Various Deep Learning Algorithms in Computational Intelligence

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