The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.

This book presents the proceedings of the 4th International Conference on Advanced Intelligent Systems and Informatics 2018 (AISI2018), which took place in Cairo, Egypt from September 1 to 3, 2018. This international and interdisciplinary conference, which highlighted essential research and developments in the field of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several main sections: Intelligent Systems; Robot Modeling and Control Systems; Intelligent Robotics Systems; Machine Learning Methodology and Applications; Sentiment Analysis and Arabic Text Mining; Swarm Optimizations and Applications; Deep Learning and Cloud Computing; Information Security, Hiding, and Biometric Recognition; and Data Mining, Visualization and E-learning.

Machine Learning for Powder-based Metal Additive Manufacturing outlines machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs. The book combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications. The book covers ML for design in AM, ML for materials development and intelligent monitoring in metal AM, both geometrical deviation and physics informed machine learning modeling, as well as data-driven cost estimation by ML. In addition, optimization for slicing and orientation, ML to create models of materials for AM processes, ML prediction for better mechanical and microstructure prediction, and feature extraction by sensing data are all covered, and each chapter includes a case study. - Covers machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs - Combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications - Discusses algorithm development of ML for metal AM, metal AM process modeling and optimization, mathematical and simulation studies of metal AM, and pre- and post-processing smart methods for metal AM

The COVID-19 pandemic shook the world to its core. After a brief pause, organizations of all kinds had to adapt to the new circumstances given to them with very little time. The presence of the pandemic caused multiple threats that caused several disruptions to the norms, beliefs, and practices in various domains of everyday life. Both from macro and micro perspectives, individuals, households, markets, institutions, and governments developed strategies to respond to the new environment—responses that hope to eliminate or at least decrease the threats of the COVID-19 pandemic. The Handbook of Research on Interdisciplinary Perspectives on the Threats and Impacts of Pandemics explores the COVID-19 pandemic from an interdisciplinary perspective and determines how future pandemics may impact society. Beginning as a health threat, the pandemic has led the way to economic, social, psychological, political, and informational crises necessitating the examination of the phenomenon from different academic disciplines. Covering topics such as distance education, human security, and predictions, this handbook of research is an essential resource for scholars, managers, media representatives, governors, health officials, government officials, policymakers, students, professors, researchers, and academicians.

Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area. - Provides coverage of the fundamentals of Artificial Intelligence as applied to data mining, including computational intelligence and unsupervised learning methods for data clustering - Presents coverage of key topics such as heuristic methods for data clustering, deep learning methods for data classification, and neural networks - Includes case studies and real-world applications of AI techniques in data mining, for improved outcomes in clinical diagnosis, satellite data extraction, agriculture, security and defense

This two-volume set (LNAI 9329 and LNAI 9330) constitutes the refereed proceedings of the 7th International Conference on Collective Intelligence, ICCCI 2014, held in Madrid, Spain, in September 2015. The 110 full papers presented were carefully reviewed and selected from 186 submissions. They are organized in topical sections such as multi-agent systems; social networks and NLP; sentiment analysis; computational intelligence and games; ontologies and information extraction; formal methods and simulation; neural networks, SMT and MIS; collective intelligence in Web systems – Web systems analysis; computational swarm intelligence; cooperative strategies for decision making and optimization; advanced networking and security technologies; IT in biomedicine; collective computational intelligence in educational context; science intelligence and data analysis; computational intelligence in financial markets; ensemble learning; big data mining and searching.