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Summary: World has seen rapid development in the field of Information technology and Biotechnology over a decade. New experimental technologies developed in biotechnology and data available made it possible to perform experiments easily in less time and cost. These experiments also generate huge amount of data that may overwhelm even the most data-savvy researchers. Data generated during experimentation give lot of scope for companies that provide products and services in the field of biotechnology and new opportunities for researchers. This huge data may create challenges to the researches using low-throughput methods to handle and analyse data. Artificial intelligence plays prominent role in analysing huge data available in a systematic way and represent analysed data in a meaning full way. In todays time it is practically not possible to carry out research in biotechnology without utilising data available in public and private databases and artificial intelligence to analyse data. This book describes advancements and application of AI in the field of biotechnology.

Summary: In the present times, artificial intelligence has been adopted by the businesses as the most beneficial advancement in technology so far. Particularly, in the field of business, artificial intelligence is playing a major role. This book sheds light on all the positive and negative aspects, benefits and security threats of artificial intelligence in businesses. The past and present trends about the adoption of artificial intelligence in the business world are also highlighted through statistics and examples

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.

Summary: This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths

Summary: A research- and application-oriented handbook that covers one of the hottest topics in science and technology A timely and up-to-date reference, edited by two internationally renowned experts Goes beyond the applications of robotics in industrial settings to cover the science’s new uses.

Summary: This book is an edited selection of the papers presented at the International Workshop on VLSI for Artifidal Intelligence and Neural Networks which was held at the University of Oxford in September 1990. Our thanks go to all the contributors and especially to the programme committee for all their hard work. Thanks are also due to the ACM-SIGARCH, the IEEE Computer Society, and the lEE for publicizing the event and to the University of Oxford and SUNY-Binghamton for their active support. We are particularly grateful to Anna Morris, Maureen Doherty and Laura Duffy for coping with the administrative problems. Jose Delgado-Frias Will Moore April 1991 vii PROLOGUE Artificial intelligence and neural network algorithms/computing have increased in complexity as well as in the number of applications. This in tum has posed a tremendous need for a larger computational power than can be provided by conventional scalar processors which are oriented towards numeric and data manipulations. Due to the artificial intelligence requirements (symbolic manipulation, knowledge representation, non-deterministic computations and dynamic resource allocation) and neural network computing approach (non-programming and learning), a different set of constraints and demands are imposed on the computer architectures for these applications.