J. P. Ananth – författare

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11 produkter

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11 produkter

Nyhet
Classical and Quantum Principal Component Analysis in Data Engineering
AvAbhishek Kumar,J. P. Ananthm. fl.
Inbunden, Engelska, 2026
2 141 kr
Skickas inom 5-8 vardagar
This essential resource bridges the gap between classical data limitations and the future of computing, giving you the scalable, quantum-accelerated PCA strategies needed to conquer today’s massive, high-dimensional datasets. With the rapid growth of big data in fields such as genomics, internet traffic analysis, and social network data, traditional principal component analysis methods have reached their limits in terms of scalability and computational efficiency. This volume delves into cutting-edge advancements in principal component analysis (PCA), particularly focusing on its applications in handling high-dimensional and large-scale datasets. It also provides practical insights into how PCA can be applied to fields such as machine learning, bioinformatics, and finance. Through real-world case studies, hands-on examples, and guidance on implementing PCA using modern software tools and libraries, the book presents essential principles in quantum information theory and quantum algorithms, establishing the groundwork necessary to comprehend how quantum computing may expedite and improve PCA procedures. This work examines quantum algorithms for matrix decomposition, analyzes the computational benefits of quantum PCA compared to classical approaches, and showcases real applications in quantum machine learning, encryption, and quantum chemistry. Ultimately, this book will serve as a valuable resource for researchers, students, and professionals looking to the future of high-dimensional data analysis and how to apply efficient, scalable methods to PCA in their work.
Nyhet
Classical and Quantum Principal Component Analysis in Data Engineering
AvA. Jayanthiladevi,Navneet Kaurm. fl.
E-bok
PDF, Engelska, 2026
2 665 kr
Läs direkt efter köp
This essential resource bridges the gap between classical data limitations and the future of computing, giving you the scalable, quantum-accelerated PCA strategies needed to conquer today s massive, high-dimensional datasets. With the rapid growth of big data in fields such as genomics, internet traffic analysis, and social network data, traditional principal component analysis methods have reached their limits in terms of scalability and computational efficiency. This volume delves into cutting-edge advancements in principal component analysis (PCA), particularly focusing on its applications in handling high-dimensional and large-scale datasets. It also provides practical insights into how PCA can be applied to fields such as machine learning, bioinformatics, and finance. Through real-world case studies, hands-on examples, and guidance on implementing PCA using modern software tools and libraries, the book presents essential principles in quantum information theory and quantum algorithms, establishing the groundwork necessary to comprehend how quantum computing may expedite and improve PCA procedures. This work examines quantum algorithms for matrix decomposition, analyzes the computational benefits of quantum PCA compared to classical approaches, and showcases real applications in quantum machine learning, encryption, and quantum chemistry. Ultimately, this book will serve as a valuable resource for researchers, students, and professionals looking to the future of high-dimensional data analysis and how to apply efficient, scalable methods to PCA in their work.
Physiotherapy Using Artificial Intelligence
Enhancing Biomechanics for Optimal Rehabilitation
AvAbhishek Kumar,T. Ananth Kumarm. fl.
Inbunden, Engelska, 2026
2 557 kr
Skickas inom 5-8 vardagar
Physiotherapy Using Artificial Intelligence: Enhancing Biomechanics for Optimal RehabilitationEmpower your practice with this definitive resource that bridges the gap between artificial intelligence and biomechanics, providing the essential tools and knowledge to optimize assessments, personalize treatment plans, and predict recovery outcomes in the rapidly evolving landscape of modern physiotherapy.The integration of artificial intelligence (AI) with biomechanics in physiotherapy represents a transformative shift in the healthcare landscape, driven by rapid technological advancement and an increasing emphasis on personalized, data-driven care. Over the past decade, AI has progressed from theoretical exploration to practical clinical application, enabling enhanced decision-making and improved patient outcomes. This book examines the intersection of artificial intelligence and physiotherapy with a focused emphasis on biomechanics, exploring how AI can optimize biomechanical assessments, support individualized treatment planning, and predict patient progress in clinical settings. As demand grows for AI-driven innovation in rehabilitation, this volume serves as an essential resource for physiotherapists, clinicians, and researchers seeking to understand and adopt these emerging technologies to advance practice and improve rehabilitation outcomes.
Physiotherapy Using Artificial Intelligence
Enhancing Biomechanics for Optimal Rehabilitation
AvS. Oswalt Manoj,J. P. Ananthm. fl.
E-bok
Engelska, 2026
3 185 kr
Läs direkt efter köp
Physiotherapy Using Artificial Intelligence: Enhancing Biomechanics for Optimal Rehabilitation

Empower your practice with this definitive resource that bridges the gap between artificial intelligence and biomechanics, providing the essential tools and knowledge to optimize assessments, personalize treatment plans, and predict recovery outcomes in the rapidly evolving landscape of modern physiotherapy.

The integration of artificial intelligence (AI) with biomechanics in physiotherapy represents a transformative shift in the healthcare landscape, driven by rapid technological advancement and an increasing emphasis on personalized, data-driven care. Over the past decade, AI has progressed from theoretical exploration to practical clinical application, enabling enhanced decision-making and improved patient outcomes. This book examines the intersection of artificial intelligence and physiotherapy with a focused emphasis on biomechanics, exploring how AI can optimize biomechanical assessments, support individualized treatment planning, and predict patient progress in clinical settings. As demand grows for AI-driven innovation in rehabilitation, this volume serves as an essential resource for physiotherapists, clinicians, and researchers seeking to understand and adopt these emerging technologies to advance practice and improve rehabilitation outcomes.
Physiotherapy Using Artificial Intelligence
Enhancing Biomechanics for Optimal Rehabilitation
AvS. Oswalt Manoj,J. P. Ananthm. fl.
E-bok
PDF, Engelska, 2026
3 185 kr
Läs direkt efter köp
Empower your practice with this definitive resource that bridges the gap between artificial intelligence and biomechanics, providing the essential tools and knowledge to optimize assessments, personalize treatment plans, and predict recovery outcomes in the rapidly evolving landscape of modern physiotherapy.
The integration of artificial intelligence (AI) with biomechanics in physiotherapy represents a transformative shift in the healthcare landscape, driven by rapid technological advancement and an increasing emphasis on personalized, data-driven care. Over the past decade, AI has progressed from theoretical exploration to practical clinical application, enabling enhanced decision-making and improved patient outcomes. This book examines the intersection of artificial intelligence and physiotherapy with a focused emphasis on biomechanics, exploring how AI can optimize biomechanical assessments, support individualized treatment planning, and predict patient progress in clinical settings. As demand grows for AI-driven innovation in rehabilitation, this volume serves as an essential resource for physiotherapists, clinicians, and researchers seeking to understand and adopt these emerging technologies to advance practice and improve rehabilitation outcomes.
AI-driven Healthcare Innovations
Applications in Neurology and Medicine
AvJ. P. Ananth,Priya Battam. fl.
E-bok
PDF, Engelska, 2026
2 210 kr
Läs direkt efter köp
AI-driven Healthcare Innovations presents a timely and authoritative exploration of how artificial intelligence (AI) is transforming modern clinical practices and medical research. Positioned at the intersection of healthcare, data science and computational intelligence, this book provides a comprehensive context for understanding the growing role of AI in diagnosis, treatment and decision-making within neurology and broader medical domains. The book systematically examines core AI techniques, including machine learning (ML), deep learning (DL) and intelligent optimization, and demonstrates their practical deployment across neurological disorders, medical imaging, predictive analytics and personalized care. Emphasis is placed on real-world clinical workflows, data acquisition and preprocessing, model interpretability and performance evaluation. In addition, we also address ethical considerations, regulatory challenges and data security issues critical to healthcare adoption. By combining theoretical foundations with applied case studies and future research directions, this book serves as a valuable resource for researchers, clinicians, graduate students and industry professionals seeking to leverage AI-driven innovations to improve patient outcomes and advance next-generation healthcare systems.
AI-driven Healthcare Innovations
Applications in Neurology and Medicine
AvJ. P. Ananth,Priya Battam. fl.
E-bok
Engelska, 2026
2 216 kr
Läs direkt efter köp
AI-driven Healthcare Innovations presents a timely and authoritative exploration of how artificial intelligence (AI) is transforming modern clinical practices and medical research. Positioned at the intersection of healthcare, data science and computational intelligence, this book provides a comprehensive context for understanding the growing role of AI in diagnosis, treatment and decision-making within neurology and broader medical domains. The book systematically examines core AI techniques, including machine learning (ML), deep learning (DL) and intelligent optimization, and demonstrates their practical deployment across neurological disorders, medical imaging, predictive analytics and personalized care. Emphasis is placed on real-world clinical workflows, data acquisition and preprocessing, model interpretability and performance evaluation. In addition, we also address ethical considerations, regulatory challenges and data security issues critical to healthcare adoption. By combining theoretical foundations with applied case studies and future research directions, this book serves as a valuable resource for researchers, clinicians, graduate students and industry professionals seeking to leverage AI-driven innovations to improve patient outcomes and advance next-generation healthcare systems.
Agricultural Supply Chain Using Federated Learning
AvAbhishek Kumar,Pooja Dixitm. fl.
Inbunden, Engelska, 2026
2 349 kr
Skickas inom 5-8 vardagar
Master the next evolution of agricultural intelligence with this definitive guide to federated learning, providing decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty. As global agriculture faces challenges such as climate variability, resource inefficiency, and data privacy concerns, traditional centralized AI systems struggle to operate at scale. Federated learning addresses these limitations by enabling decentralized, privacy-preserving model training across distributed datasets, supporting secure and collaborative optimization. This book explores how federated learning enhances precision farming, logistics optimization, and sustainable resource management through real-time, data-driven decision-making while respecting local variations and regulatory constraints. It bridges the gap between advanced AI technologies and practical agricultural supply chain management, covering foundational concepts, system architectures, and real-world implementations. Through case studies and applied insights, the book demonstrates how federated learning can improve productivity, reduce waste, and strengthen sustainability while maintaining data sovereignty. It offers a balanced perspective on both technical and managerial aspects, making it accessible to a wide audience while retaining depth for academic and industry professionals.
Agricultural Supply Chain Using Federated Learning
AvS. Panneerselvam,S. Oswalt Manojm. fl.
E-bok
Engelska, 2026
2 152 kr
Läs direkt efter köp
Master the next evolution of agricultural intelligence with this definitive guide to federated learning, providing decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty. As global agriculture faces challenges such as climate variability, resource inefficiency, and data privacy concerns, traditional centralized AI systems struggle to operate at scale. Federated learning addresses these limitations by enabling decentralized, privacy-preserving model training across distributed datasets, supporting secure and collaborative optimization. This book explores how federated learning enhances precision farming, logistics optimization, and sustainable resource management through real-time, data-driven decision-making while respecting local variations and regulatory constraints. It bridges the gap between advanced AI technologies and practical agricultural supply chain management, covering foundational concepts, system architectures, and real-world implementations. Through case studies and applied insights, the book demonstrates how federated learning can improve productivity, reduce waste, and strengthen sustainability while maintaining data sovereignty. It offers a balanced perspective on both technical and managerial aspects, making it accessible to a wide audience while retaining depth for academic and industry professionals.
Agricultural Supply Chain Using Federated Learning
AvS. Panneerselvam,S. Oswalt Manojm. fl.
E-bok
PDF, Engelska, 2026
2 651 kr
Läs direkt efter köp
Master the next evolution of agricultural intelligence with this definitive guide to federated learning, providing decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty. As global agriculture faces challenges such as climate variability, resource inefficiency, and data privacy concerns, traditional centralized AI systems struggle to operate at scale. Federated learning addresses these limitations by enabling decentralized, privacy-preserving model training across distributed datasets, supporting secure and collaborative optimization. This book explores how federated learning enhances precision farming, logistics optimization, and sustainable resource management through real-time, data-driven decision-making while respecting local variations and regulatory constraints. It bridges the gap between advanced AI technologies and practical agricultural supply chain management, covering foundational concepts, system architectures, and real-world implementations. Through case studies and applied insights, the book demonstrates how federated learning can improve productivity, reduce waste, and strengthen sustainability while maintaining data sovereignty. It offers a balanced perspective on both technical and managerial aspects, making it accessible to a wide audience while retaining depth for academic and industry professionals.
AI-driven Healthcare Innovations
Applications in Neurology and Medicine
AvAbhishek Kumar,Priya Battam. fl.
Inbunden, Engelska, 2026
1 776 kr
Skickas inom 5-8 vardagar
AI-driven Healthcare Innovations presents a timely and authoritative exploration of how artificial intelligence (AI) is transforming modern clinical practices and medical research. Positioned at the intersection of healthcare, data science and computational intelligence, this book provides a comprehensive context for understanding the growing role of AI in diagnosis, treatment and decision-making within neurology and broader medical domains. The book systematically examines core AI techniques, including machine learning (ML), deep learning (DL) and intelligent optimization, and demonstrates their practical deployment across neurological disorders, medical imaging, predictive analytics and personalized care. Emphasis is placed on real-world clinical workflows, data acquisition and preprocessing, model interpretability and performance evaluation. In addition, we also address ethical considerations, regulatory challenges and data security issues critical to healthcare adoption. By combining theoretical foundations with applied case studies and future research directions, this book serves as a valuable resource for researchers, clinicians, graduate students and industry professionals seeking to leverage AI-driven innovations to improve patient outcomes and advance next-generation healthcare systems.

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J. P. Ananth – författare

Classical and Quantum Principal Component Analysis in Data Engineering

Classical and Quantum Principal Component Analysis in Data Engineering

Physiotherapy Using Artificial Intelligence

Physiotherapy Using Artificial Intelligence

Physiotherapy Using Artificial Intelligence

AI-driven Healthcare Innovations

AI-driven Healthcare Innovations

Agricultural Supply Chain Using Federated Learning

Agricultural Supply Chain Using Federated Learning

Agricultural Supply Chain Using Federated Learning

AI-driven Healthcare Innovations

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