Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio...Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.展开更多
The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration ...The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration Systems (LAS). LAS services involve requests and responses concerning public and private cadastral data, including credentials of parties, ownership, and spatial parcels. This study explores the integration of CTI in LAS to enhance cyber resilience, focusing on the unique vulnerabilities of LAS, such as sensitive data management and interconnection with other critical systems related to spatial data uses and changes. The approach employs a case study of a typical country-specific LAS to analyse structured vulnerabilities and their attributes to determine the degree of vulnerability of LAS through a quantitative inductive approach. The analysis results indicate significant improvements in identifying and mitigating potential threats through CTI integration, thus enhancing cyber resilience. These findings are crucial for policymakers and practitioners to develop robust cybersecurity strategies for LAS.展开更多
Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,a...Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.展开更多
This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technol...This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.展开更多
Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective i...Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.展开更多
Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth...Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure.Additionally,local data processing demands substantial manpower and hardware investments.Data isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and research.In this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain technology.This system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative work.The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)format.Then,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic analysis.Then,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services.The Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.展开更多
In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,e...In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.展开更多
AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the f...AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.展开更多
Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review...Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review is to assess and analyze the use of AI and its use in orthopedic practice, as well as its applications, limitations, and pitfalls. Methods: A review of all relevant databases such as EMBASE, Cochrane Database of Systematic Reviews, MEDLINE, Science Citation Index, Scopus, and Web of Science with keywords of AI, orthopedic surgery, applications, and drawbacks. All related articles on AI and orthopaedic practice were reviewed. A total of 3210 articles were included in the review. Results: The data from 351 studies were analyzed where in orthopedic surgery. AI is being used for diagnostic procedures, radiological diagnosis, models of clinical care, and utilization of hospital and bed resources. AI has also taken a chunk of share in assisted robotic orthopaedic surgery. Conclusions: AI has now become part of the orthopedic practice and will further increase its stake in the healthcare industry. Nonetheless, clinicians should remain aware of AI’s serious limitations and pitfalls and consider the drawbacks and errors in its use.展开更多
While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),...While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.展开更多
The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning o...The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.展开更多
Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and ...Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and governments.Smart and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless communication.One of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake identity.To figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link.We consider the communication link between vehicle-to-vehicle,and vehicle-to-infrastructure.We evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning algorithm.The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.展开更多
As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crud...As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.展开更多
Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utili...Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.展开更多
Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteris...Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.展开更多
The swift recuperation of communities following natural hazards heavily relies on the efficiency of transporta-tion systems,facilitating the timely delivery of vital resources and manpower to reconstruction sites.This...The swift recuperation of communities following natural hazards heavily relies on the efficiency of transporta-tion systems,facilitating the timely delivery of vital resources and manpower to reconstruction sites.This paper delves into the pivotal role of transportation systems in aiding the recovery of built environments,proposing an evaluative metric that correlates transportation capacity with the speed of post-earthquake recovery.Focusing on optimizing urban population capacity in the aftermath of earthquakes,the study comprehensively examines the impact of pre-earthquake measures such as enhancing building or bridge seismic performance on post-earthquake urban population capacity.The methodology is demonstrated through an analysis of Beijing’s transportation sys-tem,elucidating how enhancements to transportation infrastructure fortify the resilience of built environments.Additionally,the concept of a resource supply rate is introduced to gauge the level of logistical support available after an earthquake.This rate tends to decrease when transportation damage is significant or when the demands for repairs overwhelm available resources,indicating a need for retrofitting.Through sensitivity analysis,this study explores how investments in the built environment or logistical systems can increase the resource supply rate,thereby contributing to more resilient urban areas in the face of seismic challenges.展开更多
Considerable changes have taken place in commercial passenger air transport since the enactment of the 1978 Airline Deregulation Act in the US and the deregulation of airline networks that has occurred elsewhere. The ...Considerable changes have taken place in commercial passenger air transport since the enactment of the 1978 Airline Deregulation Act in the US and the deregulation of airline networks that has occurred elsewhere. The commercial and operational freedoms have led most of the larger carriers to develop hub-and-spoke networks, within which certain cities or metropolitan areas emerge as key nodes possessing tremendous advantages over other locations in the air transport system. This paper examines the nature of hub-and-spoke operations in air transportation services, and the benefits that accrue to a city or geographical region that is host to an airline hub. In particular, it looks into linkages between the air service hub and local economic development. Four potential types of impact of airports on the regional economy are defined and discussed. As an example, the assessment of the economic impacts of Cincinnati-Northern Kentucky International Airport (CVG), a major Delta Airlines hub, is introduced.展开更多
AIM:To conduct a bibliometric analysis of research on artificial intelligence(AI)in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions fo...AIM:To conduct a bibliometric analysis of research on artificial intelligence(AI)in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies.METHODS:Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved,covering the period from January 1,2013,to December 31,2022.In order to assess the contributions and co-occurrence relationships among different countries/regions,institutions,authors,and journals,CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified.RESULTS:A total of 750 English articles published between 2013 and 2022 were collected,and the number of publications exhibited an overall increasing trend.The majority of the articles were from China,followed by the United States and India.National University of Singapore,Chinese Academy of Sciences,and Sun Yat-sen University made significant contributions to the published works.Weinreb RN and Fu HZ ranked first among authors and cited authors.American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma.The disciplinary scope of this field includes ophthalmology,computer science,mathematics,molecular biology,genetics,and other related disciplines.The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma.Initially,the hot topics in this field were primarily“segmentation”,“classification”and“diagnosis”.However,in recent years,the focus has shifted to“deep learning”,“convolutional neural network”and“artificial intelligence”.CONCLUSION:With the rapid development of AI technology,scholars have shown increasing interest in its application in the field of glaucoma.Moreover,the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot.However,the reliability and interpretability of AI data remain pressing issues that require resolution.展开更多
Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorit...Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.展开更多
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.
文摘The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration Systems (LAS). LAS services involve requests and responses concerning public and private cadastral data, including credentials of parties, ownership, and spatial parcels. This study explores the integration of CTI in LAS to enhance cyber resilience, focusing on the unique vulnerabilities of LAS, such as sensitive data management and interconnection with other critical systems related to spatial data uses and changes. The approach employs a case study of a typical country-specific LAS to analyse structured vulnerabilities and their attributes to determine the degree of vulnerability of LAS through a quantitative inductive approach. The analysis results indicate significant improvements in identifying and mitigating potential threats through CTI integration, thus enhancing cyber resilience. These findings are crucial for policymakers and practitioners to develop robust cybersecurity strategies for LAS.
基金supported by the National Natural Science Foundation of China(No.62404111)Natural Science Foundation of Jiangsu Province(No.BK20240635)+2 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.24KJB510025)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY223157 and NY223156)Opening Project of Advanced Inte-grated Circuit Package and Testing Research Center of Jiangsu Province(No.NTIKFJJ202303).
文摘Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.
基金funded by the Scientific ResearchDeanship at theUniversity ofHa’il-Saudi Arabia through project number-RG-23251.
文摘This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.
文摘Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.
基金supported by the National Natural Science Foundation of China(62122064,62331021,62371410)the Natural Science Foundation of Fujian Province of China(2023J02005 and 2021J011184)+1 种基金the President Fund of Xiamen University(20720220063)the Nanqiang Outstanding Talents Program of Xiamen University.
文摘Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure.Additionally,local data processing demands substantial manpower and hardware investments.Data isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and research.In this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain technology.This system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative work.The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)format.Then,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic analysis.Then,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services.The Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.
基金supported by the National Natural Science Foundation of China(62172033).
文摘In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.
基金supported in part by the Hong Kong Polytechnic University via the project P0038447The Science and Technology Development Fund,Macao SAR(0093/2023/RIA2)The Science and Technology Development Fund,Macao SAR(0145/2023/RIA3).
文摘AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.
文摘Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review is to assess and analyze the use of AI and its use in orthopedic practice, as well as its applications, limitations, and pitfalls. Methods: A review of all relevant databases such as EMBASE, Cochrane Database of Systematic Reviews, MEDLINE, Science Citation Index, Scopus, and Web of Science with keywords of AI, orthopedic surgery, applications, and drawbacks. All related articles on AI and orthopaedic practice were reviewed. A total of 3210 articles were included in the review. Results: The data from 351 studies were analyzed where in orthopedic surgery. AI is being used for diagnostic procedures, radiological diagnosis, models of clinical care, and utilization of hospital and bed resources. AI has also taken a chunk of share in assisted robotic orthopaedic surgery. Conclusions: AI has now become part of the orthopedic practice and will further increase its stake in the healthcare industry. Nonetheless, clinicians should remain aware of AI’s serious limitations and pitfalls and consider the drawbacks and errors in its use.
文摘While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.
基金supported by Systematic Major Project of China State Railway Group Corporation Limited(Grant Number:P2023W002).
文摘The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.
基金This work was partially supported by The China’s National Key R&D Program(No.2018YFB0803600)Natural Science Foundation of China(No.61801008)+2 种基金Beijing Natural Science Foundation National(No.L172049)Scientific Research Common Program of Beijing Municipal Commission of Education(No.KM201910005025)Defense Industrial Technology Development Program(No.JCKY2016204A102)sponsored this research in parts.
文摘Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and governments.Smart and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless communication.One of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake identity.To figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link.We consider the communication link between vehicle-to-vehicle,and vehicle-to-infrastructure.We evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning algorithm.The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.
基金This work was financially supported by the National Natural Science Foundation of China(52074089 and 52104064)Natural Science Foundation of Heilongjiang Province of China(LH2019E019).
文摘As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.
文摘Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.
文摘Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.
基金partially supported by National Natural Science Foundation of China(Grant No.62088101)supported by the Australian Government through the Australian Research Coun-cil’s Discovery Early Career Researcher Award(DE240100207).
文摘The swift recuperation of communities following natural hazards heavily relies on the efficiency of transporta-tion systems,facilitating the timely delivery of vital resources and manpower to reconstruction sites.This paper delves into the pivotal role of transportation systems in aiding the recovery of built environments,proposing an evaluative metric that correlates transportation capacity with the speed of post-earthquake recovery.Focusing on optimizing urban population capacity in the aftermath of earthquakes,the study comprehensively examines the impact of pre-earthquake measures such as enhancing building or bridge seismic performance on post-earthquake urban population capacity.The methodology is demonstrated through an analysis of Beijing’s transportation sys-tem,elucidating how enhancements to transportation infrastructure fortify the resilience of built environments.Additionally,the concept of a resource supply rate is introduced to gauge the level of logistical support available after an earthquake.This rate tends to decrease when transportation damage is significant or when the demands for repairs overwhelm available resources,indicating a need for retrofitting.Through sensitivity analysis,this study explores how investments in the built environment or logistical systems can increase the resource supply rate,thereby contributing to more resilient urban areas in the face of seismic challenges.
基金Under the auspices of Intramural Research Incentive Grant from the Office of the Senior Vice President for Research atUniversity of Louisville
文摘Considerable changes have taken place in commercial passenger air transport since the enactment of the 1978 Airline Deregulation Act in the US and the deregulation of airline networks that has occurred elsewhere. The commercial and operational freedoms have led most of the larger carriers to develop hub-and-spoke networks, within which certain cities or metropolitan areas emerge as key nodes possessing tremendous advantages over other locations in the air transport system. This paper examines the nature of hub-and-spoke operations in air transportation services, and the benefits that accrue to a city or geographical region that is host to an airline hub. In particular, it looks into linkages between the air service hub and local economic development. Four potential types of impact of airports on the regional economy are defined and discussed. As an example, the assessment of the economic impacts of Cincinnati-Northern Kentucky International Airport (CVG), a major Delta Airlines hub, is introduced.
基金Supported by National Natural Science Foundation of China(No.82074335).
文摘AIM:To conduct a bibliometric analysis of research on artificial intelligence(AI)in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies.METHODS:Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved,covering the period from January 1,2013,to December 31,2022.In order to assess the contributions and co-occurrence relationships among different countries/regions,institutions,authors,and journals,CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified.RESULTS:A total of 750 English articles published between 2013 and 2022 were collected,and the number of publications exhibited an overall increasing trend.The majority of the articles were from China,followed by the United States and India.National University of Singapore,Chinese Academy of Sciences,and Sun Yat-sen University made significant contributions to the published works.Weinreb RN and Fu HZ ranked first among authors and cited authors.American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma.The disciplinary scope of this field includes ophthalmology,computer science,mathematics,molecular biology,genetics,and other related disciplines.The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma.Initially,the hot topics in this field were primarily“segmentation”,“classification”and“diagnosis”.However,in recent years,the focus has shifted to“deep learning”,“convolutional neural network”and“artificial intelligence”.CONCLUSION:With the rapid development of AI technology,scholars have shown increasing interest in its application in the field of glaucoma.Moreover,the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot.However,the reliability and interpretability of AI data remain pressing issues that require resolution.
文摘Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.
