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Embedded devices are used in many domains, including healthcare, industries, and home automation, all of which entail significant workloads. As a direct consequence, the embedded devices require retrieval and processing of data of large volume, which occupy large memory space in the embedded devices. Compression along with workload characterization is an effective technique for minimizing memory usage and to the improvement of endurance of memory in such devices. This paper investigates the embedded workload characterization using Extreme Learning Machine (ELM) that is particularly suitable for large-scale datasets and real-time applications. Though ELM is single-layer feedforward network, its input weight randomization has a considerable effect on the accuracy of the classification. In this paper, the authors have proposed a hybrid algorithm for the optimization of the randomization of ELM. The Particle Swarm Optimizer (PSO), the Genetic Algorithm (GA), the Ant Colony Optimizer (ACO), and the Whale Optimization Algorithm (WOA) were used in the optimization of the classification process in ELM to increase the accuracy of the results. The input data must be categorized based on the energy consumed by each workload to proceed with further processing according to the system requirements. This paper explores and analyses the performance of hybridized ELM-Genetic algorithm (ELM-GA), ELM-Particle Swarm Optimization (PSO), ELM-Ant Colony Optimizer (ELM-ACO), and ELM- Whale Optimization Algorithm (ELM-WOA) optimization algorithms. The embedded benchmarks Internet of Medical Things (IoMT), Mi Benchmark (MiBench), and Embedded Microprocessor Benchmark Consortium (EEMBC) have been used as a dataset for classification in this paper. The extensive experimental study shows that the hybridized ELM-WOA provides a 98.5 % classification accuracy, 98.1 % specificity, and 98 % sensitivity compared to the other optimization methods discussed in this paper. [ABSTRACT FROM AUTHOR]

The consistency and duration of the menstrual cycle exhibit significant associations with specific psychiatric conditions throughout an individual's lifespan. The proposed methodology surveys the relationship between psychiatric disorders and the length or regularity of the menstrual cycle and analyzes the difficulties undergone by the women. A comprehensive dataset is generated and a mathematical model using an exploratory data analytics approach is developed, in order to establish a correlation between these variables. It utilizes a cyclic methodology, leveraging shared menstrual data and a predictive model derived from vehicles to enhance network learning. A decentralized secure learning procedure is implemented to ensure data privacy and security. The transfer learning techniques helps to enhance the ability to learn from diverse data distributions in IoMT (Internet of Medical Things) networks, improve the robustness of the learning process. This approach presents a practical and effective solution for IoMT network learning, allowing each participant to contribute their individual features to collectively extract valuable insights from the data. The decentralization facilitates end-users in accessing their personal medical records while ensuring privacy, irrespective of their location and time. This system also achieves a minimal delay sensitivity of 3.2%, by providing timely access to the required information. [ABSTRACT FROM AUTHOR]

Generative AI (GenAI) represents a significant advancement in Artificial intelligence research, offering numerous benefits and opening new avenues for innovation across various domains. In healthcare, Generative AI has shown promise in applications such as drug discovery, personalized medicine, and medical imaging. This paper examines the role of Generative AI in rule-based systems, where vulnerabilities are detected with the help of formal logic. In this context, the ruleset is generated and tested to evaluate the performance of rule-based systems with the aid of GenAI. The effectiveness of the GenAI tool was evaluated using a publicly available case study from a laboratory setting. The results show that using generative Artificial intelligence in rule-based systems leads to increased creativity, continuous learning, and robust performance. GenAI responded to each use case and provided the desired results compared to traditional rule-based systems. This integration of advanced AI techniques with traditional rule-based systems ensures that these hybrid systems perform reliably and effectively. [ABSTRACT FROM AUTHOR]

Different countries may use E-health in Health systems that include all the institutions, organizations, and resources that are devoted to promote, sustain, or restore health. The basic goal of E-health projects is to enhance the delay of the service. Healthcare data now are being moved and collected from Internet of Medical Things (IoMT) environment. Different researches shows that recent E-health application is deployed in distributed computing and especially cloud computing is being used more frequently. Most of these applications are based on intelligent algorithm's such as KNN, SVM and deep learning algorithms etc... Which require more height power computing and storage capacity, but cloud computing with this delicate algorithm and with the sensitive health domain has some tradeoffs in handling the large amount of data from different IoMT devices, so it effects the delay which can cause risk. We propose Edge Computing to address the limitations of cloud. By transferring some of the computing to the network's edge, edge computing can significantly increase processing efficiency. In order to accomplish our goal, we first deploy our health application based on IoMT environment in a cloud. Then, we run it in an edge, and we analyze the response times in each of these situations. The experimental study proves that the proposed approach reduces delay in response time compared to the use of clouds. [ABSTRACT FROM AUTHOR]

Intravenous (IV) infusion is a pervasive medical intervention, administered to approximately 90% of hospitalized patients. Phlebitis, characterized by inflammation of the veins resulting from infusion, stands as a prevalent complication, ranking fourth among hospitalacquired infections globally. This research investigates the efficacy of a Smartphone Early Warning System (EWS) display in mitigating the incidence of phlebitis within the Safa treatment room at Aisyiyah Hospital. Employing a pre-experimental research design with a Static-group Comparison approach, 16 respondents were allocated to treatment and control groups. The Mann-Whitney Test, a statistical analysis, unveiled a significant difference (P Value = 0.001 < 0.05) in phlebitis incidence between the treatment group, utilizing the Smartphone EWS display, and the control group, which relied on conventional monitoring methods. Notably, the average rank of phlebitis incidence in the control group (21.12) exceeded that in the treatment group (9.78). This study sheds light on the potential of the Smartphone EWS display to curtail phlebitis during infusion, emphasizing its role in advancing nursing care quality through real-time monitoring and early prevention strategies. [ABSTRACT FROM AUTHOR]

The widespread adoption of the Internet of Things has significantly enhanced our daily lives across various dimensions. E-health has significantly benefited from advancements in the Internet of Things (IoT), particularly with the emergence of the Internet of Medical Things (IoMT). A sophisticated wireless sensor network produces a huge amount of data, requiring robust cloud-based hardware for precise processing and categorization. The IoMT allows for the extensive gathering of medical data from incoming hospital patients, enabling real-time monitoring of vital signs and health statuses. Nevertheless, effectively prioritizing patients in emergencies is challenging due to the importance and complicatedness of the data. To tackle this issue, an innovative solution involves integrating Explainable Artificial Intelligence into the IoMT ecosystem. By incorporating Explainable AI, the system enhances explainability, fostering trust and reliability in patient prioritization. This provides healthcare providers a more reliable prioritization mechanism that aligns with established medical guidelines. The study explores IoMT devices for collecting medical data from incoming patients, focusing on the MQTT protocol for lightweight devices, aiming to guide patients to the right department and prioritize emergency management through IoMT data analysis. [ABSTRACT FROM AUTHOR]

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]

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]

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple security and privacy problems. The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks. This article presents a private and easily expandable blockchain-based framework for the IoMT. The proposed framework contains several participants, including private blockchain, hospitalmanagement systems, cloud service providers, doctors, and patients. Data security is ensured by incorporating an attributebased encryption scheme. Furthermore, an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network. The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner. The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency. Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations. The average latency is 61 ms and 16 ms for read and write operations, respectively. [ABSTRACT FROM AUTHOR]

Integrating the Internet of Medical Things (IoMT) in the Hospital system has modified the traditional medical service from a reactive system based on hospitalization and diseases to a preventive and interoperable system based mainly on the interactive data flow between patients and health professionals. Medical data is collected and processed using medically connected objects (MCOs). According to gathered data, the new medical system should be able to sort patient states based on urgent and critical vital signs, and consequently, priorities are defined. In this paper, we focus on managing priority in hospital emergencies to adapt dynamic operations and interactions with different stakeholders according to the changes in their execution context. Indeed, based on data sensed from MCOs implemented in ambulances, emergency rooms might be prepared to receive injured persons like victims of road accidents or other incidents. Therefore, we design a context-aware monitoring framework for injured people based on gathered medical data to manage priorities. [ABSTRACT FROM AUTHOR]

When it comes to improving people's health, various forms of smart city applications are put to use in the area of smart healthcare. Internet of Medical Things (IoMT) refers to an environment that consists of linked healthcare systems, computational capabilities, and health monitors. More and more health care providers are using this environment to enhance the quality of the services they provide (QoS). The transmission of data from intelligent medical equipment to IoMT necessitates the development of an effective solution. To enhance the quality of service (QoS) of the transmission of health data, it is necessary to develop and evaluate a novel multi-hop reinforced clustering-based routing protocol (MRCRP). This is necessitated by the importance of IoMT and 5G networks. The selection of cluster heads is done in a way that is more effective with energy in order to make better use of resources. The original data are transformed into meaningful data through the utilization of min-max normalization, which enables further research. It is possible to extract important characteristics by employing a technique known as principal component analysis (PCA). After that, the suggested routing method is put into action in order to successfully transfer the data. The proposed method's performance metrics, including delay, throughput, energy consumption, and packet drop ratio, as well as a comparative comparison with conventional routing protocols, are investigated. [ABSTRACT FROM AUTHOR]

Health care visualization through Internet of Things (IoT) over wireless sensor network (WSN) becomes a current research attention due to medical sensor evolution of devices. The digital technology-based communication system is widely used in all application. Internet of medical thing (IoMT) assisted healthcare application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation. These smart medical devices may consume with limited resources and also the data generated by these devices are large in size. These IoMT based applications suffer from the issues such as security, anonymity, privacy, and interoperability. To overcome these issues, data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and decryption. This article proposed a well-organized data aggregation and secured transmission approach. The data generated by the sensor are collected and compressed. Aggregator nodes (AN) received the compressed data and concatenate it. The concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptography-based encryption with Message Authentication code (MAC). Fog node extracts the forwarded data from AN using Fog message extractor method (FME) with decryption. The proposed system ensures data integrity, security and also protects from security threats. This proposed model is simulated in Network Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security, privacy and also reduces the communication cost. Fog node usages in between Aggregator and base station, will reduce the cloud server/base station computational overhead and storage cost. The proposed ideology is compared with existing data aggregation schemes in terms of computational cost, storage cost, communication cost and energy cost. Cost of communication takes 18.7 ms which is much lesser than existing schemes. [ABSTRACT FROM AUTHOR]

Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing progressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors' performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to minimize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods minimized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems. [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]

Data management becomes essential component of patient healthcare. Internet of Medical Things (IoMT) performs a wireless communication between E-medical applications and human being. Instead of consulting a doctor in the hospital, patients get health related information remotely from the physician. The main issues in the E-Medical application are lack of safety, security and privacy preservation of patient's health care data. To overcome these issues, this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage. Patients health data is collected from physician, smart devices etc. The main goal is to store this highly valuable health related data in a secure, safety, easy access and less cost-effective manner. In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work (PBFT-PoW). The implementation is done using cloud technology setup with smart contracts (PBFT-PoW). The accuracy rate of PBFT is 90.15%, for PoW is 92.75% and our proposed work PBFT-PoW is 99.88%. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is an online device that senses and transmits medical data from users to physicians within a time interval. In, recent years, IoMT has rapidly grown in the medical field to provide healthcare services without physical appearance. With the use of sensors, IoMT applications are used in healthcare management. In such applications, one of the most important factors is data security, given that its transmission over the network may cause obtrusion. For data security in IoMT systems, blockchain is used due to its numerous blocks for secure data storage. In this study, Blockchain-assisted secure data management framework (BSDMF) and Proof of Activity (PoA) protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system. The main aim is to enhance the data security over the networks. The PoA protocol enhances high security of data from the literature review. By replacing the malicious node from the block, the PoA can provide high security for medical data in the blockchain. Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio, precision ratio, security, and efficiency and less response time for Blockchain-enabled healthcare systems. [ABSTRACT FROM AUTHOR]

Reliable transmission is vital to the success of the next generation of communications technologies and Fifth Generation (5G) networks. Many sensitive applications, such as eHealth and medical services, can benefit from a 5G network. The Internet of Medical Things (IoMT) is a new field that fosters the maintenance of trust among various IoMT Device to Device (D2D) modern technologies. In IoMT the medical devices have to be connected through a wireless network and constantly needs to be self-configured to provide consistent and efficient data transmission. The medical devices need to be connected with sophisticated protocols and architecture to handle the synergy of the monitoring devices. Today, one of the commonly used algorithms in D2D communication is the Optimized Link State Routing protocol (OLSR). The OLSR is considerably good at effectively utilizing the bandwidth and reserving the paths. One of the major attack against the OLSR is the Node isolation attack, also known as the Gray hole denial of service attack. The Gray hole attack exploits the vulnerabilities present with sharing the topological information of the network. The attackers may use this topological information to maliciously disconnect the target nodes from the existing network and stops rendering the communication services to the victim node. Hence, considering the sensitivity and security concerns of the data used in e-Health applications, these types of attacks must be detected and disabled proactively. In this work, a novel Node Authentication (NA) with OLSR is proposed. The simulation experiments illustrated that the proposed protocol has an excellent Packet Delivery Ratio, minimal End-End delay, and minimal Packet loss when compared to the Ad-hoc On-Demand Distance Victor (AODV) protocol and the proposed authentication scheme was able to protect the OLSR protocol from a node isolation attack. [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]

Blockchain-enabled Internet of Medical Things (IoMT) is a secure, transparent, and decentralized network of medical devices which facilitates a patient-centric healthcare system for distributed exchange and management of medical records. The existing consensus protocols designed for a blockchain network such as Proof-of-Work, Proof-of-Stake, Proof-of-Negotiation, Proof-of-Authority, and Proof-of-X-Repute lack fast processing and security. This paper presents a scalable blockchain-enabled IoMT framework that uses gateway nodes to improve the performance. A novel hybrid consensus mechanism namely Proof-of-Context (PoCt) is also proposed for blockchain-enabled IoMT environment. The proposed PoCt consensus is a fast, reliable, and secure mechanism for block mining which consists of a fuzzy-based miner selection and a block validation process with multiple parameters. Fuzzy-based miner selection is done on the basis of context factor of a node, which is computed using the impact score, vote count and trust value. The trusted miner nodes are responsible for block generation and validation. The most trusted and efficient miner node having the highest neighbour density is being selected for block generation. The other miner nodes are responsible for block validation to prevent various malicious activities. The performance analysis of the PoCt consensus shows that it is comparatively 24% more efficient, 36% faster, 1.8% more reliable, and 65% less traffic overhead than fuzzy-based super leader nomination in PoS consensus mechanism, 65% faster than random-voting and game-theory based miner selection, 25% faster than Proof-of-Negotiation consensus, 6.1% more reliable and 58% less traffic overhead than Proof-of-Authority consensus mechanism for the network of 500 nodes. Security analysis of PoCt consensus algorithm ensures its resilience against various known mining attacks. • Development of a patient-centric blockchain-enabled IoMT framework. • Design of a hybrid consensus procedure namely, Proof-of-Context (PoCt). • A fuzzy-based miner selection for PoCt has been proposed. • Security and performance of PoCt consensus has been analyzed. [ABSTRACT FROM AUTHOR]

• Development of an innovative Interfering Input Classification Model (IICM) for remote patient monitoring. • Integration of whale optimization and DQN techniques for improved classification and connectivity identification. • Segregation of sensed data into discrete and continuous categories for enhanced accuracy and efficiency. • Reduction of classification errors and improvement in classification ratio, signal identification, and classification accuracy in remote patient monitoring systems. The Internet of Medical Things (IoMT)-based Remote Patient Monitoring (RPM) systems provide real-time data and insights about patients' conditions without the need for constant physical visits to healthcare facilities. The wearable sensors are responsible for sensing the psychological vitals of humans at different intervals to detect their precise health status. This article introduces an Interfering Input Classification Model (IICM) for addressing the continuous and discrete data extraction issues in RPM. The proposed model hybridizes whale optimization and deep-Q-learning for classification and connectivity identification between different interval data. The sensed information is segregated as discrete and continuous using the search process termination of the whales. Such classified data is validated for the maximum discreteness and its interconnection between the previous sequences. This interconnection is performed using Q-learning based on different state changes through continuous clinical range correlations. The proposed model exemplifies continuous and discrete signals (data) for abnormality detection and diagnosis recommendation. Thus the proposed model is reliable in improving the classification ratio and accuracy with fewer errors. The experimental values of the proposed model are as follows: IICM enhances classification by 6.65%, signal identification by 6.63%, and classification accuracy by 10.08%. This model reduces classification time by 9.51% and error by 10.14% for different ranges of inputs. [ABSTRACT FROM AUTHOR]

This paper proposes a remote healthcare system which is referred to as Telemedicine. Telemedicine is a platform that estableshes a connection between the patient and the doctor. This platform belongs to the internet of medical things (IoMT) by enabling multiple medical sensors to connect to a server either using multiple communication technologies such as Wi-Fi, Bluetooth or GSM technologies. The system collects data from several sensors and sends them using one of the aforementioned technologies using an Arduino Board, while the interface is made with matlab and C#. Additionally, a comparative study between the three communications technologies used to connect the medical sensors to the server is investigated. And a recommendation of the best suited technology is provided with regard to the nature of the application itself. [ABSTRACT FROM AUTHOR]

• Wearable Internet of Medical Things (IoMT) devices, such as wrist actigraphs, canprovides a more objective and effective approach to early-stage mental health diagnosis. • The methodology includes a custom imputation method, a novel combinatorial sampling method for sample expansion • a multi-branch deep learning architecture is employed with one branch fed with nighttime data and another branch with daytime data • The use of Gradient weighted Class Activation Map (Grad-CAM) technique aids in visualizing and comprehending the model's decision-making process. • The model achieved a commendable accuracy of 0.94 for distinguishing depressive and schizophrenic samples from control samples individually. Deep Learning (DL) holds immense potential in revolutionizing healthcare, offering robust support to clinicians and enhancing patient care. However, coming up with the right DL model is always challenging and depends on quality, quantity, and type of data. In this paper, two motor activity datasets are utilized: " Depresjon" dataset includes activity recordings from 32 healthy individuals (402 days) and 23 individuals with unipolar and bipolar depression (291 days) and the " Psykose" dataset consists of 22 schizophrenia subjects (285 days) and 32 healthy subjects (402 days). The motor activity data, represented as time-series signals, poses challenges due to variable lengths and non-uniform starting timestamps for each subject. Additionally, the daytime and nighttime distributions differ across samples, requiring explicit handling for Convolutional Neural Network models. To address this issue, a multi-branch DL architecture is employed with one branch fed with nighttime data and another with daytime data, capable of capturing features across various scales, accommodating patterns of different sizes. Moreover, the combined outputs of these branches are subjected to a self-attention-mechanism (MultiHeadAttention), which prioritizes essential features. The use of Gradient weighted Class Activation Map (Grad-CAM) technique aids in comprehending the model's decision-making process. The benchmark datasets were used to validate the model, which exhibited an accuracy of 0.94 for both classifying depressive and schizophrenic episodes from control subjects. An accuracy of 0.81 for classifying depressive episodes, and schizophrenics from control samples. This accuracy further increases when combining the control samples from both datasets, to 0.97 for depression and 0.98 for schizophrenia. [ABSTRACT FROM AUTHOR]

In this paper, we discuss current trends in how health professionals and patients are using wearables, connected devices, and software tools to deliver care and health monitoring purposes. We emphasize the importance of considering users' experience through understanding user workflows, their needs, and their limitations when creating connected health ecosystems (CHES). We discuss both the patient and the provider as "users" in the ecosystem. We note both barriers to using the Internet of medical things (IoMT) to create CHES and efforts to overcome them. The increasing penetration of the Internet and the availability of connected health devices along with changes in reimbursement policies provide an environment for CHES to grow. [ABSTRACT FROM AUTHOR]

The IoT devices with embedded mobile devices create the Internet of Mobile Things (IoMT) paradigm. Mobility is not supported by the routing protocol for low-power and lossy networks (RPL) created for static networks. IoMT has raised routing challenges such as link failure, instability, energy depletion, packet loss, and handover delay in the network. In this context, IoMT Fuzzy-based RPL (IoMT-FRPL) is proposed in this paper to enhance routing performance. Receiving Signal Strength Indicator (RSSI), Euclidean distance, Hop Count, and Expected Transmission Count (ETX) metrics are built into the fuzzy interface system for the mobile nodes in the network to conserve energy. The IoMT-FRPL consists of the following three key steps: The first steps are data transmission and motion investigation, the second is fuzzy-based prediction of a new static parent for the mobile node, and the third is verifying the unique attachment point. When conventional RPL, mRPL, and EMA-RPL were compared to IoMT-performance FRPL's in Cooja/Contiki 2.7, the simulation results revealed improvements in energy consumption, handover delay, packet delivery rate (PDR), and signaling cost. [ABSTRACT FROM AUTHOR]

Discernible patterns of a person's daily activities can be utilized to detect behavioral symptomatology of mental illness at early stages. Wearable Internet of Medical Things (IoMT) devices with sensors that collect motion data and provide objective measures of physical activity can help to better monitor and detect potential episodes related to the mental health conditions at earlier, more treatable stages. This research puts forward a neuro-symbolic model which uses learnable parameters with integrated knowledge for detection of depression episodes using IoMT based actigraphic input. A novel deep fuzzy model, Depress-DCNF is a hybrid of convolutional neural network (CNN) and an adaptive neuro fuzzy inference system (ANFIS) where CNN is used to extract high-level features from the motor activity recordings which are eventually combined with the discriminative statistical features to produce an optimized feature map. This optimized feature map is finally used to train the ANFIS model which accurately performs the depression classification task. The model is validated on the Depresjon benchmark dataset and compares favorably to state-of-the-art approach giving a superior performance accuracy of 85.10%. • Evaluation of the effectiveness of combination of deep learning extracted features with statistical features in depression assessment. • Design of neuro-symbolic classification model for depression detection based on actigraphic inputs from IoMT. • Exploration of the capabilities of neuro-fuzzy model for handling uncertainties in the motor activity data. • Use of Adaptive Neuro Fuzzy Inference System to better classify the data in extracted feature space. [ABSTRACT FROM AUTHOR]

• A fusion-based watermarking algorithm to secure digital medical records. • Objective measures confirm that the strength of the suggested algorithm since it is also suitable for cloud environment. • The suggested algorithm offers better robustness in best case over the traditional schemes. The healthcare digital records include personal and sensitive information about the patient. These records are easily being distributed via different platform for clinical diagnosis purpose. This may lead to compromise with the security of the transmitted records. A fusion-based watermarking algorithm to secure medical records using non-subsampled contourlet transforms (NSCT), fast Walsh-Hadamard transform (FWHT) and Schur-decomposition is proposed in this paper. In the first part of the paper, mark images are fused using the NSCT algorithm, which generate final mark image. In the second part, using NSCT, FWHT and Schur-decomposition hide the fused-mark into the host medical image. The final part is marked data encryption based on hyperchaotic and DNA to provide the integrity and authenticity of the record. Objective measures confirm that the strength of the suggested algorithm since it is also suitable for cloud environment. Further, it is demonstrated that the proposed algorithm has a strong ability to extract hidden mark than conventional ones. [ABSTRACT FROM AUTHOR]

• We develop a robust and secure watermarking algorithm to solve the issue of violation of copyright and integrity of medical records. • Fuzzy optimization scheme is used to compute the optimal embedding strength for the embedding of mark data. • Encryption is considered to enhance the security of our algorithm. • The suggested algorithm is suitable for cloud-based healthcare applications. The healthcare records include personal and sensitive information about the patient. These records are easily distributed via different platform for clinical diagnosis purpose. Presently, large volumes of medical data are stored on cloud platform. However, outsourcing medical data to these popular platforms may introduce security issues. Thus, a secure watermarking algorithm based on integer wavelet transform (IWT)-Schur-Randomized Singular Value Decomposition (RSVD) is put forward for medical records sharing in the cloud environment. To ensure high level of authentication, discrete wavelet transform (DWT) based embedding is conducted to achieve dual watermarking by concealing system MAC address in the logo image. Prior to this, the system address is encoded via turbo code to reduce /eliminate the channel noise, if any. Further, both invisibility and robustness are achieved by fuzzy inference system (FIS), and the marked image is encrypted using chaotic encryption scheme to further improve the security. Furthermore, experimental results is reported and compared with the existing works which demonstrated promising outcomes, indicating its potential towards realization of a value-added tool for smart healthcare applications. [ABSTRACT FROM AUTHOR]

• We have designed a device-to-device direct authentication scheme for IoMT. • In the proposed scheme, each device uses ECC based secure certificate for authentication purposes. • In addition to the provision of traditional security requirements, the proposed scheme resists physical capturing and related attacks. • The proposed scheme is secure under the formal RoR model. The Internet of Medical Things (IoMT) is structured upon both the sensing and communication infrastructure and computation facilities. The IoMT provides the convenient and cheapest ways for healthcare by aiding the remote access to the patients' physiological data and using machine learning techniques for help in diagnosis. The communication delays in IoMT can be very harmful to healthcare. Device to device (D2D) secure communication is a vital area that can reduce communication delays; otherwise, caused due to the mediation of a third party. To substantiate a secure D2D communication framework, some schemes were recently proposed to secure D2D based communication infrastructure suitable for IoMT-based environments. However, the insecurities of some schemes against device physical capture attack and non-provision of anonymity along with related attacks are evident from the literature. This calls for a D2D secure access control system for realizing sustainable smart healthcare. In this article, using elliptic curve cryptography, a certificate based D2D access control scheme for IoMT systems (D2DAC-IoMT) is proposed. The security of the proposed D2DAC-IoMT is substantiated through formal and informal methods. Moreover, the performance analysis affirms that the proposed scheme provides a good trade-off between security and efficiency compared with some recent schemes. [ABSTRACT FROM AUTHOR]

IoMT sensors such as wearables, moodables, ingestible sensors and trackers have the potential to provide a proactive approach to healthcare. But grouping, traversing and selectively tapping the IoMT data traffic and its immediacy makes data management & decision analysis a pressing issue. Evidently, the selection process for real-world, time-constrained health problems involves looking at multivariate time-series data generated simultaneously from various wearables resulting in data overload and accuracy issues. Computational intelligence of edge analytics can extend predictive capability by quickly turning digital biomarker data into actions for remote monitoring and trigger alarm during emergency incidents without relying on backend servers. But the pervasive generation of data streams from IoMT levies significant issues in data visualization and exploratory data analysis. This paper presents a genetically optimized Fuzzy C-means data clustering technique for affective state recognition on the edge. Clustering segregates the biomarker data in chunks and generates a summarized data for each subject which is then genetically optimized to avoid stagnation in local optima. A multi-level convolution neural network is finally used to classify the affective states into the baseline, stress and amusement categories. The model is evaluated on the publicly available WESAD dataset and compares favorably to state-of-the-art with less time complexity. It demonstrates the use of data clustering technique for numerosity reduction of real-time data streams in intelligent edge analytics which facilitates fast analysis of affective state of the user. • Continuous IoMT-based data analysis showcasing computational intelligence on the edge. • Extending predictive capabilities for turning digital biomarker data into actions. • Optimized Fuzzy C-means data clustering for data summarization. • Deep hierarchical model for affective state detection on edge using optimized data. • Validated on publicly available WESAD dataset IoMT biomarker data • Compares favorably to state-of-the-art with less time complexity. [ABSTRACT FROM AUTHOR]

• A novel actigraphy encoding, segmentation and analysis system is proposed for processing vibroarthrography data to identify cartilage degeneration. • The proposed algorithm includes two unique contributions in physiological signal pre-processing and adaptive segmentation. • Two new actigraphy-specific features are introduced in this research which characterize limb movements using accelerometers. • The proposed system in this research work has the potential to be applied in a telehealth framework. Hectic lifestyle coupled sometimes with deficient choices in food and exercise, has led to an increase in the number of individuals being diagnosed with joint generation disorders. In addition, a substantial number of individuals in specialized occupations (such as army) or sports suffer from joint related injuries, thereby leading to surgical replacements and in some severe cases as disability. Standard methods of detecting joint disorders or disabilities include arthroscopy and arthrogram. Vibroarthrography (VAG) is a non-invasive technique wherein the specialist listens to sounds generated from joint movements recorded using actigraphs. In this study, we propose a system which encodes, analyses and segments actigraphy-based VAG data for assessing the severity of cartilage degeneration, and highlighting instances of activity which causes crackling sounds during limb movements. The proposed system encapsulates IoMT (Internet of Medical Things) requirements by providing efficient data compression and analysis at the source. Using an actigraphy dataset from 89 participants, our experiments yielded that the system is able to compress the actigraphy data into 3-bits per sample, thereby reducing the signal size by about 88%, without losing any vital limb movement information. This has been further validated using a simple pattern classification of 3-bit quantized VAG data from participants with healthy and unhealthy knee joints. The method, which yielded a recognition accuracy of 84.6%, as compared to raw VAG data which yielded 80% accurate results. In addition, the proposed adaptive segmentation scheme in the system, leverages the 3-bit encoding by correctly identifying 90% of the segments of interest from each VAG signal. [ABSTRACT FROM AUTHOR]