Laxmi Shaw – författare

Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical applications in healthcare, offering an accessible guide tailored to the unique challenges of medical environments. It highlights how NLP technologies are revolutionizing patient care, medical documentation, and clinical decision-making while emphasizing ethical, legal, and interoperability considerations. Structured into four sections, the book begins by laying foundational knowledge in NLP and healthcare data, covering concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models, including BioBERT and ClinicalBERT, and emerging impacts of large language models like GPT.The applications section explores real-world implementations of intelligent assistants, such as virtual health chatbots, clinical documentation tools, conversational AI for patient engagement, and voice recognition integrated into electronic health records. Technical chapters provide insights into system architectures, evaluation metrics, data privacy, security, and interoperability standards like FHIR. The final section looks ahead to future directions including multilingual NLP, federated learning for privacy preservation, and the evolving landscape of AI-driven healthcare assistants. This book is an indispensable resource for a broad audience.Bridges AI research and healthcare practice with accessible, healthcare-focused NLP insights for clinical and operational useProvides practical guidance on designing and deploying intelligent virtual assistants to enhance patient care and engagementAddresses ethical, legal, and interoperability challenges unique to healthcare NLP applicationsExplores cutting-edge technologies, including large language models and federated learning in real-world medical contextsEquips data scientists and clinicians with tools to analyze unstructured medical data and improve clinical decision-making

Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical applications in healthcare, offering an accessible guide tailored to the unique challenges of medical environments. It highlights how NLP technologies are revolutionizing patient care, medical documentation, and clinical decision-making while emphasizing ethical, legal, and interoperability considerations. Structured into four sections, the book begins by laying foundational knowledge in NLP and healthcare data, covering concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models, including BioBERT and ClinicalBERT, and emerging impacts of large language models like GPT.The applications section explores real-world implementations of intelligent assistants, such as virtual health chatbots, clinical documentation tools, conversational AI for patient engagement, and voice recognition integrated into electronic health records. Technical chapters provide insights into system architectures, evaluation metrics, data privacy, security, and interoperability standards like FHIR. The final section looks ahead to future directions including multilingual NLP, federated learning for privacy preservation, and the evolving landscape of AI-driven healthcare assistants. This book is an indispensable resource for a broad audience. - Bridges AI research and healthcare practice with accessible, healthcare-focused NLP insights for clinical and operational use- Provides practical guidance on designing and deploying intelligent virtual assistants to enhance patient care and engagement- Addresses ethical, legal, and interoperability challenges unique to healthcare NLP applications- Explores cutting-edge technologies, including large language models and federated learning in real-world medical contexts- Equips data scientists and clinicians with tools to analyze unstructured medical data and improve clinical decision-making

Internet of Things (IoT) systems create a massive attack surface with billions of connected devices that often have weak default credentials and limited security capabilities, making them easy targets for cybercriminals to exploit at scale. Compromised IoT devices can serve as entry points for attackers to access valuable network resources, steal sensitive personal and business data, or launch large‑scale botnet attacks that disrupt critical infrastructure. Without proper security measures, IoT vulnerabilities can lead to serious consequences, including operational disruptions, safety hazards in critical systems like healthcare and transportation, and significant financial and legal penalties from regulatory noncompliance. IoT Cybersecurity: Trends, Challenges, and Solutions addresses the significant knowledge gap between rapidly deployed connected devices and understanding their unique security challenges. Highlights include:An efficient lightweight cryptography technique for enhancing IoT securityMachine learning approaches for IoT network threat detection and security optimizationUsing AI to enhance IoT-based intrusion detection systemsA study on emerging threats and vulnerabilitiesThis book presents research and insights into practice that explore security holes and effective solutions in the realm of IoT cybersecurity. Covering the evolving threat landscape in IoT environments, it sheds light on the intricacies of cybersecurity patterns and addresses the challenges that arise. This book is a resource offering innovative solutions, research findings, case studies, and practical insights related to securing IoT ecosystems.

Internet of Things (IoT) systems create a massive attack surface with billions of connected devices that often have weak default credentials and limited security capabilities, making them easy targets for cybercriminals to exploit at scale. Compromised IoT devices can serve as entry points for attackers to access valuable network resources, steal sensitive personal and business data, or launch large‑scale botnet attacks that disrupt critical infrastructure. Without proper security measures, IoT vulnerabilities can lead to serious consequences, including operational disruptions, safety hazards in critical systems like healthcare and transportation, and significant financial and legal penalties from regulatory noncompliance. IoT Cybersecurity: Trends, Challenges, and Solutions addresses the significant knowledge gap between rapidly deployed connected devices and understanding their unique security challenges. Highlights include:

An efficient lightweight cryptography technique for enhancing IoT security Machine learning approaches for IoT network threat detection and security optimization Using AI to enhance IoT-based intrusion detection systems A study on emerging threats and vulnerabilities

This book presents research and insights into practice that explore security holes and effective solutions in the realm of IoT cybersecurity. Covering the evolving threat landscape in IoT environments, it sheds light on the intricacies of cybersecurity patterns and addresses the challenges that arise. This book is a resource offering innovative solutions, research findings, case studies, and practical insights related to securing IoT ecosystems.