Various Deep Learning Algorithms in Computational Intelligence

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Computer science; Information technology industries; affective words; angle detection box; artificial neural network; attention mechanism; automatic optical inspection; backpropagation neural network (BPNN); barrier options; BDLSTM; Black-Scholes model; breast segmentation; CNN; CNN-LSTMs; computer vision; cubical complex; cubical homology; data analysis; decision making; deep learning; DNS; domain; dynamic smoothing; emotion distribution learning; Faster-RCNN; feature fusion; fine-tune; firewall; FPN; GAN; generative adversarial network; generative model; ghettos; GIS; GRU; hybrid deep learning; image classification; image generation; image synthesis; Inception score; k-Nearest Neighbor (kNN); labor exports; LSTM; machine learning; malicious; mammogram; MobileNetV3; modeling; multi-task CNN; n/a; name; networks; Northeast Asian Countries; object detection; optimization of convolutional neural networks; option Greeks; persistent homology; polynomial regression; precipitation nowcasting; PredRNN_v2; PSO; radar image prediction; rain radar; random forest regression; random forest regression (RFR); RPN; SARS-CoV-2; scoreGAN; segregation; semantic segmentation; sign language recognition; ski goggles lenses; Stacked LSTM; surface defect; text-based emotion analysis; time series; traffic sign detection; UNet; Washington D.C.; waste classification; YOLOv5; you only look once (YOLO)
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication

 This reprint highlights the importance of Deep Learning (DL), which has garnered significant attention in science, industry, and academia. It draws inspiration from the functioning of the human brain and the concept of learning. Unlike traditional and machine learning methods, deep learning techniques emulate the human brain's neural networks at a lower scale, allowing them to process and analyze substantial quantities of unstructured data. The remarkable proficiency of deep learning in unveiling intricate structures within extensive datasets genuinely resembles the extraordinary aptitude of the brain to recognize patterns and form complex connections. This unique characteristic allows DL to excel in modeling and solving complex problems across various scientific and technological fields. Just as the brain learns from experience, DL architectures learn through algorithms from data by adjusting numerous parameters during training to optimize their performance and accuracy. This concept of learning and adaptation is fundamental to DL's success. This reprint serves as an excellent opportunity to disseminate current knowledge beyond academic boundaries, reaching a diverse audience encompassing academics, professionals, and the general public. This wide readership fosters the potential for meaningful connections to established projects and the cultivation of collaboration for future research endeavors.