Preface

From the last decade onward, a considerable amount of research and developments in technology have taken place especially in the healthcare industry, with the involvement of technologies like Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Blockchain, Communication Systems, Internet of Things, Multisensory Systems, etc. The purpose is only to make smart and secure healthcare possible. Machine Learning techniques and algorithms are required to give a boost to the aim of smart healthcare. Hence, to fulfill the vision of smart healthcare, the tools and applications based on Artificial Intelligence and Machine Learning are becoming extremely popular for producing more accuracy (in terms of values) in predicting results (without being explicitly programmed) in the healthcare industry. However, Artificial Intelligence-based algorithms are quite helpful for the transformation of physiological data into clinical information of real values, but for processing such big data from a set of medical images and identifying or extracting characteristic patterns of health function, then translating these patterns into clinical information definitely requires an adequate knowledge base of physiology, advanced digital signal processing capabilities, and machine learning. The domain of Artificial Intelligence can be taken into three main groups viz. Artificial Slight Intellect, Artificial Overall Intelligence, and Artificial Super Intelligence, and undoubtedly all these groups work in an absolute manner in the presence of fundamental or advanced Machine Learning techniques. There are a number of categories existing concerning the AI/ML algorithms used for fulfilling the objective of smart healthcare such as supervised (regression, decision-tree, classification) and unsupervised (clustering, association analysis, hidden Markov model, etc.). Although the preparation of intelligent algorithms or systems based on AI/ML combined with novel wearable portable devices (especially sensors etc.) offers unprecedented possibilities and opportunities for remote patient monitoring, traditional sharing schemes cannot guarantee the security and immutability of data. Machine Learning along with Artificial Intelligence is quite helpful towards the research and development in health care, but still lacking somewhat especially in security and privacy related to healthcare information of all categories i.e. the dream of smart healthcare is far behind. So to make the dream of smart healthcare come true, the emerging Blockchain technology is spreading its feet in the healthcare industry having revolutionized results. The Blockchain is helping in numerous perspectives in health care such that for managing the security, the integrity of data i.e. electronic health records (EHRs), electronic medical records (EMRs); preserving immutability of data, tracking the origin, spreading of data, the authenticity of data, data sharing, protection against data spoofing, etc., as compared to traditional security mechanism i.e. a single technology having a number of features. In simple words, smart healthcare will only be possible if both the accuracy in results, security and privacy of such results will be equally maintained. In a nutshell, the primary goal of this book is to offer a variety of techniques for a broad readership, ranging from computing and methodologies to business analytics in the health sciences.

In the chapter titled, “Blockchain Associated Machine Learning Approach for Earlier Prognosis and Preclusion of Osteoporosis in Elderly”, the authors discuss a fully automated mechanism for suspecting osteoporosis patients, which uses machine learning techniques to improve prognosis and preciseness through various processes. Here, we created an automated method that combines principal component analysis (PCA) and the weighted k-nearest neighbors algorithm (wkNN) to effectively detect, predict, and categorize BMD scores as normal, osteopenia, and osteoporosis.

In the chapter titled, “Online Detection of Malnutrition-Induced Anemia from Nail Color using Machine Learning Algorithms”, the author proposed a noninvasive online-based malnutrition-induced anaemia detection using a smartphone App for remotely measuring and monitoring anaemia and malnutrition in humans. This painless method enables user-friendly measurements of human bloodstream parameters such as haemoglobin (Hb), iron, folic acid, and vitamin B12 by embedding intelligent image processing algorithms that will process photos of fingernails captured by the camera in the smartphone, thereby providing a contact-free measurement system during this Covid 19 pandemic.

In the chapter titled, “Artificial Intelligence and Bioinformatics Promise Smart and Secure Healthcare: A Covid-19 Perspectives”, the authors elaborate on the principle, procedure and applications of AI equipped with bioinformatics knowledge to create opportunities, and prospects and answer the challenges met by academicians, researchers, students and industry professionals from the background of computer science, bioinformatics, and healthcare.

In the chapter titled, “Detection of Breast Cancer using Context-Aware Capsule Neural Network”, the authors' primary focus in this work is on the extraction of the features of the images and to accomplish this work, 3D mammogram images are pre-processed. Noise is removed and these preprocessed images are further passed through different convolution layers. After convolution process is done, images are fed to the capsule layers for final classification.

In the chapter titled, “Enhancement of Breast Cancer Screening through Texture and Deep Feature Fusion Model using MLO and CC View Mammograms”, the authors’ proposed model is more concentrated on the extraction and fusion of deep features from the two views to improve screening efficacy. The efficacy of the model is evaluated on mammogram images taken from MLO view and CC views of the DDSM data set. Medical imaging-based ML techniques are commonly used for breast cancer detection and diagnosis, but they are time-consuming.

In the chapter titled, “Artificial Intelligence Assisted Colonoscopy in Diagnosis of Colorectal Cancer”, the authors discuss how AI has gained attention for its potential to improve standard clinical practice. One such use is in diagnostic colonoscopy, where it can help identify precancerous lesions early and permit appropriate care.

In the chapter titled, “Developing a Smart Device for the Manufacturing of Health Products for Patients Using the Internet of Things”, healthcare analytics in a connected world were briefly discussed. In this study, the causes of the creation of contemporary healthcare are methodically examined, along with its causes, methods, and effects.

The authors of the chapter titled, “Blockchain Security in Healthcare” discuss the security and privacy needs, threats, and solution strategies in healthcare Blockchain for the exchange of electronic medical data, which further aids healthcare professionals, healthcare service developers, and healthcare consumers in gaining a thorough understanding of the security and privacy requirements and technologies for enabling a secure and decentralized EMR data sharing.

In this chapter titled, “Enhancing the Communication of Speech Impaired People using Embedded Vision Based Gesture Recognition through Deep Learning”, the author proposes to employ an image-based recognition system for American Sign Language (ASL) namely, (i). classification of handcrafted features using Machine Learning methods, (ii) classification utilising a pre-trained model via transfer learning, and (iii) classification of deep features derived from a specific layer by machine learning classifiers.

The chapter titled, “Advancing Data Science: A New Ray of Hope to Mental Health Care” examines the contributions of AI/ML and Blockchain to several mental healthcare system domains and discusses its potential in many additional unexplored frontiers in this discipline.

The chapter titled, “Machine Learning Based Techniques for Pneumonia Disease Identification in the Health Industry”, discusses the applications of one of the AI sub-disciplines, ML, and the difficulties and obstacles that researchers encounter when identifying early-stage pneumonia disease. In conclusion, Blockchain technology combined with ML and DL may be useful to create safe diagnostic systems as cloud systems have grown to be a possible hazard due to the accumulation of data stored there.

In the chapter titled, “Framework towards Smart Healthcare Tourism based on the Internet of Medical Things (IoMT)”, the authors provide the outlines of the Internet of Things-based health monitoring system that may be helpful for foreign visitors and hotel management throughout maintaining the health of both its guests and staff. The system will identify and examine the body’s many vital signs before telling the operator of the condition of each person’s health.

This chapter titled, “Unmasking the Sentiments of People Towards Pandemic: Twitter Sentiment Analysis in Real Time”, aims at examining and assessing people’s feelings and sentiments throughout the coronavirus outbreak. The study analysed people’s sentiments on the COVID-19 pandemic among Indians using sentimental analysis from tweets collected on Twitter.

In the chapter titled “Application of Industry 4.0: AI and IoT to Improve Supply Chain Performance”, the author briefs about how Artificial Intelligence and the Internet of Things play a vital role in enhancing supply chain management specifically in the healthcare industry. The businesses may streamline operations, cut expenses, and enhance decision-making by utilizing such emerging technologies.