KI-Beratung Beta
Vor Ort
Merkzettel Warenkorb

Gutscheinbedingungen

**Gültig bis 05.10.2026 ab einem Mindestbestellwert von 30€ auf Spielzeug, Schreibwaren, Filme, Geschenke & Trends, Musik, tolino eReader & Zubehör, Hörbücher und Hörbuch-Downloads, nicht preisgebundene Bücher und Kalender online auf thalia.at und in der Thalia App. Einzelne Artikel können ausgeschlossen sein. Aufgrund der Buchpreisbindung sind deutschsprachige Bücher und eBooks ausgenommen. Zusätzlich ausgenommen sind Abos & Flatrates, Games, Geschenkkarten/-boxen, Shelfies, Software, Zeitschriften sowie einzelne Artikel von tonies®. Pro Einkauf einmal einlösbar. Nur gültig mit im Onlineshop hinterlegter Bonuscard. Click & Collect nur bei Onlinevorabzahlung möglich. Keine Barauszahlung. Nicht kombinierbar mit anderen Aktionen und Gutscheinen. Gutschein wird auf max. 500€ Bestellwert angerechnet. Nicht gültig für Versandkosten und Services. Preisgebundene Artikel sind vom Mindestbestellwert ausgeschlossen.
Produktbild: Natural Language Processing for Healthcare
- 13%

Natural Language Processing for Healthcare The Rise of Intelligent Assistants

Aus der Reihe Advances in ubiquitous sensing applications for healthcare
13% sparen

165,99 € UVP 191,20 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

26.03.2026

Herausgeber

Laxmi Shaw + weitere

Verlag

Elsevier Science & Technology

Seitenzahl

444

Maße (L/B/H)

23,6/19/2,4 cm

Gewicht

1139 g

Sprache

Englisch

ISBN

978-0-443-45252-9

Beschreibung

Portrait

Dr. Laxmi Shaw is a Researcher and Faculty at Texas A&M University-Victoria, United States, where her work centres on adversarial machine learning, large language models (LLMs), healthcare analytics, fraud detection, and energy management systems. She was previously a Postdoctoral Scholar at Texas State University and a Senior Postdoctoral Fellow (Volunteer) at the University of Texas at Austin. She has worked on projects with Samsung Research & Development and Carrier Corporation (UTC-HRDC). With over a decade of combined research and industry experience, Dr. Shaw has co-authored 5 books and published more than 40 peer-reviewed papers in journals, international conferences, and edited volumes. Her research spans AI/ML security, EEG signal processing, IoT-enabled anomaly detection, Siamese networks, adversarial robustness in LLMs, and GPU-accelerated healthcare analytics. She is a Senior Member of IEEE and an active reviewer for several journals.She earned her Ph.D. in Electrical Engineering with a specialization in Artificial Intelligence and Machine Learning from the prestigious Indian Institute of Technology (IIT) Kharagpur, India. She also holds a Master of Technology (M.Tech) in Instrumentation and Electronics Engineering from Jadavpur University, and a Bachelor of Engineering (B.E.) in Electronics and Instrumentation Engineering from Sambalpur University, Odisha. She has authored three books and over 35 peer-reviewed papers on AI/ML security, EEG processing, IoT anomaly detection, and GPU-accelerated healthcare analytics. A Senior IEEE member and award-winning researcher, she actively reviews for leading journals and is committed to ethical, explainable, and secure AI, especially in healthcare and adversarial contexts.

Dr. Shubham Mahajan is an academic and researcher, member of IEEE, ACM, and IAENG. He earned a B.Tech from Baba Ghulam Shah Badshah University, an M.Tech from Chandigarh University, and a PhD from Shri Mata Vaishno Devi University. He is currently Assistant Professor at Amity University, Haryana. His research spans artificial intelligence and image processing, including video compression, image segmentation, fuzzy entropy, nature-inspired optimization, data mining, machine learning, robotics, and optical communications. He holds patents internationally and has published widely in high-impact venues; he has edited several Scopus-indexed books. He has received multiple awards for research excellence and travel support from IEEE, among others. He has served as IEEE Campus Ambassador at premier institutes and promotes international collaborations. He participates in technical program committees and editorial boards for conferences and journals, shaping discourse in AI and image processing.

Dr. Kamal Upreti is an Associate Professor of Computer Science at CHRIST (Deemed to be University), Ghaziabad. He holds , a Ph.D. in Computer Science & Engineering, and a postdoctoral fellowship at National Taipei University of Business, Taiwan, funded by MHRD.

With teaching, research, and industry exposure, he has produced numerous patents and publications. His interests span modern physics, data analytics, cybersecurity, ML, healthcare, embedded systems, and cloud computing. Notable projects include Hydrastore in Japan, IPDS in India, and an ICMR-funded cardiovascular-prediction project with GB Pant and AIIMS Delhi.

Dr. Upreti serves as session chair, keynote speaker, trainer, and faculty developer, and has been honored as Best Teacher, Best Researcher, and an M.Tech Gold Medalist.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

26.03.2026

Herausgeber

Verlag

Elsevier Science & Technology

Seitenzahl

444

Maße (L/B/H)

23,6/19/2,4 cm

Gewicht

1139 g

Sprache

Englisch

ISBN

978-0-443-45252-9

EU-Ansprechpartner

Elsevier B.V.
Radarweg 29
1043 NX Amsterdam
NL
productsafety@elsevier.com

Herstelleradresse

Elsevier Science & Technology
London Wall 125
EC2Y 5AS London
GB
tradeorders@elsevier.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Natural Language Processing for Healthcare

  • Section I: Foundations of NLP in Healthcare
    1. The Digital Health Revolution: Natural Language Processing Technologies Reshaping Patient Care and Medical Documentation
    2. Large Language Models and Generative AI in Healthcare: Multimodal Intelligence, Clinical Integration, and the Future of Medical Practice
    3. Navigating the Utility of Generative Artificial Intelligence in Healthcare Delivery
    4. GENERATIVE ARTIFICIAL INTELLIGENCE IN MEDICINE

    Section II: Core Technologies and Approaches
    5. Advancing Patient Care with Conversational AI: Applications, Challenges, and Future Directions
    6. The Voice Revolution in Medicine: Reshaping Clinical Workflows with Voice Assistants and Speech Recognition
    7. MACHINES THAT UNDERSTAND ILLNESS: Natural Language Processing based hospital kiosk systems
    8. Telehealth Workspaces for Healthcare Providers

    Section III: Applications and Case Studies
    9. AI-Driven Innovations in Infectious Disease Detection and Control
    10. Depression Identification from Social Media using n-gram based Deep Neural Network
    11. HeaLytix: Comparative Analysis of Classification Algorithms and Deep Learning Optimizers For Cardiac Disease Detection
    12. 3D U-Net based Segmentation of Liver Vessels from Computed Tomography Images
    13. Revolutionizing Patient Care with Digital Twins: A Smart Healthcare Perspective

    Section IV: Global, Ethical, and Technical Challenges
    14. Legal And Regulatory Compliance In Digital Twin - Enabled Healthcare
    15. Multilingual NLP, Personalisation, and Global Health
    16. AI for Multilingual, Human Centered Personalization, and Public Health
    17. Data Privacy, Security, and Ethics in Medical NLP
    18. Federated Learning, Explainability, and the Road Ahead