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The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios. [ABSTRACT FROM AUTHOR]

One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the PIMA Indian Diabetes dataset. Patients with diabetes were classified on the basis of laboratory findings. Pre-processing tasks, such as the handling of imbalanced data and missing values, were performed prior to feature importance and normalisation activities. The algorithm with the best performance was examined using precision, accuracy, F1, recall and area under the curve metrics. Then, all ML models were hyper parametrically tuned via grid search to optimise their performance and reduce their error times. The decision process was also evaluated to further enhance the models. The AdaBoost-ET model performed even when features were not selected for binary classification. The model proposed in this study can predict diabetes with unprecedented high accuracy compared with the models in previous studies. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is revolutionizing healthcare by providing remarkable possibilities for remote patient monitoring, instantaneous data analysis, and customized healthcare delivery. However, the widespread use of interconnected medical devices has exposed vulnerabilities to cyber threats, posing significant challenges to the security, privacy, and accessibility of healthcare data and services. The CICIoMT2024 dataset is a crucial resource in IoMT security, offering a wide range of cyber-attacks targeting IoMT devices. This paper uses data balancing techniques like SMOTE and advanced machine learning (ML) models to analyze cyber threats on IoMT devices, aiming to improve healthcare system safety by identifying and mitigating cyberattacks. By conducting extensive experiments, the paper has determined the most effective ML models for three different levels of classification of the dataset: binary, multiclass, and multitype. Employing ML techniques like AdaBoost, Random Forest, kNN, and XGBoost proves to be extremely powerful in accurately categorizing various types of attacks. This study emphasizes the importance of proactive cybersecurity measures in IoMT ecosystems, as well as the effectiveness of ML techniques in protecting healthcare systems from evolving cyber threats.

Abstract The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management.

Internet of Medical Things (IoMT) can be leveraged for periodic sensing and recording of different health parameters using sensors, wireless communications, and computation platforms. Health care systems can be enhanced by using IoMT for remote patient monitoring and data-driven diagnosis powered by machine learning algorithms. In the context of IoMT, federated learning (FL) is an excellent choice to manage machine learning (ML) algorithms to drive this analysis. This is because FL models can be trained in a distributed manner on local heterogeneous datasets that all contribute to the "collective wisdom". The model parameters can be regulated and shared without sharing the actual health data, ensuring confidentiality and security. This paper makes a case for the viability of FL-based analysis of data acquired via IoMT by presenting some use cases and recent work in this area and proposing a novel framework for data analysis using FL specifically in the context of mental stress detection. It shows that FL-based methods can significantly reduce the required communication overhead for each local device from 10.02MB/day up to only 754B/day as compared to non-FL techniques. [ABSTRACT FROM AUTHOR]

The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management. [ABSTRACT FROM AUTHOR]

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem. [ABSTRACT FROM AUTHOR]

Mental health diagnosis often relies on subjective evaluations, which can be intrusive and lack objectivity. With the current global situation brought about by the COVID-19 pandemic, the need for real-time, on-demand healthcare services has become more apparent than ever. Fortunately, wearable Internet of Medical Things (IoMT) devices, such as wrist actigraphs and smartphones, offer a promising solution by generating objective data that can aid in early-stage mental health diagnosis. This paper presents a novel Deep Convolutional Neural Architecture (CNN) with a split attention mechanism, that outperforms the traditional methods of analyzing motor activity data in diagnosing schizophrenia. To address the unique characteristics of motor activity, the proposed method includes a novel imputation method, a sampling technique based on a sliding window for sample expansion, and the Synthetic Minority Over-sampling Technique (SMOTE) technique for class balancing. The results demonstrate the highest accuracy of 94% using 24-h actigraphy data. Overall, the application of this methodology can greatly contribute to the development of pervasive healthcare systems, providing non-invasive, objective, and real-time mental healthcare services. [ABSTRACT FROM AUTHOR]

Anomaly detection is a critical aspect of various applications, including security, healthcare, and network monitoring. In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. FusionNet’s architecture leverages the diversity of these algorithms to achieve high accuracy and precision. We evaluate FusionNet’s performance on two distinct datasets, Dataset 1 and Dataset 2, and compare it with traditional machine learning models, including SVM, KNN, and RF. The results demonstrate that FusionNet consistently outperforms these models across both datasets in terms of accuracy, precision, recall, and F1 score. On Dataset 1, FusionNet achieves an accuracy of 98.5% and on Dataset 2, it attains an accuracy of 99.5%. FusionNet’s remarkable ability to detect anomalies with exceptional accuracy underscores its potential for real-world applications. This study highlights the significance of FusionNet as a robust model for anomaly detection and provides insights into its superior performance over traditional models. The results emphasize the promising prospects of FusionNet in security, healthcare, and other domains where accurate anomaly detection is crucial.

The recent global pandemic has resulted in growth in themedical and healthcare sectors. Applications used in these domains have become more advanced and digitally integrated. Sensor-based Internet of Things (IoT) devices are increasing in healthcare andmedical units. The emerging trend with the use of IoT devices in medical healthcare is termed as Internet of Medical Things (IoMT). The instruments used in these healthcare units comprise various sensors that can record patient body observations. These recorded observations are streamed across Internetbased channels to be stored and analyzed in centralized servers. Patient diagnostics are performed based on the information retrieved from these devices. Machine learning and artificial intelligence play a significant role in diagnostic mechanisms and identifying diseases across observation sets. The data collected by these observation sets are analyzed closely with the help of Artificial Neural Networks (ANN). This distributed system of devices and servers raises privacy concerns. Blockchain is a technology that can preserve the privacy of the information collected from devices. A blockchain is a mechanism in which information goes across various network nodes. A centralized architecture where information flows from the sensor devices to a centralized server risks large-scale information leak. This paper proposes a prototype and a model for collecting information and protecting the data from intrusion. Any application that resides in the cloud and collects data from a sensor based IoT device can be prone to intrusion. The detection occurs at an early stage and in a fraction of the time. The model uses artificial neural networks and gets evaluated across various datasets. Improved accuracy and performance with data analyzed at the IoT-based devices prove the efficiency of the model proposed in this study. The stability and accuracy of the results after testing these applications make the model sustainable and acceptable across healthcare systems. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a growing trend within the rapidly expanding Internet of Things, enhancing healthcare operations and remote patient monitoring. However, these devices are vulnerable to cyber-attacks, posing risks to healthcare operations and patient safety. To detect and counteract attacks on the IoMT, methods such as intrusion detection systems, log monitoring, and threat intelligence are utilized. However, as attackers refine their methods, there is an increasing shift toward using machine learning and deep learning for more accurate and predictive attack detection. In this paper, we propose a fuzzy-based self-tuning Long Short-Term Memory (LSTM) intrusion detection system (IDS) for the IoMT. Our approach dynamically adjusts the number of epochs and utilizes early stopping to prevent overfitting and underfitting. We conducted extensive experiments to evaluate the performance of our proposed model, comparing it with existing IDS models for the IoMT. The results show that our model achieves high accuracy, low false positive rates, and high detection rates, indicating its effectiveness in identifying intrusions. We also discuss the challenges of using static epochs and batch sizes in deep learning models and highlight the importance of dynamic adjustment. The findings of this study contribute to the development of more efficient and accurate IDS models for IoMT scenarios. [ABSTRACT FROM AUTHOR]

The growth of the Internet of Medical Things (IoMT) and healthcare data analytics allows wearable Wireless Body Area Networks (WBANs) and ambient sensors to collect the large quantities of physiological signals necessary for better patient diagnostics and treatments. Artificial intelligence and machine learning algorithms frequently require precisely synchronized signals from multiple sensors, which in turn require time-consuming and energy-inefficient synchronization methods with constant wireless network connectivity. We propose ecoSync, a highly energy-efficient time synchronization algorithm for Wi-Fi devices in IoMT applications. We demonstrated that ecoSync can correct the time difference error to ±42 µs with an hour between resynchronizations, using only 658 millijoules of energy. This is an 87% improvement in time difference error and a 99.93% reduction in energy usage over using TSF for synchronization alone over a 1 h period. Wireless synchronization of sensors allows placement of physiological sensors on objects of everyday use (Smart Stuff), which in turn allows seamless collection of physiological status data every time we interact with smart objects in an IoMT environment. [ABSTRACT FROM AUTHOR]

Internet has changed and evolved since its origin and in recent time the communication channel for the internet has developed a flexible system design to accommodate newer devices and customized services. Internet of Medical Things (IoMT) is one such blooming service for larger data processing and customization. In this paper, we propose a novel interdependency attribute protocol for authentication and enhancing security aspects of IoMT communication. The protocol is developed on remote monitoring and Message Queuing Telemetric Transport (MQTT) protocol foundation values to secure data transmission and communication via active public domain internet and cloud storage. The approach is developed on machine learning models for attribute customization and classification. The model has been validated with AWS open pipeline cloud platforms for IoMT devices customization. The IoMT device cloud server evaluation and authentication via interdependency attribute protocol has outperformed the existing security models and analysis with Machine learning tools. [ABSTRACT FROM AUTHOR]

Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients’ health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees’ stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated sooner. However, overwhelming the fog layer with extensive data will increase the load on fog nodes, leading to computational challenges. This study aims to reduce fog computation by proposing machine learning (ML) models with two phases. The first phase of the ML model assesses the priority of the situation based on the stress level. In the second phase, a classifier determines the cause of stress, which was either interruptions or time pressure while completing a task. This approach reduced the computation cost for the fog node, as only high-priority records were transferred to the fog. Low-priority records were forwarded to the cloud. Four ML approaches were compared in terms of accuracy and prediction speed: Knearest neighbors (KNN), a support vector machine (SVM), a bagged tree (BT), and an artificial neural network (ANN). In our experiments, ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN, SVM, and BT. [ABSTRACT FROM AUTHOR]

According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction. [ABSTRACT FROM AUTHOR]

In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features. [ABSTRACT FROM AUTHOR]

The growth of the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to various cybersecurity attacks and threats, which lead to negative consequences. Cyberattacks can damage not just the IoMT devices in use but also human life. Currently, several security solutions have been proposed to enhance the security of the IoMT, employing machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. Furthermore, blockchain technology enables a decentralized approach to the healthcare system, eliminating some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects malicious data measured by medical sensors to find fraudulent data. As a result, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. Additionally, a blockchain network is used in the fog layer to ensure that the data is not altered, enhancing the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% based on the F1-score accuracy metric. [ABSTRACT FROM AUTHOR]

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions. [ABSTRACT FROM AUTHOR]

The physical and mental health of people can be enhanced through yoga, an excellent form of exercise. As part of the breathing procedure, yoga involves stretching the body organs. The guidance and monitoring of yoga are crucial to ripe the full benefits of it, as wrong postures possess multiple antagonistic effects, including physical hazards and stroke. The detection and monitoring of the yoga postures are possible with the Intelligent Internet of Things (IIoT), which is the integration of intelligent approaches (machine learning) and the Internet of Things (IoT). Considering the increment in yoga practitioners in recent years, the integration of IIoT and yoga has led to the successful implementation of IIoT-based yoga training systems. This paper provides a comprehensive survey on integrating yoga with IIoT. The paper also discusses the multiple types of yoga and the procedure for the detection of yoga using IIoT. Additionally, this paper highlights various applications of yoga, safety measures, various challenges, and future directions. This survey provides the latest developments and findings on yoga and its integration with IIoT. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a growing trend that has led to the use of connected devices, known as the Internet of Health. The healthcare domain has been a target of cyberattacks, especially with a large number of IoMT devices connected to hospital networks. This factor could allow attackers to access patients' personal health information (PHI). This research paper proposes Chidroid, an innovative mobile Android application that can retrieve, collect, and distribute logs from smart healthcare devices. The proposed approach enables the creation of datasets, allowing non-structured data to be parsed into semi-structured or structured data that can be used for machine learning and deep learning, and the proposed approach can serve as a universal policy-based tool to examine and analyse security issues in most recent Android versions by distributing logs for analysis. The validation tests demonstrated that the application could retrieve logs and system metrics from various assets and devices in an efficient manner. The collected logs can provide visibility into the device's activities and help to detect and mitigate potential security risks. This research introduces a way to perform a security analysis on Android devices that uses minimal system resources and reduces battery consumption by pushing the analysis stage to the edge. [ABSTRACT FROM AUTHOR]

Sleep apnea syndrome (SAS) is a breathing disorder while a person is asleep. The traditional method for examining SAS is Polysomnography (PSG). The standard procedure of PSG requires complete overnight observation in a laboratory. PSG typically provides accurate results, but it is expensive and time consuming. However, for people with Sleep apnea (SA), available beds and laboratories are limited. Resultantly, it may produce inaccurate diagnosis. Thus, this paper proposes the Internet of Medical Things (IoMT) framework with a machine learning concept of fully connected neural network (FCNN) with k-nearest neighbor (k-NN) classifier. This paper describes smart monitoring of a patient's sleeping habit and diagnosis of SA using FCNN-KNN+ average square error (ASE). For diagnosing SA, the Oxygen saturation (SpO2) sensor device is popularly used for monitoring the heart rate and blood oxygen level. This diagnosis information is securely stored in the IoMT fog computing network. Doctors can carefully monitor the SA patient remotely on the basis of sensor values, which are efficiently stored in the fog computing network. The proposed technique takes less than 0.2 s with an accuracy of 95%, which is higher than existing models. [ABSTRACT FROM AUTHOR]

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women's ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system (QBWO-MLDSS) for smart healthcare. The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately. Besides, the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data. In addition, the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets. Moreover, symbiotic organisms search (SOS) with extreme learning machine (ELM) model is applied as a classifier for the detection and classification of ELM model, thereby improving the overall classification performance. The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study's novelty. The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset, and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches. [ABSTRACT FROM AUTHOR]

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare industry by leveraging its capabilities to improve personalized healthcare services by enabling seamless communication of medical data. IoMT facilitates prompt emergency responses and provides improved quality of medical services with minimum cost. With the advancement of modern technology, progressively ubiquitous medical devices raise critical security and data privacy concerns through resource constraints and open connectivity. Vulnerabilities in IoMT devices allow unauthorized access for potential entry into healthcare and sensitive personal data. In addition, the patient may experience severe physical damage with the attack on IoMT devices. To provide security to IoMT devices and privacy to patient data, we have proposed a novel IoMT framework with the hybridization of Bayesian optimization and extreme learning machine (ELM). The proposed model derives encouraging performance with enhanced accuracy in decision-making process compared to similar state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this article, the Internet of Medical Things (IoMT) framework based on Apache Spark big data processing technology is proposed for real‐time analysis of health data obtained from wireless body area networks (WBANs), which is one of the most important components of IoMT. The proposed framework consists of four layers: data source, data collection, data analytics and visualization. In addition, the proposed IoMT framework is presented with two different disease prediction scenarios, diabetes and heart disease. Diabetes and heart disease prediction processes are carried out using the random forest (RF), logistic regression (LR) and support vector machine (SVM) algorithms belonging to the Apache Spark machine learning library (MLlib). The analysis of health data generated in WBANs takes place in real‐time in the Apache Spark‐based data analytics layer. In this study, the performances of MLlib algorithms in the real‐time model developed for heart and diabetes disease are examined. The SVM algorithm with an accuracy rate of 93.33% for heart disease and the LR algorithm with an accuracy rate of 78.89% for diabetes are found to provide the best performances. [ABSTRACT FROM AUTHOR]

In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features.

Epidemics are widespread perhaps the persistent medical problem continues causing financial, physiological, and psychological havoc around the planet. All areas are affected, from universities to the medical system. Innovation, on either hand, has greatly simplified our life and current condition. Even though the pandemic outbreak had a negative impact, it also resulted in discoveries and concepts that helped many people get through the difficult times and finally helped us adapt to the changing norms. IoMT and machine intelligence are two domains that have enabled the application of smart and telehealthcare, as well as earlier diagnosis, management, and treatment of various ailments throughout fearful times. The goal of this systematic review is to look at how IoMT and AI may help support, secure, and improve the healthcare system. In this review article, we see quickly how these fields are lowering human labor and making remote medical surveillance possible. We also go through the IoMT's hardware platform, as well as its implementations and associated topics. We also want to talk about why, because of the security concerns, mainstream acceptance and adoption are sluggish. These sectors are continuing to provide fresh perspectives not just in the fight against the novel Coronavirus pandemic, but also in a variety of other disciplines. This study aims to provide a comprehensive overview of AI and machine learning as important methods in healthcare and related disciplines. [ABSTRACT FROM AUTHOR]

Congestive heart failure (CHF) is a chronic heart disease that causes debilitating symptoms and leads to higher mortality and morbidity. In this paper, we present HARPER, a novel automatic detector of CHF episodes able to distinguish between Normal Sinus Rhythm (NSR), CHF, and no-CHF. The main advantages of HARPER are its reliability and its capability of providing an early diagnosis. Indeed, the method is based on evaluating real-time features and observing a brief segment of ECG signal. HARPER is an independent tool meaning that it does not need any ECG annotation or segmentation algorithms to provide detection. The approach was submitted to complete experimentation by involving both the intra- and inter-patient validation schemes. The results are comparable to the state-ofart methods, highlighting the suitability of HARPER to be used in modern IoMT systems as a multi-class, fast, and highly accurate detector of CHF. We also provide guidelines for configuring a temporal window to be used in the automatic detection of CHF episodes. [ABSTRACT FROM AUTHOR]

Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems. In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully‐connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems. [ABSTRACT FROM AUTHOR]

The proliferation of chronic disorders such as COVID-19 has recognized the importance of people all over the world having immediate access to healthcare. The recent pandemic has shown deficiencies in the traditional healthcare infrastructure, namely that hospitals and clinics alone are inadequate for grappling with such a disaster. One of the key technologies that favours new healthcare solutions is smart and interconnected wearables. Thanks to developments in the Internet of Things (IoT), these wearables will now collect data on an unprecedented scale. However, as a result of their extensive use, security in these critical systems has become a major concern. This paper presents an intrusion detection mechanism based on Machine Learning Algorithms for healthcare applications used in home network environments. Experiments are carried out on a home network to detect attacks against a health care application. Experiments using the proposed mechanism based on Machine Learning algorithms to detect attacks against a healthcare application are carried out on a home network, and the results show a good performance of the used algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the use in different stages of pregnancy of ultrasound imaging and to examine the tumors diagnosed during lactation or pregnancy. There are recent advancements in the application of obstetric ultrasound and imaging techniques helpful for improving the outcome of the pregnancy using various Learning techniques. This paper addresses the need to implement sustainable ultrasound standards with an acceptably high maternal and perinatal mortality rates to provide better and more affordable, quality Ultrasonic Flaw (UT) equipment which can improve Obstetric health care. The state-of-the-art learning approach for obstetric ultrasound is a category of methods in machine learning that are gaining popularity and attracting interest in various fields, including image processing and computer vision. In this paper advanced Machine learning processes map a raw input image to the desired output image using logistic regression classifier(LRC) and Convolution neural networks (CNNs) are of particular interest among all Machine learning methods. Furthermore, we have utilized the Internet of Medical Things (IoMT) for obstetric tumor image segmentation and identification of tumors for the medical experts. The experimental results show the LRC based on CNN can be utilized to predict the output of the ultrasound of obstetric with increased maternal and perinatal mobility rates.

Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also attained. For the disallowed class, the scores were 89, 73, and 80% for precision, recall, and F1-measure, respectively. The performance of our proposed BPS is subdivided into two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age, etc. The proposed system is outstanding as none of the earlier revised works of literature described a recommender system of this kind.

Patient gesture recognition is a promising method to gain knowledge and assist patients. Healthcare monitoring systems integrated with the Internet of Things (IoT) paradigm to perform the remote solutions for the acquiring inputs. In recent years, wearable sensors, and information and communication technologies are assisting for remote monitoring and recommendations in smart healthcare. In this paper, the dependable gesture recognition (DGR) using a series learning method for identifying the action of patient monitoring through remote access is presented. The gesture recognition systems connect to the end-user (remote) and the patient for instantaneous gesture identification. The gesture is recognized by the analysis of the intermediate and structuring features using series learning. The proposed gesture recognition system is capable of monitoring patient activities and differentiating the gestures from the regular actions to improve the convergence. Gesture recognition through remote monitoring is indistinguishable due to the preliminary errors. Further, it is convertible using series learning. Therefore, the misdetections and classifications are promptly identified using the DGR and verified by comparative analysis and experimental study. From the analysis, the proposed DGR approach attains 94.92% high precision for the varying gestures and 89.85% high accuracy for varying mess factor. The proposed DGR reduces recognition time to 4.97 s and 4.93 s for the varying gestures and mess factor, respectively. [ABSTRACT FROM AUTHOR]

The escalating demand for biomedical systems that can precisely diagnose and manage critical diseases underscores the need for innovative solutions. A non-invasive and intelligent Internet of Medical Things (IoMT) system emerges as a promising technology, potentially enabling physicians to assess patients with reduced health risks. The respiratory rate is a pivotal vital sign among the primary clinical assessments. The allure of Radio Frequency (RF) sensing lies in its ability to monitor respiratory patterns without direct contact. However, the practical implementation of such systems often necessitates supplementary hardware to manage the extensive data and radio functionalities, leading to concerns related to cost and feasibility. Software-Defined Radio (SDR) technology presents itself as a viable solution to these challenges. This research introduces a comprehensive framework for the IoMT system, aiming to diagnose respiratory abnormalities early through RF sensing and SDR technology. We employ a deep learning framework and compare its performance with traditional machine learning models to ensure reliable and precise classification of respiratory abnormalities. The achieved results underscore the superiority of deep learning frameworks over conventional machine learning models in classifying respiratory anomalies. Specifically, the deep learning framework exhibits exceptional performance in discerning the temporal dependencies and patterns inherent in respiratory abnormalities, achieving an average accuracy exceeding 98% for each respiratory abnormality classification. [Display omitted] • Proposed innovative IoMT framework, which harnesses the power of Radio Frequency (RF) sensing and Software Defined Radio (SDR) technology to provide an early and contactless diagnosis of respiratory abnormalities, including sleep apnea. This system is designed with the patient's comfort in mind, eliminating the need for invasive sensors or tethered devices. • To Ensure the accuracy and reliability of the data, we employ advanced signal-processing algorithms. These algorithms refine the raw data, eliminating noise and artifacts, thereby enhancing the accuracy of respiratory rate measurements. • The proposed approach is the implementation of a deep learning framework, specifically designed for classifying respiratory patterns. In comparative analyses, this framework consistently outperforms conventional machine learning models, highlighting its effectiveness and potential to revolutionize respiratory health monitoring. [ABSTRACT FROM AUTHOR]

Recognizing human activity from highly sparse body sensor data is becoming an important problem in Internet of Medical Things (IoMT) industry. As IoMT currently uses batteryless or passive wearable body sensors for activity recognition, the data signals of these sensors have a high sparsity, which means that the time intervals of sensors' readings are irregular and the number of sensors' readings in time unit is frequently limited. Therefore, learning activity recognition models from temporally sparse data is challenging. Traditional machine learning techniques cannot be applicable in this scenario due to their focus on a single sensing modality requiring regular sampling rate. In this work, we propose an effective end-to-end deep neural network (DNN) model to recognize human activities from temporally sparse data signals of passive wearable sensors that improve the accuracy rate of human activity recognition. A dropout technique is used in the developed model to deal with sparsity problem and avoiding overfitting problem. In addition, optimization of the proposed DNN model was performed by evaluating a different number of hidden layers. Various experiments were conducted on a public clinical room dataset of sparse data signals to compare the performance of the proposed DNN model with the conventional and other deep learning approaches. The experimental results demonstrate that the proposed DNN model outperforms the existing state-of-the-art methods in terms of lower inference delay and activity recognition accuracy. [ABSTRACT FROM AUTHOR]

The Internet of Things is visualized as a fundamental networking model that bridges the gap between the cyber and real-world entity. Uniting the real-world object with virtualization technology is opening further opportunities for innovation in nearly every individual's life. Moreover, the usage of smart heterogeneous multimedia devices is growing extensively. These multimedia devices that communicate among each other through the Internet form a unique paradigm called the Internet of Multimedia Things (IoMT). As the volume of the collected data in multimedia application increases, the security, reliability of communications, and overall quality of service need to be maintained. Primarily, distributed denial of service attacks unveil the pervasiveness of vulnerabilities in IoMT systems. However, the Software Defined Network (SDN) is a new network architecture that has the central visibility of the entire network, which helps to detect any attack effectively. In this regard, the combination of SDN and IoMT, termed SD-IoMT, has the immense ability to improve the network management and security capabilities of the IoT system. This article proposes an SDNassisted two-phase detection framework, namely SD-IoMT-Protector, in which the first phase utilizes the entropy technique as the detection metric to verify and alert about the malicious traffic. The second phase has trained with an optimized machine learning technique for classifying different attacks. The outcomes of the experimental results signify the usefulness and effectiveness of the proposed framework for addressing distributed denial of service issues of the SD-IoMT system. [ABSTRACT FROM AUTHOR]