Advanced Healthcare Systems. Группа авторов

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СКАЧАТЬ that the presented method performs better than existing benchmark systems for considering parameters like disease prediction accuracy, sensitivity, specificity, F-measure, and computational time.

       • In Chapter 11, “Cloud-IoT-Driven Healthcare: Review, Architecture, Security Implications and Open Research Issues,” Junaid Latief Shah, Heena Farooq Bhat and Asif Iqbal Khan discuss security loopholes inherent in IoT architecture and the Cloud platform. The chapter also elaborates on various security countermeasures that have been proposed in the literature, highlighting their strengths and limitations. Also, a discussion on possible defense measures has been provided. The chapter culminates in underlining some burning research problems and security issues that need to be addressed for seamless healthcare services.

       • In Chapter 12, “A Novel Usage of Artificial Intelligence and Internet of Things in Remote-Based Healthcare Applications,” Dr. V. Arulkumar, D. Mansoor Hussain, S. Sridhar, and Dr. P. Vivekananda present the information necessary to reap the benefits of research capacity solutions through AI techniques. Healthcare services are among the applications enabled by the IoT. Advanced sensors may be used to monitor the health of permanent patients or may be inserted into the bodies of patients that can analyze, combine and prioritize the information gathered. Working with algorithms helps doctors change treatments and, at the same time, helps to economize on healthcare.

       • In Chapter 13, “Use of Machine Learning in Healthcare,” V. Lakshman Narayana, R. S. M. Lakshmi Patibandla, B. Tarakeswara Rao and Arepalli Peda Gopi focus on AI-assisted healthcare. Quotient Health has developed a program designed to reduce the cost of EMR structures by strengthening and standardizing the structuring of these frames. This chapter discusses healthcare AI, various implementations of AI, certifiable healthcare benefits, the morality of AI computations and opportunities to improve quality of healthcare skills.

       • In Chapter 14, “Methods of MRI Brain Tumor Segmentation,” Amit Verma discusses the requirements for and importance of using MRI imaging in brain tumor segmentation and the basic methods of doing it. Furthermore, a region-based generative model with weighted aggregation methods for performing brain tumor segmentation using MRI images is also discussed.

       • In Chapter 15, “Early Detection of Type 2 Diabetes Mellitus Using a Deep Neural Network-Based Model,” Varun Sapra and Luxmi Sapra focus on implementing a deep neural network for early identification of diabetes mellitus. For this purpose, benchmark dataset available on the UCI Machine Learning Repository and Kaggle are explored. This chapter suggests a deep neural network-based framework for early detection of disease that can be used as an adjunct tool in clinical practices.

       • In Chapter 16, “A Comparative Analysis of Implementation Framework for Masked Face Detection,” Pranjali Singh, Amitesh Garg and Amritpal Singh discuss quick and accurate approaches for the difficult task of face recognition resulting from certain facial features being hidden by the masks used during the current pandemic. This study uses deep learning-related techniques to resolve the issues of detecting facial features hidden by a mask. Another method of face mask detection is through TensorFlow, YOLOv5, SSDMNV2, SVMs, OpenCV, and Keras. The first step is to discard the masked face region. Next, a pre-trained deep convolutional neural network (CNN) is applied to extract the best features from the obtained regions. Labeled image data is used to train the CNN model. With 98.7% accuracy, a face mask is identified by the proposed system. By using the SVM classifier, the dataset of RMFD had a testing accuracy of 99.64%, SMFD achieved a 99.49% testing accuracy, and LFW achieved 100% testing accuracy. The SSDMNV2 approach used in the study in this chapter yields a 92.64% accuracy score and a 93% F1 score.

       • In Chapter 17, “IoT-Based Automated Healthcare System,” Dr. Darpan Anand and Mr. Ashish Kumar give an overview of the SDN and NFV types of sensors used in IoT devices. Apart from that, the views of various researchers are also given. The challenges of an SDN-based IoT device for healthcare architecture are also discussed.

The seventeen chapters of this book were written by eminent professors, researchers, and those in the industry from different countries. The chapters were initially peer reviewed by the editorial board members, reviewers, and those in the industry who also span many countries. All chapters have been designed to include basic introductory topics and advancements as well as future research directions, which will enable budding researchers and engineers to pursue their work in this area.

      The topic of intelligent IoT for advanced healthcare system(s) is so diversified that it cannot be covered in a single book. However, with the encouraging research contributed by the researchers in this book, we (contributors), editorial board members, and reviewers tried to sum up the latest research domains, developments in the data analytics field, and other applicable areas. First and foremost, we express our heartfelt appreciation to all the authors. We thank them all for considering and trusting this edited book as the platform for publishing their valuable work, and for for the kind co-operation extended by them during the various stages of processing this manuscript. We hope this book will serve as a motivating factor for those researchers who have spent years working as crime analysts, data analysts, statisticians, and budding researchers.

       Dr. Rohit Tanwar

      School of Computer Science,

      University of Petroleum and Energy Studies, Dehradun, India

       Dr. S. Balamurugan

      Director of Research and Development,

      Intelligent Research Consultancy Service (iRCS), Coimbatore, India

       Dr. Rakesh Kumai Saini

      School of Computing, DIT University, Dehradun, India

       Dr. Vishal Bharti

      School of Computing, DIT University, Dehradun, India

       Dr. Premkumar Chithaluru

      School of Computer Science,

      University of Petroleum and Energy Studies, Dehradun, India

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      Internet of Medical Things—State-of-the-Art

       Kishor Joshi1 and Ruchi Mehrotra2*

       1Mahavir Heart Hospital, Patna, India

       2University of Petroleum & Energy Studies, Dehradun, India

       Abstract

      Technological innovations have helped in early diagnosis and disease management, thereby preventing long-term complications of various diseases which contribute to morbidity and mortality. Internet of Things used in the healthcare industry and is the Internet of Medical Things (IoMT). The chapter compiles seminal research about IoMT. The paper elaborates the growth of the IoMT market in the last СКАЧАТЬ