Buch, Englisch, Band 1637, 546 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 850 g
Third International Conference, NCAA 2022, Jinan, China, July 8-10, 2022, Proceedings, Part I
Buch, Englisch, Band 1637, 546 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 850 g
Reihe: Communications in Computer and Information Science
ISBN: 978-981-19-6141-0
Verlag: Springer Nature Singapore
The 77 papers included in these proceedings were carefully reviewed and selected from 205 submissions. These papers were categorized into 10 technical tracks, i.e., neural network theory, and cognitive sciences, machine learning, data mining, data security & privacy protection, and data-driven applications, computational intelligence, nature-inspired optimizers, and their engineering applications, cloud/edge/fog computing, the Internet of Things/Vehicles (IoT/IoV), and their system optimization, control systems, network synchronization, system integration, and industrial artificial intelligence, fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences, computer vision, image processing, and theirindustrial applications, natural language processing, machine translation, knowledge graphs, and their applications, Neural computing-based fault diagnosis, fault forecasting, prognostic management, and system modeling, and Spreading dynamics, forecasting, and other intelligent techniques against coronavirus disease (COVID-19).
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
TE-BiLSTM: Improved Transformer and BiLSTM on Fraudulent Phone Text Recognition.- Cross elitist learning multifactorial evolutionary algorithm.- Heterogeneous Adaptive Denoising Networks for Recommendation.- Formation Control Optimization via Leader Selection for Rotor Unmanned Aerial Vehicles.- Dynamic Monitoring Method Based on Compara-tive Study of Power and Environmental Protection Indicators.- Combustion State Recognition Method in Municipal Solid Waste Incineration Process Based on Improved Deep Forest.- RPCA-induced Graph Tensor Learning for Incomplete Multi-view Inferring and Clustering.- TRUST-TECH Assisted GA-SVM Ensembles and its Applications.
