Search

Summary: Introduction to Search Algorithms considers various aspects of search algorithms including an extensive overview of search algorithms and related terms. It includes introduction and the fundamentals of search algorithms, fundamentals of linear search algorithm, introduction to a* based best-first search algorithms. Provides the reader with insights into the development of its history, so as to understand the running particle swarm optimization on graphics processing, search via quantum walk.

Summary: The book ‘Distributed Database Architecture’ discusses various topics related to the database architecture. The technology of database management is a very commonly used one nowadays and is the basis of many tech-businesses. The book takes the readers through the various aspects of the distributed database architecture, the way in which it is designed and th manner in which it is brought to use. The book can be used a good source of information by the various IT professionals or aspiring the data analysts.

Summary: Deep neural networks and applications makes the readers aware about the various Artificial Neutral Networks (ANN) and the topologies related to Main Neutral Networks (MNN). The book throws light on the prospect of artificial intelligence and the applications it has in risk management. It further elaborates on the Artificial Neutral Networks in detail and discusses the practical applications of the deep neutral networks. Also discussed in the book is the optimization of deep learning for the best performance of e-learning data, the methodology and the research framework, development of the algorithms that quicken the data processing over complex network architectures and the optimization of database query structures using deep learning.

Summary: Today, it is considered that intelligence includes at least two skills: the ability to memorize and store knowledge, and the ability to process knowledge. The person (or machine) without any knowledge cannot be considered intelligent. The ability of learning - acquisition of new knowledge, is also one of the aspects of the intelligence, although we can classify it as an ability to solve problems. As an “intelligent feature” we can also consider the ability to communicate with other intelligent beings. For the concept of intelligence - two questions are essential: the question of knowledge and the reasoning (making conclusions), and, this corresponds to the terms of a knowledge base and a reasoning process. The component of reasoning (inference) also represents a kind of knowledge - it is knowledge about the process of carrying out new information from an existing knowledge base.This edition covers different topics from bio-intelligence science, and application of bio-intelligence in different domains - the bio-medical domain, the learning, the medicine etc.Section 1 focuses on biological aspects of the intelligence, describing biological vs. artificial intelligence, brain as an emergent finite automaton, biological neural network structure and spike activity prediction based on multi-neuron spike train data, an experiment in use of brain computer interfaces for cognitive researches, and chessboard model of human brain and an application on memory capacity.Section 2 focuses on topics from neuroscience, describing patterns discovery in brain signals using decision trees, an interactive immersive tool for brain education, art, and neuro-therapy, analyzing brain functions by subject classification of functional near-infrared spectroscopy data using convolutional neural networks analysis, modeling neuromorphic persistent firing networks, and creativity as central to critical reasoning and the facilitative role of moral education. Section 3 focuses on pattern recognition in neuro and medical applications, describing Brain-k for structural image processing: creating electrical models of the human head, application of machine learning in postural control kinematics for the diagnosis of Alzheimer’s disease, and semi supervised clustering by iterative partition and regression with neuroscience applications.Section 4 focuses on neural networks applications, describing quantum-inspired neural networks with application, training feedforward neural networks using symbiotic organisms search algorithm, artificial intelligence for speech recognition based on neural networks, and deep recurrent neural network-based auto-encoders for acoustic novelty detection.