Shailendra Pratap Singh – författare

Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources.Provides a complete overview of the challenges and pain points in implementing AI-driven diagnostics and disease prediction within the healthcare industryExplores various edge computing applications in healthcare and the use of hardware acceleration for AI applicationsPresents a comprehensive resource that bridges the gap between AI and healthcare

Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources. - Provides a complete overview of the challenges and pain points in implementing AI-driven diagnostics and disease prediction within the healthcare industry- Explores various edge computing applications in healthcare and the use of hardware acceleration for AI applications- Presents a comprehensive resource that bridges the gap between AI and healthcare

This laboratory reference compiles the essential protocols used in cyanobacterial research. Cyanobacteria is a model system for biofuel production and sequestration of carbon dioxide to tackle global climate change due to increasing levels of greenhouse gases. Topics range from identification and growth parameters to biomolecule estimation and isolation. It also covers genome analysis and bioengineering of cyanobacteria for novel products.Key Features:Provides step-by-step procedures and troubleshooting tipsCovers identification, growth parameters and pigment estimation methodologiesDescribes isolation, characterization and purification of microcystin, phycobiliproteins and scytonemin from cyanobacteriaDiscusses genomics, proteomics and bioengineering of cyanobacteria from novel productsExplains sample preparation and visualization protocol for electron microscopy-based analysis of cyanobacteriaThis collection is useful to students and researchers in life sciences. It is also meant for industry experts who are involved in the production of biofuels, biofertilizers, other value-added products, and carbon sequestration using cyanobacteria.

The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.

This laboratory reference compiles the essential protocols used in cyanobacterial research. Cyanobacteria is a model system for biofuel production and sequestration of carbon dioxide to tackle global climate change due to increasing levels of greenhouse gases. Topics range from identification and growth parameters to biomolecule estimation and isolation. It also covers genome analysis and bioengineering of cyanobacteria for novel products.

Key Features:

Provides step-by-step procedures and troubleshooting tips Covers identification, growth parameters and pigment estimation methodologies Describes isolation, characterization and purification of microcystin, phycobiliproteins and scytonemin from cyanobacteria Discusses genomics, proteomics and bioengineering of cyanobacteria from novel products Explains sample preparation and visualization protocol for electron microscopy-based analysis of cyanobacteria

This collection is useful to students and researchers in life sciences. It is also meant for industry experts who are involved in the production of biofuels, biofertilizers, other value-added products, and carbon sequestration using cyanobacteria.

This laboratory reference compiles the essential protocols used in cyanobacterial research. Cyanobacteria is a model system for biofuel production and sequestration of carbon dioxide to tackle global climate change due to increasing levels of greenhouse gases. Topics range from identification and growth parameters to biomolecule estimation and isolation. It also covers genome analysis and bioengineering of cyanobacteria for novel products.

Key Features:

Provides step-by-step procedures and troubleshooting tips Covers identification, growth parameters and pigment estimation methodologies Describes isolation, characterization and purification of microcystin, phycobiliproteins and scytonemin from cyanobacteria Discusses genomics, proteomics and bioengineering of cyanobacteria from novel products Explains sample preparation and visualization protocol for electron microscopy-based analysis of cyanobacteria

This collection is useful to students and researchers in life sciences. It is also meant for industry experts who are involved in the production of biofuels, biofertilizers, other value-added products, and carbon sequestration using cyanobacteria.

The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.

The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.

This book discusses how Cloud, Fog, and Edge computing, alongside federated learning and blockchain, are revolutionizing healthcare systems by addressing the challenges of data management, privacy, and efficiency. Targeted at healthcare technology researchers, professionals, and advanced students, it explores how these technologies enhance patient care, data security, and organizational effectiveness. The book provides a detailed overview of how Cloud, Fog, and Edge computing work in healthcare, focusing on real-time data processing and secure data sharing. It covers integrating federated learning for privacy-preserving AI models, blockchain for ensuring data integrity, and the technical and regulatory challenges of implementing these systems in healthcare settings. Real-world case studies illustrate successful applications, while practical advice helps navigate common obstacles. A must-read for anyone involved in healthcare delivery, research, or policy, this book offers invaluable insights into the future of healthcare technology. It equips healthcare professionals and technologists with the knowledge to leverage these emerging tools to improve patient outcomes, safeguard electronic health records, and streamline healthcare delivery systems.