singh pancham (30 resultados)

Condición: New. pp. 112.

Condición: New. pp. 112 Revised Edition.

Condición: New. pp. 112.

Condición: New.

Hardcover. Condición: As New.

Condición: New.

Condición: New. pp. 375.

Condición: New. pp. 375.

Condición: New. pp. 375.

Condición: New. In.

Condición: New.

Hardcover. Condición: New. 1st Edition. Contents: Preface. 1. Water management for developing smart cities in India/Vijay Kumar. 2. Impact of changing climate in snow glacial steams of Himachal Himalaya/Rajesh Kumar, Shaktiman Singh, Ramesh Kumar, Atar Singh and Anshuman Bhardwaj. 3. Success and failed stories in identification of groundwater potential zones in Hard rock terrain/V. Varalakshmi, R. Kotaiah, B. Venkateswara Rao and Y. Sivaprasad. 4. Scaling up the knowledge and adoption of water saving technologies through the training programme/K.S. Bhargav, R.P. Sharma and Nishith Gupta. 5. Estimation of water productivity for wheat in farmer's field of North Bihar/Ravish Chandra, S.K. Jain and A.K. Singh. 6. Design and economic evaluation of soil moisture uniformity coefficient based overlapped micro-sprinkler irrigation systems for black cotton soils of Malwa region, Madhya Pradesh/V.K. Jain and D.M. Denis. 7. Effect of planting geometry and fertigation patterns on growth, yield and water productivity of tomato/S.S. Mali, B.K. Jha and S.K. Naik. 8. Protected cultivation and drip fertigation technology for efficient use of water in agriculture/M. Hasan and Neelam Patel. 9. Flood Hazard modelling of Mahanadi river using HEC-RAS/Dhiman Kumar, Vinod Kumar Tripathi and Prabeer Kumar Parhi. 10. Water management in India: looking beyond per capita availability/M.S. Bhat and Jasleen Kaur. 11. Water quality criteria: quality standards for water supplies/Sumeet Kumar Gupta, Sudeep Kumar and Chetna Sinha. 12. Analysis of rainfall and temperature trend of Jharkhand State/Jyoti Kumari and Prabeer Kumar Parhi. 13. Industrial effluent characterization of Patliputra industrial estate Patna/Prashant and Subodh Kumar. 14. Spatial and temporal analysis of rainfall trend in Bihar (India)/Priti Kumari and P.K. Parhi. 15. A 3D-framework for minimizing water consumption in large scale manufacturing sector with special reference to Steel Industry/S. Mitra Mazumder, M. Choubey and Ashutosh Gupta. 16. Water quality assessment of river Ganges at Allahabad, Uttar Pradesh, India/Pankaj Kumar Singh and Satyendra Nath. 17. Development of a cell-to-cell routing scheme for satellite based hydrological model (SHM)/Pranesh Kumar Paul, Nikul Kumari, Rajendra Singh, Niranjan Panigraphy and Ashok Mishra. 18. Water management and sustainable development/S.N. Prajapati and Nagendra Prasad. 19. Stream flow simulation by using soil and water assessment tool for Chotki-Berghi watershed in Jharkhand/Surojit Sarkar, Vivek Vaibhav and Ajai Singh. 20. Simulation of water movement for transplanted Thilli crop using hydrus-ID/Utkarsh Upadhyaya, Vinod Kr. Tripathi and Ajai Singh. The basic concept behind sustainable development endorses taking up strong measures to spur economic and social development, particularly for people in developing countries, while ensuring that environmental integrity and management is sustained for future generations. Water is at the core of sustainable development and is critical for socio-economic development, healthy ecosystems and for human survival itself. It is central to the production and preservation of a host of benefits and services for people. Water is also at the heart of adaptation to climate change, serving as the crucial link between the climate system, human society and the environment. Today, more than 1.7 billion people live in river basins where depletion through use exceeds natural recharge, a trend that will see two thirds of the world's population living in water-stressed countries by 2025. Water can pose a serious challenge to sustainable development but managed efficiently and equitably, water can play a key enabling role in strengthening the resilience of social, economic and environmental systems in the light of rapid and unpredictable changes. The conference received a good number of research papers and review articles. The research papers were reviewed critically and we were of the opinion that this may be come out in a form of edited book. This will ens.

Paperback. Condición: New. The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. It equips readers with the knowledge and practical insights needed to harness these technologies effectively, enhancing software development, testing, maintenance, and deployment processes. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics. Moreover, the forward-looking section discussing future trends and research directions will inspire readers to explore new avenues in software engineering. This book serves as a valuable resource for those seeking to leverage advanced technologies to improve software engineering processes, making it an essential addition to the library of anyone involved in software development.

Hardback. Condición: New. The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. It equips readers with the knowledge and practical insights needed to harness these technologies effectively, enhancing software development, testing, maintenance, and deployment processes. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics. Moreover, the forward-looking section discussing future trends and research directions will inspire readers to explore new avenues in software engineering. This book serves as a valuable resource for those seeking to leverage advanced technologies to improve software engineering processes, making it an essential addition to the library of anyone involved in software development.

Paperback. Condición: New. The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. It equips readers with the knowledge and practical insights needed to harness these technologies effectively, enhancing software development, testing, maintenance, and deployment processes. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics. Moreover, the forward-looking section discussing future trends and research directions will inspire readers to explore new avenues in software engineering. This book serves as a valuable resource for those seeking to leverage advanced technologies to improve software engineering processes, making it an essential addition to the library of anyone involved in software development.

Hardback. Condición: New. The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. It equips readers with the knowledge and practical insights needed to harness these technologies effectively, enhancing software development, testing, maintenance, and deployment processes. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics. Moreover, the forward-looking section discussing future trends and research directions will inspire readers to explore new avenues in software engineering. This book serves as a valuable resource for those seeking to leverage advanced technologies to improve software engineering processes, making it an essential addition to the library of anyone involved in software development.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. As software systems grow in complexity and scale, ensuring their reliability and quality becomes challenging. Traditional methods of defect detection are time-consuming, prone to errors, and inadequate for identifying issues. To address these limitations, the integration of machine learning (ML) techniques and large language models (LLMs) emerges as a transformative approach in automating software defect detection. ML algorithms can learn from historical bug data to predict vulnerabilities, while LLMs can detect anomalies with high accuracy. This convergence holds the potential to improve automation, software engineering, and defect detection, while introducing new challenges in interpretability, data bias, and model reliability that require further exploration. Automating Software Defect Detection Through Machine Learning and LLMs explores how cutting-edge technologies like machine learning (ML) and large language models (LLMs) transform software detection. It examines how these technologies enhance accuracy, scalability, and efficiency in identifying and mitigating software defects. This book covers topics such as algorithms, fraud detection, and software engineering, and is a useful resource for engineers, security professionals, academicians, researchers, and computer scientists. "This book provides a comprehensive understanding of how machine learning and large language models revolutionize the process of software defect detection"-- Provided by publisher. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. As software systems grow in complexity and scale, ensuring their reliability and quality becomes challenging. Traditional methods of defect detection are time-consuming, prone to errors, and inadequate for identifying issues. To address these limitations, the integration of machine learning (ML) techniques and large language models (LLMs) emerges as a transformative approach in automating software defect detection. ML algorithms can learn from historical bug data to predict vulnerabilities, while LLMs can detect anomalies with high accuracy. This convergence holds the potential to improve automation, software engineering, and defect detection, while introducing new challenges in interpretability, data bias, and model reliability that require further exploration. Automating Software Defect Detection Through Machine Learning and LLMs explores how cutting-edge technologies like machine learning (ML) and large language models (LLMs) transform software detection. It examines how these technologies enhance accuracy, scalability, and efficiency in identifying and mitigating software defects. This book covers topics such as algorithms, fraud detection, and software engineering, and is a useful resource for engineers, security professionals, academicians, researchers, and computer scientists. "This book provides a comprehensive understanding of how machine learning and large language models revolutionize the process of software defect detection"-- Provided by publisher. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. As software systems grow in complexity and scale, ensuring their reliability and quality becomes challenging. Traditional methods of defect detection are time-consuming, prone to errors, and inadequate for identifying issues. To address these limitations, the integration of machine learning (ML) techniques and large language models (LLMs) emerges as a transformative approach in automating software defect detection. ML algorithms can learn from historical bug data to predict vulnerabilities, while LLMs can detect anomalies with high accuracy. This convergence holds the potential to improve automation, software engineering, and defect detection, while introducing new challenges in interpretability, data bias, and model reliability that require further exploration. Automating Software Defect Detection Through Machine Learning and LLMs explores how cutting-edge technologies like machine learning (ML) and large language models (LLMs) transform software detection. It examines how these technologies enhance accuracy, scalability, and efficiency in identifying and mitigating software defects. This book covers topics such as algorithms, fraud detection, and software engineering, and is a useful resource for engineers, security professionals, academicians, researchers, and computer scientists. "This book provides a comprehensive understanding of how machine learning and large language models revolutionize the process of software defect detection"-- Provided by publisher. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: new. Hardcover. As software systems grow in complexity and scale, ensuring their reliability and quality becomes challenging. Traditional methods of defect detection are time-consuming, prone to errors, and inadequate for identifying issues. To address these limitations, the integration of machine learning (ML) techniques and large language models (LLMs) emerges as a transformative approach in automating software defect detection. ML algorithms can learn from historical bug data to predict vulnerabilities, while LLMs can detect anomalies with high accuracy. This convergence holds the potential to improve automation, software engineering, and defect detection, while introducing new challenges in interpretability, data bias, and model reliability that require further exploration. Automating Software Defect Detection Through Machine Learning and LLMs explores how cutting-edge technologies like machine learning (ML) and large language models (LLMs) transform software detection. It examines how these technologies enhance accuracy, scalability, and efficiency in identifying and mitigating software defects. This book covers topics such as algorithms, fraud detection, and software engineering, and is a useful resource for engineers, security professionals, academicians, researchers, and computer scientists. "This book provides a comprehensive understanding of how machine learning and large language models revolutionize the process of software defect detection"-- Provided by publisher. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condición: Neu. Automating Software Defect Detection Through Machine Learning and LLMs | Bryan Gardiner (u. a.) | Taschenbuch | Englisch | 2025 | IGI Global | EAN 9798337344614 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. Automating Software Defect Detection Through Machine Learning and LLMs | Bryan Gardiner (u. a.) | Buch | Englisch | 2025 | IGI Global | EAN 9798337344607 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.