Startseite Mathematik 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT
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1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT

  • Songita Sett , Soumita Seth und Saurav Mallik
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Drug Discovery and Telemedicine
Ein Kapitel aus dem Buch Drug Discovery and Telemedicine

Abstract

Rapid technological convergence is changing businesses and transforming conventional procedures in fields like research and healthcare. This chapter highlights the revolutionary functions and interrelated applications of the core concepts of drug discovery, telemedicine, artificial intelligence (AI), computer vision (CV), and the Internet of Things (IoT). Artificial intelligence (AI)-driven predictive models and computational tools have transformed drug discovery, which was once a time-consuming and costly process. This has accelerated the development of new therapies. Particularly in remote areas, telemedicine has helped close the healthcare access gap, thanks to wearable technology and digital communication tools. In domains such as autonomous systems and diagnostics, AI and CV are improving decision-making, automating difficult processes, and increasing precision. In order to provide real-time monitoring and predictive analytics across multiple industries, the IoT ecosystem includes smart devices. These technologies work together to create a data-driven future that is innovative, efficient, and accessible. This chapter provides a thorough overview of their core ideas and applications, along with thoughts on how they could revolutionize today’s society.

Abstract

Rapid technological convergence is changing businesses and transforming conventional procedures in fields like research and healthcare. This chapter highlights the revolutionary functions and interrelated applications of the core concepts of drug discovery, telemedicine, artificial intelligence (AI), computer vision (CV), and the Internet of Things (IoT). Artificial intelligence (AI)-driven predictive models and computational tools have transformed drug discovery, which was once a time-consuming and costly process. This has accelerated the development of new therapies. Particularly in remote areas, telemedicine has helped close the healthcare access gap, thanks to wearable technology and digital communication tools. In domains such as autonomous systems and diagnostics, AI and CV are improving decision-making, automating difficult processes, and increasing precision. In order to provide real-time monitoring and predictive analytics across multiple industries, the IoT ecosystem includes smart devices. These technologies work together to create a data-driven future that is innovative, efficient, and accessible. This chapter provides a thorough overview of their core ideas and applications, along with thoughts on how they could revolutionize today’s society.

Kapitel in diesem Buch

  1. Frontmatter I
  2. Contents V
  3. List of Contributing Authors VII
  4. 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
  5. 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
  6. 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
  7. 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
  8. 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
  9. 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
  10. 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
  11. 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
  12. 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
  13. 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
  14. 11 Ambulance booking and tracking website 183
  15. 12 Entropy based emergency rescue location selection with uncertain travel time 207
  16. 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
  17. 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
  18. 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
  19. Index
Heruntergeladen am 8.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/9783111504667-001/pdf
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