Tripathi Padmesh (27 results)

Condition: As New. Unread book in perfect condition.

Condition: new.

Condition: As New. Unread book in perfect condition.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Condition: new. Hardcover. This essential book provides a comprehensive, expert-led guide on how federated learning can revolutionize crop yield, enhance resource management, and ensure a pathway to sustainable food quality and safety. The convergence of artificial intelligence, machine learning, and data science with agriculture and food, provides remarkable opportunities to improve quality, sustainability, and productivity in the agricultural sector. Federated Learning is a promising technology that has emerged at this intersection. In the context of smart agriculture, federated learning holds promise for improving crop yield, resource management, and decision-making. Additionally, federated learning provides greater clarity and understanding in the world of agriculture, encouraging stakeholders to explore and adopt this technology for improved farm management. Readers will find the book: Explores the integration of federated learning, a novel machine learning technique, into the realm of agriculture and food quality enhancement, showcasing the latest advancements;Introduces real-world applications of federated learning in agriculture, and demonstrates the way this technology can transform farming practices, crop monitoring, pest control, and food quality assurance;By bridging the fields of agriculture, machine learning, and food science, it offers a holistic perspective on leveraging technology to address challenges in food production and quality management;Emphasizes the importance of sustainability in agriculture, exploring how federated learning can contribute to more efficient resource utilization, reduced environmental impact, and the overall sustainability of food production systems;Discusses the future directions of smart agriculture and food quality enhancement, envisioning how federated learning and other emerging technologies can continue to shape the industry and address evolving challenges. Audience Agriculture specialists, agricultural engineers, professionals associated with food safety, crop managers, quality assurance professionals, IT professionals, data scientists, and academics working towards improved quality and sustainability in agriculture. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: New.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Condition: new. Hardcover. This essential book provides a comprehensive, expert-led guide on how federated learning can revolutionize crop yield, enhance resource management, and ensure a pathway to sustainable food quality and safety. The convergence of artificial intelligence, machine learning, and data science with agriculture and food, provides remarkable opportunities to improve quality, sustainability, and productivity in the agricultural sector. Federated Learning is a promising technology that has emerged at this intersection. In the context of smart agriculture, federated learning holds promise for improving crop yield, resource management, and decision-making. Additionally, federated learning provides greater clarity and understanding in the world of agriculture, encouraging stakeholders to explore and adopt this technology for improved farm management. Readers will find the book: Explores the integration of federated learning, a novel machine learning technique, into the realm of agriculture and food quality enhancement, showcasing the latest advancements;Introduces real-world applications of federated learning in agriculture, and demonstrates the way this technology can transform farming practices, crop monitoring, pest control, and food quality assurance;By bridging the fields of agriculture, machine learning, and food science, it offers a holistic perspective on leveraging technology to address challenges in food production and quality management;Emphasizes the importance of sustainability in agriculture, exploring how federated learning can contribute to more efficient resource utilization, reduced environmental impact, and the overall sustainability of food production systems;Discusses the future directions of smart agriculture and food quality enhancement, envisioning how federated learning and other emerging technologies can continue to shape the industry and address evolving challenges. Audience Agriculture specialists, agricultural engineers, professionals associated with food safety, crop managers, quality assurance professionals, IT professionals, data scientists, and academics working towards improved quality and sustainability in agriculture. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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

Condition: New.

Hardcover. Condition: Brand New. 400 pages. In Stock.

Condition: New.

Hardcover. Condition: new. Hardcover. This essential book provides a comprehensive, expert-led guide on how federated learning can revolutionize crop yield, enhance resource management, and ensure a pathway to sustainable food quality and safety. The convergence of artificial intelligence, machine learning, and data science with agriculture and food, provides remarkable opportunities to improve quality, sustainability, and productivity in the agricultural sector. Federated Learning is a promising technology that has emerged at this intersection. In the context of smart agriculture, federated learning holds promise for improving crop yield, resource management, and decision-making. Additionally, federated learning provides greater clarity and understanding in the world of agriculture, encouraging stakeholders to explore and adopt this technology for improved farm management. Readers will find the book: Explores the integration of federated learning, a novel machine learning technique, into the realm of agriculture and food quality enhancement, showcasing the latest advancements;Introduces real-world applications of federated learning in agriculture, and demonstrates the way this technology can transform farming practices, crop monitoring, pest control, and food quality assurance;By bridging the fields of agriculture, machine learning, and food science, it offers a holistic perspective on leveraging technology to address challenges in food production and quality management;Emphasizes the importance of sustainability in agriculture, exploring how federated learning can contribute to more efficient resource utilization, reduced environmental impact, and the overall sustainability of food production systems;Discusses the future directions of smart agriculture and food quality enhancement, envisioning how federated learning and other emerging technologies can continue to shape the industry and address evolving challenges. Audience Agriculture specialists, agricultural engineers, professionals associated with food safety, crop managers, quality assurance professionals, IT professionals, data scientists, and academics working towards improved quality and sustainability in agriculture. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condition: new. Paperback. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. 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. Condition: new. Hardcover. 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. Condition: new. Hardcover. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condition: Neu. Cyber Forensic Frameworks for User-Centric Human Threat Intelligence Analysis | Seifedine Kadry (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337348995 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Hardcover. Condition: new. Hardcover. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As cyber threats grow in scale, sophistication, and human involvement, traditional technical defenses struggle to protect digital ecosystems. Cyber forensic frameworks evolve toward user-centric human threat intelligence analysis, recognizing that attackers, insiders, and victims are all human actors with distinct behaviors, motivations, and decision patterns. By integrating behavioral analytics, digital trace evidence, and contextual user data, these frameworks enable investigators to move toward proactive threat identification and attribution. Focusing on the human dimension of cyber forensics may further enhance the accuracy of threat intelligence while supporting more adaptive, ethical, and resilient security strategies in complex digital environments. Cyber Forensic Frameworks for User-Centric Human Threat Intelligence Analysis explores the integration of cyber forensic methodologies with human threat intelligence to enhance security and investigative measures against sophisticated cyber threats. It provides a structured framework for analyzing human-driven cyber threats, incorporating behavioral analysis, AI, and forensic technologies. This book covers topics such as cybercrime, social engineering, and privacy rights, and is a useful resource for engineers, security professionals, academicians, researchers, and scientists.

Buch. Condition: Neu. Cyber Forensic Frameworks for User-Centric Human Threat Intelligence Analysis | Seifedine Kadry (u. a.) | Buch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337348988 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As cyber threats grow in scale, sophistication, and human involvement, traditional technical defenses struggle to protect digital ecosystems. Cyber forensic frameworks evolve toward user-centric human threat intelligence analysis, recognizing that attackers, insiders, and victims are all human actors with distinct behaviors, motivations, and decision patterns. By integrating behavioral analytics, digital trace evidence, and contextual user data, these frameworks enable investigators to move toward proactive threat identification and attribution. Focusing on the human dimension of cyber forensics may further enhance the accuracy of threat intelligence while supporting more adaptive, ethical, and resilient security strategies in complex digital environments. Cyber Forensic Frameworks for User-Centric Human Threat Intelligence Analysis explores the integration of cyber forensic methodologies with human threat intelligence to enhance security and investigative measures against sophisticated cyber threats. It provides a structured framework for analyzing human-driven cyber threats, incorporating behavioral analysis, AI, and forensic technologies. This book covers topics such as cybercrime, social engineering, and privacy rights, and is a useful resource for engineers, security professionals, academicians, researchers, and scientists.