Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

Determinants of internet addiction among medical students in Casablanca: a cross-sectional study

Objective:This study aimed to estimate the prevalence and determinants of Internet addiction among medical students at the Faculty of Medicine and Pharmacy of Casablanca,Morocco.Methods:This was a cross-sectional study conducted among students at the Faculty of Medicine and Pharmacy in Casablanca between October and March 2020.An online questionnaire was administered to students to collect data and internet addiction was assessed by the Young questionnaire.A score threshold≥50 was adopted to define addiction.Univariate and multivariate logistic regression analyses were used to identify factors associated with internet addiction.Results:Out of a total of 4093 FMPC students enrolled in the 2020-2021 academic year,506 agreed to participate in this study,including 303 females and 203 males.The mean addiction score assessed on the Young scale was(49.08±16.11).The prevalence of Internet addiction was 44.5%(225/506,95% CI:40% to 49%).Multiple regression analysis showed that being older than 20 years(OR=0.17,95% CI:0.40 to 0.64),being female(OR=1.70,95% CI:1.04 to 2.78),being in the dissertation year(6th year)(OR=5.17,95% CI:2.23 to 11.44),having a history of psychiatric consultation(OR=2.64,95% CI:1.34 to 5.21),having divorced parents(OR=2.64,95% CI:1.05 to 5.87),use of sleeping medication(OR=2.9,95% CI:1.05 to 3.70),sleep disorders(OR=2.06,95% CI:1.25 to 3.79),sleep deprivation(OR=2.26,95% CI:1.39 to 3.65),excessive daytime sleepiness(OR=5.39,95% CI:2.19 to 13.24),anxiety disorders(OR=1.47,95% CI:1.18 to 2.30),duration of internet connection(>4 h)(OR=11.43,95% CI:4.85 to 27.66),and having frequent conflicts with parents(OR=2.37,95% CI:1.49 to 3.79)and friends(OR=0.26,95% CI:0.11 to 0.65)were independently associated with internet addiction.Conclusion:The prevalence of Internet addiction among medical students in Casablanca remains high.Targeted action on the determinants would be of great value in prevention.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis.To overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the image.And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models.In this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and PSNR.The experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

Efficient Certificateless Authenticated Key Agreement for Blockchain-Enabled Internet of Medical Things

Internet of Medical Things(IoMT)plays an essential role in collecting and managing personal medical data.In recent years,blockchain technology has put power in traditional IoMT systems for data sharing between different medical institutions and improved the utilization of medical data.However,some problems in the information transfer process between wireless medical devices and mobile medical apps,such as information leakage and privacy disclosure.This paper first designs a cross-device key agreement model for blockchain-enabled IoMT.This model can establish a key agreement mechanism for secure medical data sharing.Meanwhile,a certificateless authenticated key agreement(KA)protocol has been proposed to strengthen the information transfer security in the cross-device key agreement model.The proposed KA protocol only requires one exchange of messages between the two parties,which can improve the protocol execution efficiency.Then,any unauthorized tampering of the transmitted signed message sent by the sender can be detected by the receiver,so this can guarantee the success of the establishment of a session key between the strange entities.The blockchain ledger can ensure that the medical data cannot be tampered with,and the certificateless mechanism can weaken the key escrow problem.Moreover,the security proof and performance analysis are given,which show that the proposed model and KA protocol are more secure and efficient than other schemes in similar literature.

Machine Learning-Enabled Communication Approach for the Internet of Medical Things

The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

Network Learning-Enabled Sensor Association for Massive Internet of Things

The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single GW.This is mitigated by allotting various channels to adjoining GWs.Furthermore,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network execution.Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs.Recently,in remote IoT networks,the utilization of machine learning(ML)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these ML techniques.Therefore,this paper proposes the use of an RL algorithm for GW determination and association in IoT networks.For this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(MAB)calculation,to investigate and determine the optimal GW with which to associate.In this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network topologies.The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches.

Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things(IoMT)enabled e-healthcare has the potential to greately improve conventional healthcare services significantly.However,security and privacy become major issues of IoMT because of the restricted processing abilities,storage,and energy constraints of the sensors.Therefore,it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors.In order to ensure security on sensitive medical data,effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers.In this view,this study designs an effective metaheuristic optimization based encryption with user authentication(EMOE-UA)technique for IoMT environment.This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity.Moreover,the EMOE-UA technique employs optimal multikey homomorphic encryption(OMKHE)technique to encrypt the IoMT data.Furthermore,the improved social spider optimization algorithm(ISSOA)was employed for the optimal multikey generation of the MKHE technique.The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects.The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

In this paper,the Internet ofMedical Things(IoMT)is identified as a promising solution,which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service(QoS)in the healthcare sector.However,problems with the present architectural models such as those related to energy consumption,service latency,execution cost,and resource usage,remain a major concern for adopting IoMT applications.To address these problems,this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming(MILP),with the objective of efficiently processing and placing IoMT applications in the edge-fog-cloud computing environment,while maintaining certain quality standards(e.g.,energy consumption,service latency,network utilization).A modeling environment is used to assess and validate the proposed model by considering different traffic loads and processing requirements.In comparison to the other existing models,the performance analysis of the proposed approach shows a maximum saving of 38%in energy consumption and a 73%reduction in service latency.The results also highlight that offloading the IoMT application to the edge and fog nodes compared to the cloud is highly dependent on the tradeoff between the network journey time saved vs.the extra power consumed by edge or fog resources.

On the design of an AI-driven secure communication scheme for internet of medical things environment

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the Internet.It is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data.In other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare outcomes.However,this system is prone to securityand privacy-related attacks on healthcare data.Therefore,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential.To mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT.The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT.Furthermore,we provide the security analysis of the proposed scheme to show its security against different possible attacks.Moreover,a comparative study of the proposed scheme with other similar schemes is presented.Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality attributes.Finally,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

Diabetes and cancer: Associations, mechanisms, and implications for medical practice

Both diabetes mellitus and cancer are prevalent diseases worldwide. It is evident that there is a substantial increase in cancer incidence in diabetic patients. Epidemiologic studies have indicated that diabetic patients are at significantly higher risk of common cancers including pancreatic, liver, breast, colorectal, urinary tract, gastric and female reproductive cancers. Mortality due to cancer is moderately increased among patients with diabetes compared with those without. There is increasing evidence that some cancers are associated with diabetes, but the underlying mechanisms of this potential association have not been fully elucidated. Insulin is a potent growth factor that promotes cell proliferation and carcinogenesis directly and/or through insulin-like growth factor 1(IGF-1). Hyperinsulinemia leads to an increase in the bioactivity of IGF-1 by inhibiting IGF binding protein-1. Hyperglycemia serves as a subordinate plausible explanation of carcinogenesis. High glucose may exert direct and indirect effects upon cancer cells to promote proliferation. Also chronic inflammation is considered as a hallmark of carcinogenesis. The multiple drugs involved in the treatment of diabetes seem to modify the risk of cancer. Screening to detect cancer at an early stage and appropriate treatment of diabetic patients with cancer are important to improve their prognosis. This paper summarizes the associations between diabetes and common cancers, interprets possible mechanisms involved, and addresses implications for medical practice.

Exploration and Research on the Integrated Development of“Internet Plus Medical Treatment”

Objective To analyze the development of“internet plus medical treatment”and to explore advantages.Methods The literature of“internet plus medical treatment”was systematically combed and analyzed.Results and Conclusion After exploring the status quo of“internet plus hospitals”,smart pharmacy and web-assisted health management in China,we find that there are some problems in the medical service at present,such as the imperfect laws and regulations,the hidden dangers of information security and the obstacles of medical insurance payment.Therefore,we propose that the development of web-assisted medical service should be led by the government and relevant policies must be improved.Then,self-regulation should be strengthened,and industry standards should be enhanced.Three suggestions are made to improve medical insurance payment and benefit both hospitals and patients,which can provide reference for promoting the development of“internet plus medical treatment”in China.

Resources for Medical Editors in World Association of Medical Editors

Introduction Established in 1995, WAME （pronounced ＂whammy＂） is a nonprofit voluntary association of editors of peer-reviewed medical journals from countries around the world who seek to foster international cooperation among and education of medical journal editors.

Call for Papers International Symposium on Toxicology Jointly Sponsored by the China Preventive Medical Association and the Chinese Pharmacological Society

The scientific program will consist of symposia and poster sessions. Topics related to theoretical and applied research in the domain of toxicology and toxicological studies on chemicals of public concern will be welcome. The presenter’s name, address, and telephone and FAX numbers should be submitted along with the title of the presentation and whether it is oral or poster. Deadline: April 15, 1990. Papers should be submitted to:

External Association Analysis of Famous Doctors' Dysmenorrhea Medical Cases Based on Data Mining

Objective:Dysmenorrhea is a common gynecological disease.Some severe symptoms affect the quality of life,causing physical and psychological discomfort.To analyze the dysmenorrhea cases of famous Senior traditional Chinese medicine(TCM)doctors and TCM masters through the data mining technology and explore core related rules between the symptoms,pathogenesis,coated tongue,pulse condition,so as to deeply deconstruct the law of TCM clinical differentiation and treatment of dysmenorrhea,verify the effectiveness of the law,and optimize the clinical TCM diagnosis and treatment scheme.Methods:The external correlation analysis of dysmenorrhea medical cases through the Medcase data processing platform and the algorithm of FP-Growth enhanced association analysis was applied.Results:Altogether 171 medical records were studied,which included 171 female patients and 483 clinical visits.The age of the oldest patient was 49,the age of the youngest was 14,the average age was 28 years old.The medical cases involoved 41 kinds of pathogenesis,147 clinical symptoms,16 types of pulse condition,84 types of coated tongue and 292 traditional Chinese medicine.The external correlation processing revealed that there were 25 groups of clinical symptoms and TCM association rules;17 groups of clinical symptoms and secondary TCM association rules;21 groups of pathogenesis and Chinese medicine association rules;34 rules of coated tongue and Chinese medicine association rules;19 rules of pulse condition and Chinese medicine association rules;28 rules of clinical symptoms and pathogenesis associations.Conclusion:When treating dysmenorrhea according to the differentiation and treatment of TCM,the viscera location of the core pathogenesis mainly are kidney,spleen and liver,the pathological factors focus on blood deficiency,blood stasis and qi stagnation.The highly-related TCM include Danggui(Radix Angelicae Sinensis),Baishao(Radix Paeoniae Alba),Chuanxiong(Rhizoma Ligustici),Yanhusuo(Rhizoma Corydalis),Xiangfu(Rhizoma Cyperi).The study of these rules has reference value for the acquisition of the core pathogenesis,the clinical differentiation and the medication.

Efficient Group Blind Signature for Medical Data Anonymous Authentication in Blockchain-Enabled IoMT

Blockchain technology promotes the development of the Internet of medical things(IoMT)from the centralized form to distributed trust mode as blockchain-based Internet of medical things(BIoMT).Although blockchain improves the cross-institution data sharing ability,there still exist the problems of authentication difficulty and privacy leakage.This paper first describes the architecture of the BIoMT system and designs an anonymous authentication model for medical data sharing.This BIoMT system is divided into four layers:perceptual,network,platform,and application.The model integrates an anonymous authentication scheme to guarantee secure data sharing in the network ledger.Utilizing the untampered blockchain ledger can protect the privacy of medical data and system users.Then,an anonymous authentication scheme called the group blind signature(GBS)scheme is designed.This scheme can provide anonymity for the signer as that one member can represent the group to sign without exposing his identity.The blind property also can protect the message from being signed as it is anonymous to the signer.More-over,this GBS scheme is created with the lattice assumption,which makes it more secure against quantum attacks.In addition,the security proof shows that this GBS scheme can achieve the security properties of dynamical-almost-full anonymity,blindness,traceability,and non-frameability.The comparison analysis and performance evaluation of key size show that this GBS scheme is more efficient than similar schemes in other literature.

Relationships of loneliness and mobile phone dependence with Internet addiction in Japanese medical students

We investigated factors contributing to Internet addiction in 105 Japanese medical students. The subjects were administered by a self-reporting questionnaire designed to evaluate demographic factors, Internet addiction, loneliness, health-related lifestyle factors, depressive state, patterns of behavior, and mobile phone dependence. Results of multivariate logistic regression analysis indicated that loneliness and mobile phone dependence were positively related to degree of addiction. Our findings suggest that Internet addiction is associated with loneliness and mobile phone dependence in Japanese students.

Energy Aware Clustering with Medical Data Classification Model in IoT Environment

With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.