Healthcare Monitoring and Data Analysis Using IoT : Technologies and Applications

• Autore: Jain Vishal
• Pubblicazione: Stevenage : , : Institution of Engineering & Technology, , 2022; ©2022
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (427 pages)
• Disciplina: 616.028
• Soggetto topico: Patient monitoring - Technological innovations
• Altri autori: ChatterjeeJyotir Moy KumarPradeep KoseUtku
• Nota di contenuto: Intro -- Contents -- About the editors -- Preface -- Acknowledgments -- 1. COVID-19 pandemic analysis using application of AI | Pawan Whig, Rahul Reddy Nadikattu and Arun Velu -- 1.1 Introduction -- 1.2 Literature survey -- 1.3 Dataset used for analysis -- 1.4 Various machine learning libraries -- 1.5 Training and testing -- 1.6 Bias and variance -- 1.7 Result -- 1.8 Conclusion -- References -- 2. M-health: a revolution due to technology in healthcare sector | Mayuri Diwakar Kulkarni, Ashish Suresh Awate and Jyotir Moy Chatterje -- 2.1 Introduction -- 2.2 Discussion -- 2.3 Conclusion and future work -- References -- 3. Analysis of Big Data in electroencephalography (EEG) | Sagar Motdhare, Garima Mathur and Ravi Kant -- 3.1 Introduction -- 3.2 Methodology -- 3.3 EEG signal recording -- 3.4 Activity/action of EEG -- 3.5 EEG applications -- 3.6 Mathematical model -- 3.7 Across the boundaries of small sample sizes -- 3.8 EEG signal analytics and seizure analysis -- 3.9 EEG digital video -- 3.10 EEG data storage and its management -- 3.11 Big Data in epileptic EEG analysis -- 3.12 Conclusion -- 3.13 Future scope -- References -- 4. An analytical study of COVID-19 outbreak | Shipra Gupta, Vijay Kumar, P. Patil and Lajwanti Kishnani -- 4.1 Introduction -- 4.2 Review of literature -- 4.3 Method -- 4.4 Results -- 4.5 Discussions -- 4.6 Precautions -- 4.7 Conclusions and future scope -- Acknowledgment -- References -- 5. IoT-based smart healthcare monitoring system | Hakan Yuksel -- 5.1 Introduction -- 5.2 Related work -- 5.3 Proposed method -- 5.4 Result and discussion -- 5.5 Conclusion and future scope -- References -- 6. Development of a secured IoMT device with prioritized medical information for tracking and monitoring COVID patients in rural areas | P.K. Jawahar, K. Indragandhi, G. Kannan and Yiu-Wing Leung -- 6.1 Introduction. ; 6.2 Security threats in IoMT -- 6.3 Introduction to COVID-19 -- 6.4 Proposed system architecture -- 6.5 Conclusion and future scope -- References -- 7. An IoT-based system for a volumetric estimation of human brain morphological features from magnetic resonance images | S.N. Kumar, A. Lenin Fred, L.R. Jonisha Miriam, H. Ajay Kumar, I. Christina Jane, Parasuraman Padmanabhan and Balazs Gulyas -- 7.1 Introduction -- 7.2 Materials and methods -- 7.3 Results and discussion -- 7.4 Conclusion and future scope -- Acknowledgments -- References -- 8. Healthcare monitoring through IoT: security challenges and privacy issues | S.O. Owoeye, A.S. Akinade, K.I. Adenuga and F.O. Durodola -- 8.1 Introduction -- 8.2 IoT applications in personalized healthcare -- 8.3 Challenges of IoT in personalized healthcare -- 8.4 Security of IoT in personalized healthcare -- 8.5 Privacy -- 8.6 Conclusion and future scope -- References -- 9. E-health natural language processing | Saman Hina, Raheela Asif and Pardeep Kumar -- 9.1 Unstructured datasets for E-health NLP research -- 9.2 Annotation challenges dealing with health-care corpora -- 9.3 NLP methods that can be adopted to tackle semantics for medical text analysis -- 9.4 E-health and Internet of Things (IoT) -- 9.5 Contributions required from NLP researchers in health-care applications -- 9.6 Conclusion and future work -- References -- 10. Blockchain of things for healthcare asset management | Sajid Nazir, Mohammad Kaleem, Hassan Hamdoun, Jafar Alzubi and Hua Tianfield -- 10.1 Introduction -- 10.2 Healthcare asset management -- 10.3 Challenges and opportunities in healthcare -- 10.4 Blockchain: concepts and frameworks -- 10.5 Blockchain of things architecture for healthcare asset management -- 10.6 Major healthcare application areas -- 10.7 Conclusion and future work -- References. ; 11. Artificial intelligence: practical primer for clinical research in cardiovascular disease | Shivendra Dubey, Chetan Gupta and Kalpana Rai -- 11.1 Artificial intelligence -- 11.2 Traditional statistics versus AI -- 11.3 Representative algorithms of AI -- 11.4 Machine power along with big data -- 11.5 Challenges to implementation -- 11.6 Conclusion and future work -- References -- 12. Deep data analysis for COVID-19 outbreak | S.O. Owoeye, O.J. Odeyemi, F.O. Durodola and K.I. Adenuga -- 12.1 Introduction to deep data analysis -- 12.2 Deep data analysis for COVID-19 -- 12.3 CNN architectures -- 12.4 Building the neural network -- 12.5 Neural network architecture -- 12.6 Other parameters used to configure the neural network -- 12.7 Model summary -- 12.8 Metrics used for evaluation -- 12.9 Results and evaluation -- 12.10 Conclusion and future scope -- References -- 13. Healthcare system using deep learning | J.B. Shajilin Loret and P.C. Sherimon -- 13.1 Introduction -- 13.2 History of healthcare deep learning -- 13.3 Deep learning benefits -- 13.4 Components of deep learning -- 13.5 The role of deep learning in healthcare in the future -- 13.6 Deep learning applications in healthcare -- 13.7 Conclusion and future work -- References -- 14. Intelligent classification of ECG signals using machine learning techniques | Kuldeep Singh Kaswan, Anupam Baliyan, Jagjit Singh Dhatterwal, Vishal Jain and Jyotir Moy Chatterjee -- 14.1 Introduction -- 14.2 Heart-generated ECG signal -- 14.3 Filtering parameters least-mean-square algorithm -- 14.4 Retrieve and classify ECG signals utilizing ML-based techniques -- 14.5 Artificial neural network (ANN)-based ECG signals -- 14.6 Classification of ECG signals based fuzzy logic (FL) -- 14.7 Fourier transform wavelet transforms -- 14.8 Combination of machine learning and statistical algorithms. ; 14.9 Conclusion and future work -- References -- 15. A survey and taxonomy on mutual interference mitigation techniques in wireless body area networks | Neethu Suman and P.C. Neelakantan -- 15.1 Introduction -- 15.2 Interference issues in WBAN -- 15.3 Mutual interference mitigation schemes -- 15.4 Conclusion and future scope -- References -- 16. Predicting COVID cases using machine learning, android, and firebase cloud storage | Ritesh Kumar Sinha, Sukant Kishoro Bisoy, Saurabh Kumar, Sai Prasad Sarangi and Utku Kose -- 16.1 Introduction -- 16.2 Literature survey -- 16.3 Implementation and methodology -- 16.4 Machine learning models -- 16.5 Introduction to android app -- 16.6 Result analysis -- 16.7 Conclusion and future work -- References -- 17. Technological advancement with artificial intelligence in healthcare | Manas Kumar Yogi, Jyotsna Garikipati and Jyotir Moy Chatterjee -- 17.1 Introduction -- 17.2 Literature review -- 17.3 Disease identification and diagnosis -- 17.4 Drug discovery and manufacturing -- 17.5 Electronic health records -- 17.6 Disease prediction using machine learning -- 17.7 Fairness -- 17.8 Data analytics role in healthcare -- 17.9 Deep learning applications in healthcare -- 17.10 Conclusion and future scope -- References -- 18. Changing dynamics on the Internet of Medical Things: challenges and opportunities | Imtiaz Ali Brohi, Najma Imtiaz Ali and Pardeep Kumar -- 18.1 Introduction -- 18.2 The applications of Internet of Things -- 18.3 Healthcare and Internet of Things -- 18.4 Security in Internet of Medical Things -- 18.5 Privacy in Internet of Medical Things -- 18.6 Perception of trust and risk in IoMT -- 18.7 Conclusion and future scope -- References -- 19. Internet of Drones (IOD) in medical transport application | G. Prasad, J. Kavya and J. Sahana -- 19.1 Introduction to unmanned aerial vehicle. ; 19.2 Internet of Things in Industry 5.0 -- 19.3 Applications in medical transport -- 19.4 Methodology and approach -- 19.5 Conclusion and future -- Acknowledgment -- References -- 20. Blockchain-based Internet of Things (IoT) for healthcare systems: COVID-19 perspective | Anand Sharma, S.R. Biradar, H.K.D. Sarma and N.P. Rana -- 20.1 Introduction -- 20.2 IoT in healthcare system -- 20.3 COVID-19 outbreak -- 20.4 Blockchain -- 20.5 Blockchain-based IoT for healthcare systems -- 20.6 Advantages of proposed system -- 20.7 Conclusion and future scope -- References -- 21. Artificial intelligence-based diseases detection and diagnosis in healthcare | Said El Kafhali and Iman El Mir -- 21.1 Introduction -- 21.2 Overview of diseases detection and diagnosis techniques -- 21.3 Supervised learning models -- 21.4 Unsupervised learning models -- 21.5 Reinforcement learning models -- 21.6 Summary of some applications for disease diagnosis in healthcare -- 21.7 Some open research problems -- 21.8 Conclusions -- References -- Index.
• Sommario/riassunto: This edited book covers big data analysis methods of patient data gained via IoT-enabled monitoring systems. The information gathered can be processed to aid clinicians with diagnoses, prognoses and interventions. This book is a great reference to those using, designing, modelling and analysing intelligent healthcare services.
• Titolo autorizzato: Healthcare Monitoring and Data Analysis Using IoT
• ISBN: 1-83724-472-3; 1-5231-4666-4; 1-83953-438-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911006840803321
• Lo trovi qui: Univ. Federico II
• Serie: Healthcare Technologies