean-semivariance model; membership grade; meta-heuristic; model predictive control; multi-behavior recommendation; multi-strategy; MultiRocket; n/a; neural architecture search; neural networks; NLP; object detection; online learning; opposition-based learning; output optimization; overseas Chinese associations; particle swarm optimization; peak shaving and frequency regulation; performance analysis; pilot abnormal behavior; pixel level; polymorphic mapping; portfolio optimization; principal component analysis; PROPHET; PSO; quantum dynamics; quick access recorder; random replacement; ridge regression; saving mileage; sequential recommendation; service level agreement; short-term traffic-flow forecasting; signal-to-noise ratio distance; sliding window; spatial-temporal systems; splicing model; stacking model; stock announcement news; stock return; support vector machine; support vector machine swarm intelligence; support vector machines; swarm intelligence; talent stability prediction; target detection; time series; time series classification; tomato leaf; traffic flow forecasting; transaction priority; variational mode decomposition; warning system; whale optimization algorithm; YOLOv4 algorithm; YOLOv5 algorithm
• Sommario/riassunto: With the development of computer technology and communication technology, various industries have collected a large amount of data in different forms, so-called big data. How to obtain valuable knowledge from these data is a very challenging task. Machine learning is such a direct and effective method for big data analytics. In recent years, a variety of advanced machine learning technologies have emerged, and they continue to play important roles in the era of big data. Considering advanced machine learning and big data together, we have selected a series of relevant works in this Special Issue to showcase the latest research advancements in this field. Specifically, a total of thirty-three articles are included in this Special Issue, which can be roughly categorized into six groups: time series analysis, evolutionary computation, pattern recognition, computer vision, image encryption, and others.
• Titolo autorizzato: Advanced Machine Learning Applications in Big Data Analytics
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910743276603321
• Lo trovi qui: Univ. Federico II