A Secure and Efficient Information Authentication Scheme for E-Healthcare System

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)devices.The cellular network consists of several towers installed at appropriate locations within a smart city.These cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk checking.Security is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant challenge.As a result,that requires attention,and without information authentication,such a barrier cannot be removed.So,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare system.The proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic discussion.In comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

In today’s digital era,e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices,computers and the inter-net to provide high-quality healthcare services.E-healthcare decision support sys-tems have been developed to optimize the healthcare services and enhance a patient’s health.These systems enable rapid access to the specialized healthcare services via reliable information,retrieved from the cases or the patient histories.This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions.In the current research work,a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System(SFLODL-DSS)is designed for the diagnosis of the Cardiovascular Diseases(CVD).The aim of the proposed model is to identify and classify the cardiovascular diseases.The proposed SFLODL-DSS technique primarily incorporates the SFLO-based Feature Selection(SFLO-FS)approach for feature subset election.For the pur-pose of classification,the Autoencoder with Gated Recurrent Unit(AEGRU)model is exploited.Finally,the Bacterial Foraging Optimization(BFO)algorithm is employed tofine-tune the hyperparameters involved in the AEGRU method.To demonstrate the enhanced performance of the proposed SFLODL-DSS technique,a series of simulations was conducted.The simulation outcomes established the superiority of the proposed SFLODL-DSS technique as it achieved the highest accuracy of 98.36%.Thus,the proposed SFLODL-DSS technique can be exploited as a proficient tool in the future for the detection and classification of CVD.

Consumer Trust--Challenge for E-Healthcare

The evolution of interuet within last years and continuous advances in electronic commerce and communication provide exciting opportunities to implement powerful framework of resources, tools and applications that revolutionize way in which healthcare institutions interact with their patients, as well as deliver and manage medical services. Internet-based healthcare is application of information and communication technologies across the whole range of healthcare functions. It covers everything from electronic prescriptions and computerized medical records to the use of new systems and services that cut waiting times and reduces data errors. Development and implementation of web-enabled communication, patient services and other e-health initiatives are increasingly important to maintaining competitive advantage and to compete for market share. More importantly, value added for patients by facilitating access to information and resources is expected to improve quality of services, speed of treatment and potentially to rationalize management of administrative processes. However, introductions of such e-healthcare services into market can be successful on condition that customers will recognize all these advantages and have trust in organizations provide theses e-services. In this paper authors will concentrate on customer trust as key factor determining success of e-healthcare. The purpose of this study will determine character and power of trust placed by customers in e-healthcare, and to identify factors influencing customers trust to e-healthcare. Authors have ventured thesis that customer trust to e-healthcare is high as consequence of even higher customer satisfaction with traditional healthcare services and great customer trust in traditional healthcare institutions.

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times,cities are getting smart and can be managed effectively through diverse architectures and services.Smart cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic diseases.With latest developments in digital data collection,AI techniques can be employed for clinical decision making process.On the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the globe.Generally,wearables can be employed in healthcare systems that instigate the development of CVD detection and classification.With this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC technique.The proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data collection.Then,the collected data is saved in cloud server for examination.Followed by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health condition.To accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)technique.In addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of CVD.The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

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.

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

A SUSTAINABLE PLATFORM FOR E-SERVICE SYSTEM DESIGN

By integrating system thinking and social psychology, this paper presents an Activity System Theory (AST)1 approach to the platform design of e-service systems in general, and e-healthcare systems in specific. In the first part, some important principles of AST and a sustainable model of human activity system are introduced. Then a project ‘Integrated Mobile Information System for Healthcare (IMIS)’ is presented to demonstrate how to construct a comprehensive platform for various complex e-service systems based on the sustainable model of AST. Our research focused on the complex e-healthcare system in Sweden, and the results showed that the model of AST can provide the designers of e-service system with a comprehensive and sustainable platform for designing various kinds of e-service systems.

An emerging technology-wearable wireless sensor networks with applications in human health condition monitoring

Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task,which involves anatomy,artificial intelligence,and sensing and computing networks.A wearable wireless sensor network(WWSN)turns into an emerging technology,which is capable of acquiring dynamic data related to a human body’s physiological conditions.The collected data can be applied to detect anomalies in a patient,so that he or she can receive an early alert about the adverse trend of the health condition,and doctors can take preventive actions accordingly.In this paper,a new WWSN for anomaly detections of health conditions has been proposed,system architecture and network has been discussed,the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN.The novelty of the detected model lies in its relevance to chronobiology.Anomalies of health conditions are contextual and assessed not only based on the time and spatial correlation of the collected data,but also based on mutual relations of the data streams from different sources of sensors.A new algorithm is proposed to identify anomalies using the following procedure:(1)collected raw data is preprocessed and transferred into a set of directed graphs to represent the correlations of data streams from different sensors;(2)the directed graphs are further analyzed to identify dissimilarities and frequency patterns;(3)health conditions are quantified by a coefficient number,which depends on the identified dissimilarities and patterns.The effectiveness and reliability of the proposed WWSN has been validated by experiments in detecting health anomalies including tachycardia,arrhythmia and myocardial infarction.

Bio-inspired cryptosystem on the reciprocal domain:DNA strands mutate to secure health data

Healthcare and telemedicine industries are relying on technology that is connected to the Internet.Digital health data are more prone to cyber attacks because of the treasure trove of personal data they possess.This necessitates protection of digital medical images and their secure transmission.In this paper,an encryption technique based on DNA mutated with Lorenz and Lüchaotic attractors is employed to generate high pseudo-random key streams.The proposed chaos-DNA cryptic system operates on the integer wavelet transform(IWT)domain and a bio-inspired crossover,mutation unit for enhancing the confusion and diffusion phase in an approximation coefficient.Finally,an XOR operation is performed with a quantised chaotic set from the developed combined attractors.The algorithm attains an average entropy of 7.9973,near-zero correlation with an NPCR of 99.642%,a UACI of 33.438%,and a keyspace of 10~(203).Further,the experimental analyses and NIST statistical test suite have been designed such that the proposed medical image encryption technique has the potency to withstand any statistical,differential,and brute force attacks.