A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Introduction

Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention.

Objective

This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS.

Method

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery.

Results

This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed.

Discussions

Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements.

Conclusion

The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.

A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data

The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a viable answer to the shortage of healthcare professionals. However, the medical field's ability to utilize this technology may be constrained by rules governing the sharing of data and privacy issues. Furthermore, endangering human life is what happens when a medical machine learning system is tricked or hacked. As a result, robust protections against cyberattacks are essential in the medical sector. This research uses two technologies, namely federated learning and blockchain, to solve these problems. The ultimate goal is to construct a trusted federated learning system on the blockchain that can predict people who are at risk for developing diabetes. The study's findings were deemed satisfactory as it achieved a multilayer perceptron accuracy of 97.11% and an average federated learning accuracy of 93.95%.

Older Adults' Perceptions About Using Intelligent Toilet Seats Beyond Traditional Care: Web-Based Interview Survey

BACKGROUND

In contemporary society, age tech (age technology) represents a significant advancement in health care aimed at enhancing patient engagement, ensuring sustained independence, and promoting quality of life for older people. One innovative form of age tech is the intelligent toilet seat, which is designed to collect, analyze, and provide insights based on toileting logs and excreta data. Understanding how older people perceive and interact with such technology can offer invaluable insights to researchers, technology developers, and vendors.

OBJECTIVE

This study examined older adults' perspectives regarding the use of intelligent toilet seats. Through a qualitative methodology, this research aims to unearth the nuances of older people's opinions, shedding light on their preferences, concerns, and potential barriers to adoption.

METHODS

Data were collected using a web-based interview survey distributed on Amazon Mechanical Turk. The analyzed data set comprised 174 US-based individuals aged ≥65 years who voluntarily participated in this study. The qualitative data were carefully analyzed using NVivo (Lumivero) based on detailed content analysis, ensuring that emerging themes were coded and classified based on the conceptual similarities in the respondents' narratives.

RESULTS

The analysis revealed 5 dominant themes encompassing the opinions of aging adults. The perceived benefits and advantages of using the intelligent toilet seat were grouped into 3 primary themes: health-related benefits including the potential for early disease detection, continuous health monitoring, and seamless connection to health care insights. Technology-related advantages include the noninvasive nature of smart toilet seats and leveraging unique and innovative data collection and analysis technology. Use-related benefits include ease of use, potential for multiple users, and cost reduction owing to the reduced need for frequent clinical visits. Conversely, the concerns and perceived risks were classified into 2 significant themes: psychological concerns, which included concerns about embarrassment and aging-related stereotypes, and the potential emotional impact of constant health monitoring. Technical performance risks include concerns centered on privacy and security, device reliability, data accuracy, potential malfunctions, and the implications of false positives or negatives.

CONCLUSIONS

The decision of older adults to incorporate intelligent toilet seats into their daily lives depends on myriad factors. Although the potential health and technological benefits are evident, valid concerns that need to be addressed remain. To foster widespread adoption, it is imperative to enhance the advantages while simultaneously addressing and mitigating the identified risks. This balanced approach will pave the way for a more holistic integration of smart health care devices into the routines of the older population, ensuring that they reap the full benefits of age tech advancements.

Alert-Grouping: Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle with Alarm Fatigue in Intensive Care

In Intensive Care Units (ICUs), patients are monitored using various devices that generate alerts when specific metrics, such as heart rate and oxygen saturation, exceed predetermined thresholds. However, these alerts can be inaccurate and lead to alert fatigue, resulting in errors and inaccurate diagnoses. We propose Alert grouping, a "Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle Alarm Fatigue in Intensive Care" model. The alert grouping looks at patients at the individual and cluster levels, and healthcare-related constraints to assist medical and nursing teams in setting personalized alert thresholds of vital parameters. By simulating the function of ICU patient bed devices, we demonstrate that the proposed alert grouping model effectively reduces the number of alarms overall, improving the alert system's validity and reducing alarm fatigue. Implementing this personalized alert model in ICUs boosts medical and nursing teams' confidence in the alert system, leading to better care for ICU patients by significantly reducing alarm fatigue, thereby improving the quality of care for ICU patients.

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

Digital health for aging populations

Growing life expectancy poses important societal challenges, placing an increasing burden on ever more strained health systems. Digital technologies offer tremendous potential for shifting from traditional medical routines to remote medicine and transforming our ability to manage health and independence in aging populations. In this Perspective, we summarize the current progress toward, and challenges and future opportunities of, harnessing digital technologies for effective geriatric care. Special attention is given to the role of wearables in assisting older adults to monitor their health and maintain independence at home. Challenges to the widespread future use of digital technologies in this population will be discussed, along with a vision for how such technologies will shape the future of healthy aging.

NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory

The continued spread of COVID-19 seriously endangers the physical and mental health of people in all countries. It is an important method to establish inter agency COVID-19 detection and prevention system based on game theory through wireless communication and artificial intelligence. Federated learning (FL) as a privacy preserving machine learning framework has received extensive attention. From the perspective of game theory, FL can be regarded as a process in which multiple participants play games against each other to maximize their own interests. This requires that the user's data is not leaked during the training process. However, existing studies have proved that the privacy protection capability of FL is insufficient. In addition, the existing way of realizing privacy protection through multiple rounds of communication between participants increases the burden of wireless communication. To this end, this paper considers the security model of FL based on game theory, and proposes our scheme, NVAS, a non-interactive verifiable privacy-preserving FL aggregation scheme in wireless communication environments. The NVAS can protect user privacy during FL training without unnecessary interaction between participants, which can better motivate more participants to join and provide high-quality training data. Furthermore, we designed a concise and efficient verification algorithm to ensure the correctness of model aggregation. Finally, the security and feasibility of the scheme are analyzed.

A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis

In today's digital world, information is growing along with the expansion of Internet usage worldwide. As a consequence, bulk of data is generated constantly which is known to be "Big Data". One of the most evolving technologies in twenty-first century is Big Data analytics, it is promising field for extracting knowledge from very large datasets and enhancing benefits while lowering costs. Due to the enormous success of big data analytics, the healthcare sector is increasingly shifting toward adopting these approaches to diagnose diseases. Due to the recent boom in medical big data and the development of computational methods, researchers and practitioners have gained the ability to mine and visualize medical big data on a larger scale. Thus, with the aid of integration of big data analytics in healthcare sectors, precise medical data analysis is now feasible with early sickness detection, health status monitoring, patient treatment, and community services is now achievable. With all these improvements, a deadly disease COVID is considered in this comprehensive review with the intention of offering remedies utilizing big data analytics. The use of big data applications is vital to managing pandemic conditions, such as predicting outbreaks of COVID-19 and identifying cases and patterns of spread of COVID-19. Research is still being done on leveraging big data analytics to forecast COVID-19. But precise and early identification of COVID disease is still lacking due to the volume of medical records like dissimilar medical imaging modalities. Meanwhile, Digital imaging has now become essential to COVID diagnosis, but the main challenge is the storage of massive volumes of data. Taking these limitations into account, a comprehensive analysis is presented in the systematic literature review (SLR) to provide a deeper understanding of big data in the field of COVID-19.

A Novel Optimized Perturbation-Based Machine Learning for Preserving Privacy in Medical Data

In recent times, providing privacy to the medical dataset has been the biggest issue in medical applications. Since, in hospitals, the patient's data are stored in files, the files must be secured properly. Thus, different machine learning models were developed to overcome data privacy issues. But, those models faced some problems in providing privacy to medical data. Therefore, a novel model named Honey pot-based Modular Neural System (HbMNS) was designed in this paper. Here, the performance of the proposed design is validated with disease classification. Also, the perturbation function and the verification module are incorporated into the designed HbMNS model to provide data privacy. The presented model is implemented in a python environment. Moreover, the system outcomes are estimated before and after fixing the perturbation function. A DoS attack is launched in the system to validate the method. At last, a comparative assessment is made between executed models with other models. From the comparison, it is verified that the presented model achieved better outcomes than others.

A Machine Learning Approach for Monitoring and Classifying Healthcare Data-A Case of Emergency Department of KSA Hospitals

The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special occasions events such as Hajj or Umrah, when pilgrims travel from one region to another with severe disease conditions. Apart from the EDs, it is critical to monitor the movements of patients from EDs to other wards inside the hospital or in the region. This is to track the spread of viral illnesses that require more attention. In this scenario, Machine Learning (ML) algorithms can be used to classify the data into many classes and track the target audience. The current research article presents a Machine Learning-based Medical Data Monitoring and Classification Model for the EDs of the KSA hospitals and is named MLMDMC-ED technique. The most important aim of the proposed MLMDMC-ED technique is to monitor and track the patient's visits to the EDs, the treatment given to them based on the Canadian Emergency Department Triage and Acuity Scale (CTAS), and their Length Of Stay (LOS) in the hospital, based on their treatment requirements. A patient's clinical history is crucial in terms of making decisions during health emergencies or pandemics. So, the data should be processed so that it can be classified and visualized in different formats using the ML technique. The current research work aims at extracting the textual features from the patients' data using the metaheuristic Non-Defeatable Genetic Algorithm II (NSGA II). The data, collected from the hospitals, are classified using the Graph Convolutional Network (GCN) model. Grey Wolf Optimizer (GWO) is exploited for fine-tuning the parameters to optimize the performance of the GCN model. The proposed MLMDMC-ED technique was experimentally validated on the healthcare data and the outcomes indicated the improvements of the MLMDMC-ED technique over other models with a maximum accuracy of 91.87%.

An Optimized IoT-enabled Big Data Analytics Architecture for Edge-Cloud Computing

The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: IoT-edge and Cloud-processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. Optimized Yet Another Resource Negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic dataset using Apache Spark. The comparative analysis is decorated with existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.

Correlation evaluation between cancer microenvironment related genes and prognosis based on intelligent medical internet of things

The study of tumor microenvironment plays an important role in the treatment of cancer patients. In this paper, intelligent medical Internet of Things technology was used to analyze cancer tumor microenvironment-related genes. Through experiments designed and analyzed cancer-related genes, this study concluded that in cervical cancer, patients with high expression of P16 gene had a shorter life cycle and a survival rate of 35%. In addition, through investigation and interview, it was found that patients with positive expression of P16 and Twist genes had a higher recurrence rate than patients with negative expression of both genes; high expression of FDFT1, AKR1C1, and ALOX12 in colon cancer is associated with short survival; high expressions of HMGCR and CARS1 is associated with longer survival; overexpression of NDUFA12, FD6, VEZT, GDF3, PDE5A, GALNTL6, OPMR1, and AOAH in thyroid cancer is associated with shortened survival; high expressions of NR2C1, FN1, IPCEF1, and ELMO1 is associated with prolonged survival. Among the genes associated with the prognosis of liver cancer, the genes associated with shorter survival period are AGO2, DCPS, IFIT5, LARP1, NCBP2, NUDT10, and NUDT16; the genes associated with longevity are EIF4E3, EIF4G3, METTL1, NCBP1, NSUN2, NUDT11, NUDT4, and WDR4. Depending on the prognostic role of genes in different cancers, they can influence patients to achieve the effect of reducing patients' symptoms. In the process of disease analysis of cancer patients, this paper uses bioinformation technology and Internet of things technology to promote the development of medical intelligence.

Assessing the Contribution of Managerial Accounting in Sustainable Organizational Development in the Healthcare Industry

Sustainability and digital transformation are two phenomena influencing the activities of all organizations. Managerial accounting is an essential component of these transformations, having complex roles in decision-making to ensure sustainable development through implementing modern technologies in the accounting process. This paper studies the roles of digitized managerial accounting in organizational sustainability drivers from a decision-making perspective. The empirical investigation assesses the influence of managerial accounting on the economic, social, and environmental drivers of sustainability from the perception of 396 Romanian accountants using an artificial neural network analysis and structural equation modeling. As a result, the research provides a holistic view of the managerial accounting roles enhanced by digital technologies in the sustainable development of healthcare organizations. From the accountants' perception, the leading managerial accounting roles on organizational sustainability are enablers and reporters of the sustainable value created in the organization. Additionally, the roles of creators and preservers are seen as relevant by a significant part of the respondents. Therefore, healthcare organizations must implement a sustainability vision in managerial accounting and accounting information systems using the capabilities offered by new digital technologies.

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Wearable sensor devices and the Internet of Medical Things (IoMT) have changed the health service, resulting in faster monitoring, prediction, diagnosis, and treatment. Nevertheless, there have been difficulties that need to be resolved by the use of AI methods. The primary goal of this study is to introduce an AI-powered, IoMT telemedicine infrastructure for E-healthcare. In this paper, initially the data collection from the patient body is made using the sensed devices and the information are transmitted through the gateway/Wi-Fi and is stored in IoMT cloud repository. The stored information is then acquired, preprocessed to refine the collected data. The features from preprocessed data are extracted by means of high dimensional Linear Discriminant analysis (LDA) and the best optimal features are selected using reconfigured multi-objective cuckoo search algorithm (CSA). The prediction of abnormal/normal data is made by using Hybrid ResNet 18 and GoogleNet classifier (HRGC). The decision is then made whether to send alert to hospitals/healthcare personnel or not. If the expected results are satisfactory, the participant information is saved in the internet for later use. At last, the performance analysis is carried so as to validate the efficiency of proposed mechanism.

Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up

It is increasingly possible to acquire Electrocardiographic data with featured low-cost devices. The proposed dataset will help map different signals for various diseases related to Electrocardiography data. The dataset presented in this paper is related to the acquisition of electrocardiography data during the standing up and seated positions. The data was collected from 219 individuals (112 men, 106 women, and one other) in different environments, but they are in the Covilhã municipality. The dataset includes the 219 recordings and corresponds to the sensors' recordings of a 30 s sitting and a 30 s standing test, which checks to approximately 1 min for each one. This dataset includes 3.7 h (approximately) of recordings for further analysis with data processing techniques and machine learning methods. It will be helpful for the complementary creation of a robust method for identifying the characteristics of individuals related to Electrocardiography signals.

A comprehensive and systematic literature review on the big data management techniques in the internet of things

The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.

Survival study on deep learning techniques for IoT enabled smart healthcare system

Purpose

The paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare.

Methods

A deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced.

Results

Experimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%.

Conclusion

MAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.

Assessing Digital Transformation of Cost Accounting Tools in Healthcare

The expansion of digital technologies has significantly changed most economic activities and professions. Digital technologies penetrated managerial accounting and have a vast potential to transform this profession. Implementing emerging digital technologies, such as artificial intelligence, blockchain, the Internet of Things, big data, and cloud computing, can trigger a crucial leap forward, leading to a paradigm-shifting in healthcare organizations' accounting management. The paper's main objective is to investigate the perception of Romanian accountants on implementing digital technologies in healthcare organizations' accounting management. The paper implies a study based on a questionnaire among Romanian accountants who use various digital technologies implemented in traditional and innovative cost accounting tools. Based on structural equation modeling, the results emphasize the prevalence of innovative tools over traditional cost accounting tools improved through digital transformation, digital technologies assuming the most complex and time-consuming tasks. Moreover, the influence of cost accounting tools improved through digital transformation on healthcare organizations' performance is much more robust in the case of innovative tools than in the case of traditional cost accounting tools. The proposed model provides managers in healthcare organizations with information on the most effective methods in the context of digital transformation.

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

Model-Based Reinforcement Learning with Automated Planning for Network Management

Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores the potential of using Automated Planning (AP) to achieve this MBRL in the functional areas of network management. In addition, a comparison of several integration strategies of AP and RL is depicted. We also describe an architecture that realizes a cognitive management control loop by combining AP and RL. Our experiments evaluate on a simulated environment evidence that the combination proposed improves model-free RL but demonstrates lower performance than Deep RL regarding the reward and convergence time metrics. Nonetheless, AP-based MBRL is useful when the prediction model needs to be understood and when the high computational complexity of Deep RL can not be used.

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery

In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.

IoT-Based Smart Health Monitoring System for COVID-19

With the commencement of the COVID-19 pandemic, social distancing and quarantine are becoming essential practices in the world. IoT health monitoring systems prevent frequent visits to doctors and meetings between patients and medical professionals. However, many individuals require regular health monitoring and observation through medical staff. In this proposed work, we have taken advantage of the technology to make patients life easier for earlier diagnosis and treatment. A smart health monitoring system is being developed using Internet of Things (IoT) technology which is capable of monitoring blood pressure, heart rate, oxygen level, and temperature of a person. This system is helpful for rural areas or villages where nearby clinics can be in touch with city hospitals about their patient health conditions. However, if any changes occur in a patient's health based on standard values, then the IoT system will alert the physician or doctor accordingly. The maximum relative error (%ϵ _r) in the measurement of heart rate, patient body temperature and SPO₂ was found to be 2.89%, 3.03%, 1.05%, respectively, which was comparable to the commercials health monitoring system. This health monitoring system based on IoT helps out doctors to collect real-time data effortlessly. The availability of high-speed internet allows the system to monitor the parameters at regular intervals. Furthermore, the cloud platform allows data storage so that previous measurements could be retrieved in the near future. This system would help in identifying and early treatment of COVID-19 individual patients.

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues

The past decade has been characterized by the growing volumes of data due to the widespread use of the Internet of Things (IoT) applications, which introduced many challenges for efficient data storage and management. Thus, the efficient indexing and searching of large data collections is a very topical and urgent issue. Such solutions can provide users with valuable information about IoT data. However, efficient retrieval and management of such information in terms of index size and search time require optimization of indexing schemes which is rather difficult to implement. The purpose of this paper is to examine and review existing indexing techniques for large-scale data. A taxonomy of indexing techniques is proposed to enable researchers to understand and select the techniques that will serve as a basis for designing a new indexing scheme. The real-world applications of the existing indexing techniques in different areas, such as health, business, scientific experiments, and social networks, are presented. Open problems and research challenges, e.g., privacy and large-scale data mining, are also discussed.

Edge Computing Using Embedded Webserver with Mobile Device for Diagnosis and Prediction of Metastasis in Histopathological Images

Diagnosis of different breast cancer stages using histopathology whole slide images is the gold standard in grading the tissue metastasis. Traditional diagnosis involves labor intensive procedures and is prone to human errors. Computer aided diagnosis assists medical experts as a second opinion tool in early detection which prevents further proliferation. Computing facilities have emerged to an extent where algorithms can attain near human accuracy in prediction of diseases, offering better treatment to curb further proliferation. The work introduced in the paper provides an interface in mobile platform, which enables the user to input histopathology image and obtain the prediction results with its class probability through embedded web-server. The trained deep convolutional neural networks model is deployed into a microcomputer-based embedded system after hyper-parameter tuning, offering congruent performance. The implementation results show that the embedded platform with custom-trained CNN model is suitable for medical image classification, as it takes less execution time and mean prediction time. It is also noticed that customized CNN classifier model outperforms pre-trained models when used in embedded platforms for prediction and classification of histopathology images. This work also emphasizes the relevance of portable and flexible embedded device in real time clinical applications.

Determinants of the Intention to Use Smart Healthcare Devices: A Framework and Public Policy Implications

Healthcare industry is strongly influenced by new digital technologies. In this context, this study creates a framework and explores determinants of the intention to use smart healthcare devices. Several factors were identified, including usefulness, convenience, novelty, price, technological complexity, and perceived privacy risks of smart devices. Based on the samples from China, we find that usefulness, convenience, and novelty have positive influences on the intention to use smart healthcare devices. However, technological complexity is negatively related to the intention to use smart devices. The results further extend previous researches in the area of the healthcare industry.

Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles

The concept of predictive and preventive maintenance and constant monitoring of the technical condition of industrial machinery is currently being greatly improved by the development of artificial intelligence and deep learning algorithms in particular. The advancement of such methods can vastly improve the overall effectiveness and efficiency of systems designed for wear analysis and detection of vibrations that can indicate changes in the physical structure of the industrial components such as bearings, motor shafts, and housing, as well as other parts involved in rotary movement. Recently this concept was also adapted to the field of renewable energy and the automotive industry. The core of the presented prototype is an innovative interface interconnected with augmented reality (AR). The proposed integration of AR goggles allowed for constructing a platform that could acquire data used in rotary components technical evaluation and that could enable direct interaction with the user. The presented platform allows for the utilization of artificial intelligence to analyze vibrations generated by the rotary drive system to determine the technical condition of a wind turbine model monitored by an image processing system that measures frequencies generated by the machine.

A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation

Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review

Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of "smart healthcare." This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.

Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map

Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques.

Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things

The Internet of Medical Things (IoMT) has shown incredible development with the growth of medical systems using wireless information technologies. Medical devices are biosensors that can integrate with physical things to make smarter healthcare applications that are collaborated on the Internet. In recent decades, many applications have been designed to monitor the physical health of patients and support expert teams for appropriate treatment. The medical devices are attached to patients’ bodies and connected with a cloud computing system for obtaining and analyzing healthcare data. However, such medical devices operate on battery powered sensors with limiting constraints in terms of memory, transmission, and processing resources. Many healthcare solutions are helping the community with the efficient monitoring of patients’ conditions using cloud computing, however, mostly incur latency in data collection and storage. Therefore, this paper presents a model for the Secured Big Data analytics using Edge–Cloud architecture (SBD-EC), which aims to provide distributed and timely computation of a decision-oriented medical system. Moreover, the mobile edges cooperate with the cloud level to present a secure algorithm, achieving reliable availability of medical data with privacy and security against malicious actions. The performance of the proposed model is evaluated in simulations and the results obtained demonstrate significant improvement over other solutions.