Somenath Chakraborty – författare
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8 produkter
8 produkter
Häftad, Engelska, 2026
1 716 kr
Skickas inom 10-15 vardagar
Oral Cancer and Artificial Intelligence: Transforming Diagnosis, Treatment, and Research explores the intersection of artificial intelligence (AI) and oral cancer care. Sections detail how AI technologies are revolutionizing and evolving diagnosis, treatment, and management, offering new hope and improved outcomes for patients. The book is structured into ten well-defined sections, each addressing a critical aspect of oral cancer care enhanced by AI. It features contributions from leading experts in the fields of AI, oral cancer research, biomedical engineering, and clinical practice from prestigious, worldwide institutions who share valuable insights and their experiences in applying AI to transform oral cancer care. This book is an essential resource for researchers, clinicians, healthcare professionals, and students interested in the transformative potential of AI in oral cancer care. It offers a blend of theoretical knowledge, practical applications, and future directions, making it a comprehensive guide to understanding and leveraging AI to improve outcomes in oral cancer management.Provides an in-depth exploration of AI applications across all stages of oral cancer care, from early detection and diagnosis to treatment planning, prognosis, and patient supportFeatures insights and research from leading experts in AI, oral cancer, and biomedical engineeringIncludes real-world case studies and practical guidelines for integrating AI technologies into clinical workflows, making it a valuable resource for healthcare professionals and researchers
E-bok
Engelska, 20262 107 kr
Läs direkt efter köp
Oral Cancer and Artificial Intelligence: Transforming Diagnosis, Treatment, and Research explores the intersection of artificial intelligence (AI) and oral cancer care. Sections detail how AI technologies are revolutionizing and evolving diagnosis, treatment, and management, offering new hope and improved outcomes for patients. The book is structured into ten well-defined sections, each addressing a critical aspect of oral cancer care enhanced by AI. It features contributions from leading experts in the fields of AI, oral cancer research, biomedical engineering, and clinical practice from prestigious, worldwide institutions who share valuable insights and their experiences in applying AI to transform oral cancer care. This book is an essential resource for researchers, clinicians, healthcare professionals, and students interested in the transformative potential of AI in oral cancer care. It offers a blend of theoretical knowledge, practical applications, and future directions, making it a comprehensive guide to understanding and leveraging AI to improve outcomes in oral cancer management. - Provides an in-depth exploration of AI applications across all stages of oral cancer care, from early detection and diagnosis to treatment planning, prognosis, and patient support- Features insights and research from leading experts in AI, oral cancer, and biomedical engineering- Includes real-world case studies and practical guidelines for integrating AI technologies into clinical workflows, making it a valuable resource for healthcare professionals and researchers
- Nyhet
E-bok
PDF, Engelska, 2026811 kr
Läs direkt efter köp
The integration ML with robotics and weaponry is revolutionizing mechanical engineering by enabling intelligent systems that can adapt, learn, and operate autonomously. In robotics, ML allows systems to process vast amounts of data from sensors to make real-time decisions. Robots, whether in industrial settings or autonomous vehicles, can navigate environments, recognize objects, and optimize tasks through reinforcement learning algorithms. In military applications, robotics combined with ML enhances autonomous weapon systems. Unmanned aerial vehicles (UAVs) and autonomous ground systems are increasingly utilized for surveillance, targeting, and even combat roles. These systems employ ML to improve target recognition, threat analysis, and adaptive decision-making in dynamic battle environments . This reduces human risk in conflict zones and can lead to more precise operational outcomes. Mechanical engineering plays a critical role in designing the physical systems that enable robotic mobility, structure, and function. Advanced mechanical systems integrate machine learning for predictive maintenance, fault diagnosis, and condition monitoring in weaponry and industrial robotics.Mechanical engineers design robots with complex actuators, sensors, and control mechanisms that respond to real-time data processed by machine learning algorithms. The combination of robotics, ML, and mechanical engineering is driving the development of next-generation intelligent systems. These innovations not only improve automation but are also crucial for defence systems, manufacturing, and autonomous vehicle technologies. This synergy promises greater efficiency, adaptability, and autonomy in a range of applications.Key Features: Highlights Real-World Applications Explores Advanced AI Techniques Addresses Ethical and Security Concerns Equips Readers with Hands-On Knowledge Forecasts Future Technological Trends
- Nyhet
E-bok
Engelska, 2026811 kr
Läs direkt efter köp
The integration ML with robotics and weaponry is revolutionizing mechanical engineering by enabling intelligent systems that can adapt, learn, and operate autonomously. In robotics, ML allows systems to process vast amounts of data from sensors to make real-time decisions. Robots, whether in industrial settings or autonomous vehicles, can navigate environments, recognize objects, and optimize tasks through reinforcement learning algorithms. In military applications, robotics combined with ML enhances autonomous weapon systems. Unmanned aerial vehicles (UAVs) and autonomous ground systems are increasingly utilized for surveillance, targeting, and even combat roles. These systems employ ML to improve target recognition, threat analysis, and adaptive decision-making in dynamic battle environments . This reduces human risk in conflict zones and can lead to more precise operational outcomes. Mechanical engineering plays a critical role in designing the physical systems that enable robotic mobility, structure, and function. Advanced mechanical systems integrate machine learning for predictive maintenance, fault diagnosis, and condition monitoring in weaponry and industrial robotics.Mechanical engineers design robots with complex actuators, sensors, and control mechanisms that respond to real-time data processed by machine learning algorithms. The combination of robotics, ML, and mechanical engineering is driving the development of next-generation intelligent systems. These innovations not only improve automation but are also crucial for defence systems, manufacturing, and autonomous vehicle technologies. This synergy promises greater efficiency, adaptability, and autonomy in a range of applications.Key Features: Highlights Real-World Applications Explores Advanced AI Techniques Addresses Ethical and Security Concerns Equips Readers with Hands-On Knowledge Forecasts Future Technological Trends
- Nyhet
Inbunden, Engelska, 2026
1 805 kr
Skickas inom 10-15 vardagar
The integration ML with robotics and weaponry is revolutionizing mechanical engineering by enabling intelligent systems that can adapt, learn, and operate autonomously. In robotics, ML allows systems to process vast amounts of data from sensors to make real-time decisions. Robots, whether in industrial settings or autonomous vehicles, can navigate environments, recognize objects, and optimize tasks through reinforcement learning algorithms. In military applications, robotics combined with ML enhances autonomous weapon systems. Unmanned aerial vehicles (UAVs) and autonomous ground systems are increasingly utilized for surveillance, targeting, and even combat roles. These systems employ ML to improve target recognition, threat analysis, and adaptive decision-making in dynamic battle environments . This reduces human risk in conflict zones and can lead to more precise operational outcomes. Mechanical engineering plays a critical role in designing the physical systems that enable robotic mobility, structure, and function. Advanced mechanical systems integrate machine learning for predictive maintenance, fault diagnosis, and condition monitoring in weaponry and industrial robotics.Mechanical engineers design robots with complex actuators, sensors, and control mechanisms that respond to real-time data processed by machine learning algorithms. The combination of robotics, ML, and mechanical engineering is driving the development of next-generation intelligent systems. These innovations not only improve automation but are also crucial for defence systems, manufacturing, and autonomous vehicle technologies. This synergy promises greater efficiency, adaptability, and autonomy in a range of applications.Key Features:Highlights Real-World ApplicationsExplores Advanced AI TechniquesAddresses Ethical and Security ConcernsEquips Readers with Hands-On KnowledgeForecasts Future Technological Trends
Inbunden, Engelska, 2026
1 978 kr
Skickas inom 3-6 vardagar
GANs represent a generative model leveraging the growing power of deep learning (DL) algorithms. This book presents the breakthroughs, applications and latest research innovations of GANs and of quantum GANs. In particular, the book includes contributions on Autoencoders and Variational Autoencoders, Semi-Supervised GANs, Quantum GANs and Quantum Data Privacy, and Quantum GANs for image manipulation.
Häftad, Engelska, 2025
1 084 kr
Skickas inom 3-6 vardagar
Our proposed book emerges in response to the critical need for an interdisciplinary resource that encapsulates the burgeoning role of artificial intelligence (AI) in reshaping drug discovery and telemedicine. As these sectors witness transformative changes driven by AI technologies, there's a pressing demand for a comprehensive guide that navigates through these advancements, offering insights, methodologies, and practical applications to professionals at the forefront of healthcare and pharmaceutical research. At its core, the book delves into the intricate ways in which AI and machine learning algorithms are being harnessed to streamline the drug development process, from initial discovery through to clinical trials, and how these technologies are concurrently revolutionizing the delivery of healthcare services via telemedicine. Specific focus areas include the application of deep learning in identifying novel drug candidates, AI-driven predictive models for pharmacokinetics and pharmacodynamics, automation in laboratory research, and the integration of AI in diagnostic processes, personalized medicine, and patient monitoring systems. Each chapter not only explores current state-of-the-art methodologies and case studies but also critically examines challenges, such as data privacy, ethical considerations, and the need for robust, interpretable models that can be trusted by healthcare professionals and patients alike. Furthermore, the book places a strong emphasis on the synergistic potential of combining AI with telemedicine, illustrating how these technologies can expand access to healthcare, improve the accuracy of remote diagnoses, and enable continuous, data-driven patient care. By providing a panoramic view of current trends, technological innovations, and future directions, the book aims to serve as a pivotal reference for scientists, researchers, clinicians, and policymakers involved in drug discovery and healthcare delivery. In conclusion, this book stands as an essential compendium for specialists seeking to navigate the complexities and harness the opportunities presented by AI in the pharmaceutical and healthcare industries. It offers a critical, in-depth exploration of the transformative impact of AI technologies, underscoring their relevance and potential to dramatically enhance drug discovery and telemedicine practices. This publication not only equips its target audience with the knowledge to lead innovation in their fields but also engages with the broader ethical, social, and practical implications of AI, making it an invaluable resource for advancing towards more effective, efficient, and accessible healthcare solutions. The book is significant for several reasons: Interdisciplinary Appeal: It serves as a critical resource for professionals and researchers across the fields of computer science, pharmaceutical sciences, and healthcare, facilitating a deeper understanding of AI's potential and fostering interdisciplinary collaborations. Innovation in Drug Discovery: By highlighting novel AI methodologies in drug discovery, the book offers insights into how these technologies can shorten the development timelines, reduce costs, and increase the success rates of new therapies, which is crucial for addressing unmet medical needs. Revolutionizing Telemedicine: The detailed discussion on AI's role in telemedicine illustrates how these advancements can enhance access to healthcare, improve the quality of care, and make healthcare systems more efficient, especially in remote and underserved areas. Ethical and Regulatory Considerations: It likely addresses the ethical, privacy, and regulatory challenges associated with implementing AI in healthcare, offering guidelines for navigating these complexities while maximizing patient benefits. Future Directions: By exploring current trends and future possibilities, the book not only serves as a repository of current knowledge but also as a beacon for future research and development efforts in these rapidly evolving fields.
E-bok
PDF, Engelska, 20251 361 kr
Läs direkt efter köp
Our proposed book emerges in response to the critical need for an interdisciplinary resource that encapsulates the burgeoning role of artificial intelligence (AI) in reshaping drug discovery and telemedicine. As these sectors witness transformative changes driven by AI technologies, there's a pressing demand for a comprehensive guide that navigates through these advancements, offering insights, methodologies, and practical applications to professionals at the forefront of healthcare and pharmaceutical research. At its core, the book delves into the intricate ways in which AI and machine learning algorithms are being harnessed to streamline the drug development process, from initial discovery through to clinical trials, and how these technologies are concurrently revolutionizing the delivery of healthcare services via telemedicine. Specific focus areas include the application of deep learning in identifying novel drug candidates, AI-driven predictive models for pharmacokinetics and pharmacodynamics, automation in laboratory research, and the integration of AI in diagnostic processes, personalized medicine, and patient monitoring systems. Each chapter not only explores current state-of-the-art methodologies and case studies but also critically examines challenges, such as data privacy, ethical considerations, and the need for robust, interpretable models that can be trusted by healthcare professionals and patients alike. Furthermore, the book places a strong emphasis on the synergistic potential of combining AI with telemedicine, illustrating how these technologies can expand access to healthcare, improve the accuracy of remote diagnoses, and enable continuous, data-driven patient care. By providing a panoramic view of current trends, technological innovations, and future directions, the book aims to serve as a pivotal reference for scientists, researchers, clinicians, and policymakers involved in drug discovery and healthcare delivery. In conclusion, this book stands as an essential compendium for specialists seeking to navigate the complexities and harness the opportunities presented by AI in the pharmaceutical and healthcare industries. It offers a critical, in-depth exploration of the transformative impact of AI technologies, underscoring their relevance and potential to dramatically enhance drug discovery and telemedicine practices. This publication not only equips its target audience with the knowledge to lead innovation in their fields but also engages with the broader ethical, social, and practical implications of AI, making it an invaluable resource for advancing towards more effective, efficient, and accessible healthcare solutions. The book is significant for several reasons: Interdisciplinary Appeal: It serves as a critical resource for professionals and researchers across the fields of computer science, pharmaceutical sciences, and healthcare, facilitating a deeper understanding of AI's potential and fostering interdisciplinary collaborations. Innovation in Drug Discovery: By highlighting novel AI methodologies in drug discovery, the book offers insights into how these technologies can shorten the development timelines, reduce costs, and increase the success rates of new therapies, which is crucial for addressing unmet medical needs. Revolutionizing Telemedicine: The detailed discussion on AI's role in telemedicine illustrates how these advancements can enhance access to healthcare, improve the quality of care, and make healthcare systems more efficient, especially in remote and underserved areas. Ethical and Regulatory Considerations: It likely addresses the ethical, privacy, and regulatory challenges associated with implementing AI in healthcare, offering guidelines for navigating these complexities while maximizing patient benefits. Future Directions: By exploring current trends and future possibilities, the book not only serves as a repository of current knowledge but also as a beacon for future research and development efforts in these rapidly evolving fields.