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Artificial neural networks are a form of artificial intelligence that have the capability of learning, growing, and adapting with dynamic environments. With the ability to learn and adapt, artificial neural networks introduce new potential solutions and approaches to some of the more challenging problems that the United States faces as it pursues the vision of space exploration and develops other system applications that must change and adapt after deployment. Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book. The NASA IV&V and the Institute for Scientific Research, Inc. are working to be at the forefront of software safety and assurance for neural network and adaptive systems. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is structured for research scientists and V&V practitioners in industry to assure neural network software systems for future NASA missions and other applications. This book is also suitable for graduate-level students in computer science and computer engineering.

Astronautics --- Neural networks (Computer science) --- Artificial intelligence. --- Computer software --- Data processing. --- Verification. --- Validation. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Software validation --- Validation of software --- Software verification --- Verification of software --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Information Technology --- Computer Science (Hardware & Networks) --- Computer Communication Networks. --- Computer science. --- Computer network architectures. --- Computer vision. --- Optical pattern recognition. --- Artificial Intelligence. --- User Interfaces and Human Computer Interaction. --- Computer Systems Organization and Communication Networks. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Pattern Recognition. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Informatics --- Science --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- Computer communication systems. --- User interfaces (Computer systems). --- Computer organization. --- Optical data processing. --- Pattern recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Organization, Computer --- Electronic digital computers --- Interfaces, User (Computer systems) --- Human-machine systems --- Human-computer interaction --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Optical equipment --- Distributed processing

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Social welfare methods --- Organization theory

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The SAGE Handbook on Decision Making, Assessment and Risk in Social Work provides a comprehensive overview of key debates and issues; and gives pointers on future directions for practice, research, teaching, management of services, and development of theoretical understandings. It′s key aim is to support the development of sound, applied 'science' (knowledge) and values to underpin reasoned, reasonable professional judgement and decision making by social workers in practice and those in management and regulatory roles.

Social service --- Social service --- Decision making. --- Risk assessment.

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Artificial neural networks are a form of artificial intelligence that have the capability of learning, growing, and adapting with dynamic environments. With the ability to learn and adapt, artificial neural networks introduce new potential solutions and approaches to some of the more challenging problems that the United States faces as it pursues the vision of space exploration and develops other system applications that must change and adapt after deployment. Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book. The NASA IV&V and the Institute for Scientific Research, Inc. are working to be at the forefront of software safety and assurance for neural network and adaptive systems. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is structured for research scientists and V&V practitioners in industry to assure neural network software systems for future NASA missions and other applications. This book is also suitable for graduate-level students in computer science and computer engineering.

Mathematical statistics --- Computer architecture. Operating systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- neuronale netwerken --- factoranalyse --- grafische vormgeving --- informatica --- computerbesturingssystemen --- OS (operating system) --- computernetwerken --- robots

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How do social work students learn to use research to underpin their practice decisions? How do they learn that research is not an activity unconnected to their professional role and responsibilities, but rather acts as a foundation for their knowledge? By using the examples drawn from evidence-based practice (e.g. what is known to work and what we know about social work processes), the authors deliver a text that will help support students to appraise and then integrate research into both their daily practice decisions and their assignments and assessments. It will do this by defining key concepts like 'knowledge' and 'evidence' and then look at how these concepts include component parts - from law and legislation to practice knowledge and reflective and critical practice. Case examples are used to illustrate how a clear understanding of these component parts can build to a substantial evidence-base from which to draw upon. Identifying relevant research and appraising its quality are core aspects of the book. Later chapters show students how robust knowledge of evidence-based practice can develop into a clear and confident approach to their workloads and their daily practice dilemmas.

Social service --- Research.