Symbiotic intelligence advancing forecasting through human-AI collaboration / editors, Hamid Jahankhani, Northumbria University London, London, UK, Gordon Bowen, Anglia Ruskin University, Cambridge, UK, Nitsa J. Herzog, Northumbria University London Campus, London, UK, David J. Herzog, Computer Science, London Campus of Ulster, Solent and Roehampton Universities, UK.
| Format | Electronic |
| Edition | First edition. |
| Publication Info | Boca Raton : CRC Press, Taylor & Francis Group, 2026. |
| Description | viii, 334 pages illustrations 24 cm |
| Supplemental Content | Full text available from eBooks on EBSCOhost |
| Subjects |
| Other author/creator | Jahankhani, Hamid http://id.loc.gov/authorities/names/no2008146571 http://id.loc.gov/rwo/agents/no2008146571. |
| Other author/creator | Bowen, Gordon, 1950 http://id.loc.gov/authorities/names/n2014054777 http://id.loc.gov/rwo/agents/n2014054777. |
| Other author/creator | Herzog, Nitsa J., 1969 http://id.loc.gov/authorities/names/n2025028692 http://id.loc.gov/rwo/agents/n2025028692. |
| Other author/creator | Herzog, David J., 1971 http://id.loc.gov/authorities/names/n2025028693 http://id.loc.gov/rwo/agents/n2025028693. |
| Physical medium | illustrations |
| Contents | Large language models : strengths and limitations of (generative) AI clinical reasoning / David J. Herzog and Nitsa J. Herzog -- Utilizing deep learning to diagnose autism in children through facial feature analysis / Eboagu Chiamaka Chinwe and Nitsa J. Herzog -- The role of predictive analytics and artificial intelligence in cybersecurity / Baba Shaheer -- Application of deep learning (BERT model) to predict bitcoin sentiment using X (Twitter) data / Dilek Celik, Niloufar Karimian and Ahmet Celik -- Data pipeline modelling and implementation for real-time processing of job listings from online platforms / Adnane Elmotassadeq and Dilek Celik -- Using generative AI artificial intelligence to re-shape key performance indicators (KPIs) / Alnassarwin, Muawia Salman S., Bowen, Gordon and Alnasraween, Mo'en Salman S. -- Reimagining Industry 5.0 : generative artificial intelligence as the enabling technology / Amin Hosseinian-Far, Maryam Farsi, Koorosh Gharehbaghi, Hamid Jahankhani and Alireza Daneshkhah -- Data-driven future : a review of technologies and applications in the era of Industry 5.0 / Idowu Mosaku, Opeyemi Ajewole, Seun Adegoke and Amin Hosseinian-Far -- End-to-end project : vehicle counting and traffic volume forecasting / Omar Gamal AbdElKhalek Mohamed ElSherbiny and Nitsa J. Herzog -- Agriculture forecasting : predicting commodity producer prices and yield / Amela Karmaj and Nitsa J. Herzog -- Supporting sustainable supply chains with explainable artificial intelligence / Elias Pimenidis and John Patsavellas -- QRNG-powered quantum-resistant blockchain for secure and scalable IoT healthcare machine learning models / Osama Hussien, Anil Kaya, Hamid Jahankhani and Abdul Salih -- Symbiotic intelligence in forecasting cyber-enabled financial crime / Natasha Omezi and Hamid Jahankhani -- Biomedical digital twin as a part of the Industry 5.0 : conceptual view / David J. Herzog, Nitsa J. Herzog and Mhd Saeed Sharif -- XAI-driven insights-unravelling London's housing market trends and predictions / Rizwan R. Khan and Nitsa J. Herzog. |
| Abstract | "The requirement for how AI Demand will influence business change and thus influence organisations and governments is becoming important. This book is designed as a roadmap to help decision makers in the future of AI. Thus, the book builds on the momentum that AI has generated to be informative and aide memoire of AI Demand"-- Provided by publisher. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2025025353 |
| ISBN | 9781032867687 hbk |
| ISBN | 9781032889269 pbk |
| ISBN | 9781003540373 ebk |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |