kamal malik (71 résultats)

Etat : New. pp. viii + 231 Index.

Paperback. Etat : Good+. First Edition. 4pp. Book.

Etat : New. pp. viii + 231.

Etat : New. pp. viii + 231 Figures, Illus.

Paperback. Etat : Good+. First Edition. 7pp. Book.

Paperback. Etat : Good+. First Edition. 12pp. Book.

Hardcover. Etat : New. ISBN:9788170353683.

Paperback. Etat : Good+. First Edition. 33pp. Book.

Hardcover. Etat : As New. Contents Preface. 1. Introduction. 2. Historical resume. 3. Physio geographical feature of study area. 4. Materials and methods. 5. Observations. 6. Discussion. 7. Summary. Bibliography. Index. The present book entitled Mahseer Fish Bionomics and Population Barrage Impact on Fish Biology reflects the impact assessment of barrage on Mahseer fish biology mainly in terms of present status of fish diversity in upstream as well downstream of Ganga river fish catch and population size biometry age and growth food and feeding habits migratory and reproductry behaviours. The book includes seven chapters covering in depth analysis of Mahseer fish biology and suggested all possible conservation measures for habitat restoration to enhance the Mahseer population in Ganga river flowering in the foot hills of Garhwal region of Uttaranchal. The book forms a useful contribution for fisheries scientists zoologists aquacuturists sport fish anglers environmentalist researchers students of fisheries sciences and for the people engaged in the field of conservation biology and serve as s guide and inspire future research on Mahseer biology. 232 pp.

hardcover. Etat : Fine.

Etat : New.

Etat : New.

Etat : New. 1st edition NO-PA16APR2015-KAP.

Etat : New.

Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Paperback. Etat : new. Paperback. The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features: Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systems Discusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience. Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using articial intelligence, deep learning, and other machine learning techniques to ne-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the elds of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering. The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Etat : Brand New. 242 pages. 9.18x6.12x9.21 inches. In Stock.

Paperback. Etat : new. Paperback. The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features: Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systems Discusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience. Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using articial intelligence, deep learning, and other machine learning techniques to ne-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the elds of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering. The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Etat : New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

Hardback. Etat : New. New copy - Usually dispatched within 4 working days. 435.

Hardcover. Etat : new. Hardcover. Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance.This book:Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems.Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles.Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making.Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control.Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles.The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering. The book "Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications" is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Etat : New.

Etat : New. In.

Paperback. Etat : new. Paperback. The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features: Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systems Discusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience. Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using articial intelligence, deep learning, and other machine learning techniques to ne-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the elds of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering. The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New. 1st edition NO-PA16APR2015-KAP.

Etat : New.

Etat : New.

Etat : New.