Published by Astral International (P) Ltd Daya, 2018
ISBN 10: 8170355710 ISBN 13: 9788170355717
Language: English
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pp. xiv + 239 Index.
Published by Astral International (P) Ltd, 2018
ISBN 10: 8170355710 ISBN 13: 9788170355717
Language: English
Seller: Majestic Books, Hounslow, United Kingdom
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Add to basketCondition: New. pp. xiv + 239 Figure, Illus.
Published by Astral International (P) Ltd, 2018
ISBN 10: 8170355710 ISBN 13: 9788170355717
Language: English
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. pp. xiv + 239.
Published by Daya Publishing House, 2018
ISBN 10: 8170355710 ISBN 13: 9788170355717
Language: English
Seller: Books in my Basket, New Delhi, India
Hardcover. Condition: New. ISBN:9788170355717.
Published by Daya Publishing House, 2018
ISBN 10: 8170355710 ISBN 13: 9788170355717
Language: English
Seller: Vedams eBooks (P) Ltd, New Delhi, India
Hardcover. Condition: New. 2nd Edition. Contents: 1. Statistical Modelling in Fisheries Research: An Overview/Prajneshu. 2. Is Fisheries Sector in India Changing its Gear/Ganesh Kumar. 3. Non-Linear Models for Forecasting Fish Production from Ponds/Lalmohan Bhar. 4. Statistical Models and their Application in Fishery Science Research/R Venugopalan and Prajneshu. 5. Methods of Forecasting and their Application in Fishery Science/P. K. Sahu. 6. Modelling in Aquaculture/A. K. Roy. 7. Modelling of Multiobjective Freshwater Aquaculture/Pratap K J Mohapatra, Santosh K Prusty and C K Mukherjee. 8. A Revisit to Building up Models in Fishery Research/Satyabrata Pal and Subhabaha Pal. 9. Quantitative Techniques for Fish Stock Assessment/R S Biradar. 10. Quantitative Techniques for Fish Stock Assessment/R S Biradar.11. Tropho-Dynamic Modelling for Ecosystem Based Fisheries Management/Preetha Panikkar and M feroz Khan. 12. Forecasting of Fish Production of India by Culture Environment and Species/A K Roy and Nirupama Panda. 13. Expert Systems and Simulation Studies in Agriculture Aquaculture/J Panduranga Rao. 14. Recent Trends in Artifical Neural Network (ANN) and Fuzzy Logic and their Application in Aquaculture/M Balakrishnan and R C Srivastava. 15. Soft Computing Techniques for Data Analysis and Modelling in Fisheries/H C Verma. 16. An Econometric Model for Productivity Analysis of Aquaculture Farms/A K Roy, G S Saha, N Sarangi and Nibedita Jena. The book entitled 'Modelling Forecasting Artificial Neural Network and Expert System in Fisheries and Aquaculture is the first of its kind avilable in the market. The book contains altogether sixteen chapter covering both capture and culture fisheries aspects contributed by various subject matter specialists engaged in research and development activities at various national institutes of repute. Each of the chapters has been specially written by an expert in the field bringing together in a single volume a range of approaches and reviews covering conceptual and mathematical model empirical and theoretical model deterministic and stochastic model biological model pond ecosystem model and explanatory models. Both linear as well as nonlinear models applied to fisheries and aquaculture research has been dealt with illustrated examples on current real fisheries data. Adequate attention has been given to select chapters that contain both theoretical as well as applied areas of research. Important chapters like statistical modelling and their application in fisheries quantitative methods in fish stock assessment time series modeling tropho dynamic modelling and forecasting of fish production by environment and species/groups have been added. Methods of multiobjective freshwater aquaculture model are altogether a new approach in aquaculture research containing valuable information. Besides conventional methods latest developments on expert system simulation articial neural network fuzzy logic soft computing techniques in data analysis have been documented for the first time in India. Advanced econometric approach in productivity analysis has been demonstrated with a real field data identifying the role of socio-economic and farm specific management variables on technical efficiency of aquaculture farm. Finally the book provides a comprehensive account of recent developments in modelling and forecasting and also an insightful and authoritative overview of the broad umbrella of modelling and forecasting. Each and every chapter is important for researchers planners and those associated with development activities to cope with the most challenging task of providing food and nutritional security to the present population as well as to plan for supply of food to the future population of the country. (jacket).
Seller: Marlton Books, Bridgeton, NJ, U.S.A.
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Paperback. Condition: new. Paperback. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Add to basketPaperback. Condition: new. Paperback. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Hardcover. Condition: new. Hardcover. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Add to basketPaperback. Condition: new. Paperback. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Add to basketHardcover. Condition: new. Hardcover. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Add to basketHardcover. Condition: new. Hardcover. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods'.
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Add to basketBuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods'.