Search results for ""author rajesh kumar dhanaraj""

DEEP LEARNING FOR TREATMENTS The book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc. Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare. Audience The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.

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QUANTUM BLOCKCHAIN While addressing the security challenges and threats in blockchain, this book is also an introduction to quantum cryptography for engineering researchers and students in the realm of information security. Quantum cryptography is the science of exploiting quantum mechanical properties to perform cryptographic tasks. By utilizing unique quantum features of nature, quantum cryptography methods offer everlasting security. The applicability of quantum cryptography is explored in this book. It describes the state-of-the-art of quantum blockchain techniques and sketches how they can be implemented in standard communication infrastructure. Highlighting a wide range of topics such as quantum cryptography, quantum blockchain, post-quantum blockchain, and quantum blockchain in Industry 4.0, this book also provides the future research directions of quantum blockchain in terms of quantum resilience, data management, privacy issues, sustainability, scalability, and quantum blockchain interoperability. Above all, it explains the mathematical ideas that underpin the methods of post-quantum cryptography security. Readers will find in this book a comprehensiveness of the subject including: The key principles of quantum computation that solve the factoring issue. A discussion of a variety of potential post-quantum public-key encryption and digital signature techniques. Explanations of quantum blockchain in cybersecurity, healthcare, and Industry 4.0. Audience The book is for security analysts, data scientists, vulnerability analysts, professionals, academicians, researchers, industrialists, and students working in the fields of (quantum) blockchain, cybersecurity, cryptography, and artificial intelligence with regard to smart cities and Internet of Things.

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Big data – unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing and claims handling and incentivize risk reduction – is a relatively recent development in the insurance industry, the data sets previously being too impossibly great to analyse through traditional methods. However, with the global capacity to collect and store data growing alongside advancements in AI and machine learning technology, insurers need to seriously evaluate their technology stacks to ensure they can remain competitive and respond to growing customer demand. Striking a balance between the technical characteristics of the subject and the practical aspects of decision making, spanning from fraud analytics in claims management, to customer analytics, to risk analytics in solvency, the comprehensive coverage presented makes Big Data an invaluable resource for any insurance professional. Providing high quality academic research, Emerald Studies in Finance, Insurance, and Risk Management provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale. Subjects of interest may include banking, accounting, auditing, compliance, sustainability, behaviour, management, and business economics.

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SMART GRIDS AND INTERNET OF THINGS Smart grids and the Internet of Things (IoT) are rapidly changing and complicated subjects that are constantly changing and developing. This new volume addresses the current state-of-the-art concepts and technologies associated with the technologies and covers new ideas and emerging novel technologies and processes. Internet of Things (IoT) is a self-organized network that consists of sensors, software, and devices. The data is exchanged among them with the help of the internet. Smart Grids (SG) is a collection of devices deployed in larger areas to perform continuous monitoring and analysis in that region. It is responsible for balancing the flow of energy between the servers and consumers. SG also takes care of the transmission and distribution power to the components involved. The tracking of the devices present in SG is achieved by the IoT framework. Thus, assimilating IoT and SG will lead to developing solutions for many real-time problems. This exciting new volume covers all of these technologies, including the basic concepts and the problems and solutions involved with the practical applications in the real world. Whether for the veteran engineer or scientist, the student, or a manager or other technician working in the field, this volume is a must-have for any library. Smart Grids and Internet of Things: Presents Internet of Things (IoT) and smart grid (SG)-integrated frameworks along with their components and technologies Covers the challenges in energy harvesting and sustainable solutions for IoTSGs and their solutions for practical applications Describes and demystifies the privacy and security issues while processing data in IoTSG Includes case studies relating to IoTSG with cloud and fog computing machine learning and blockchain

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Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

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