Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. - Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more - Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing - Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

This book constitutes the refereed proceedings of the 10th International Conference on Theory and Practice of Natural Computing, TPNC 2021, held virtually, in December 2021. The 9 full papers presented together with 3 invited talks, in this book were carefully reviewed and selected from 14 submissions. The papers are organized in topical sections named Applications of Natural Computing, Deep Learning and Transfer Learning, Evolutionary and Swarm Algorithms.

This book presents original, peer-reviewed select articles from the International Conference on Cognitive & Intelligent Computing (ICCIC – 2021), held on December 11–12, 2021, at Hyderabad, India. The proceedings has cutting edge Research outcome related to Machine learning in control applications, Soft computing, Pattern Recognition, Decision Support Systems, Text analytics and NLP, Statistical Learning, Neural Network Learning, Learning Through Fuzzy Logic, Learning Through Evolution (Evolutionary Algorithms), Reinforcement Learning, Multi-Strategy Learning, Cooperative Learning, Planning And Learning, Multi-Agent Learning, Online And Incremental Learning, Scalability Of Learning Algorithms, Inductive Learning, Inductive Logic Programming, Bayesian Networks, Support Vector Machines, Case-Based Reasoning, Multi-Agent Systems, Human–Computer Interaction, Data Mining and Knowledge Discovery, Knowledge Management and Networks, Data Intensive Computing Architecture, Medicine, Health, Bioinformatics, and Systems Biology, Industrial and Engineering Applications, Security Applications, Smart Cities, Game Playing and Problem Solving, Intelligent Virtual Environments, Economics, Business, And Forecasting Applications. Articles in the book are carefully selected on the basis of their application orientation. The content is expected to be especially useful for Professionals, Researchers, Research students working in the area of cognitive and intelligent computing.

Maritime Technology and Engineering 3 is a collection of papers presented at the 3rd International Conference on Maritime Technology and Engineering (MARTECH 2016, Lisbon, Portugal, 4-6 July 2016). The MARTECH Conferences series evolved from biannual national conferences in Portugal, thus reflecting the internationalization of the maritime sector. The keynote lectures and the papers, making up nearly 150 contributions, came from an international group of authors focused on different subjects in a variety of fields: Maritime Transportation, Energy Efficiency, Ships in Ports, Ship Hydrodynamics, Ship Structures, Ship Design, Ship Machinery, Shipyard Technology, afety & Reliability, Fisheries, Oil & Gas, Marine Environment, Renewable Energy and Coastal Structures. This book will appeal to academics, engineers and professionals interested or involved in these fields.

Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.

This Research Topic is the second volume of this collection. You can find the original collection via https://www.frontiersin.org/research-topics/45485/deep-learning-for-marine-science Deep learning (DL) is a critical research branch in the fields of artificial intelligence and machine learning, encompassing various technologies such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformer networks and Diffusion models, as well as self-supervised learning (SSL) and reinforcement learning (RL). These technologies have been successfully applied to scientific research and numerous aspects of daily life. With the continuous advancements in oceanographic observation equipment and technology, there has been an explosive growth of ocean data, propelling marine science into the era of big data. As effective tools for processing and analyzing large-scale ocean data, DL techniques have great potential and broad application prospects in marine science. Applying DL to intelligent analysis and exploration of research data in marine science can provide crucial support for various domains, including meteorology and climate, environment and ecology, biology, energy, as well as physical and chemical interactions. Despite the significant progress in DL, its application to the aforementioned marine science domains is still in its early stages, necessitating the full utilization and continuous exploration of representative applications and best practices.