<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process...<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process of the virus imply to increase health security (patient and personal health). In this context, healthcare logistics flows require a new and safety organization improving the hospital performance. The purpose of this paper consists in optimizing healthcare logistics flows by solving problems associated to the internal logistics such as reduction of the personal health wasting time and the protection of both patients and personal health. Then, the methodology corresponds to the use of the hospital sustainable digital transformation as a response to healthcare flows and safety problems. Indeed, social, societal and environmental aspects have to be considered in addition to new technologies such as artificial intelligence (AI), Internet of Things (IoTs), Big data and analytics. These parameters could be used in the healthcare for increasing doctor, nurse, caregiver performance during their daily operations, and patient satisfaction. Indeed, this hospital digital transformation requires the use of large data associated to patients and personal health, algorithms, a performance measurement tool (actual and future state) and a general approach for transforming digitally the hospital flows. The paper findings show that the healthcare logistics performance could be improved with a sustainable digital transformation methodology and an intelligent software tool. This paper aims to develop this healthcare logistics 4.0 methodology and to elaborate the intelligent support system. After an introduction presenting the common hospital flows and their main problems, a literature review will be detailed for showing how existing concepts could contribute to the elaboration of a structured methodology. The structure of the intelligent software tool for the healthcare digital transformation and the tool development processes will be presented. An example will be given for illustrating the development of the tool.</span>展开更多
The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca...The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.展开更多
The Quality 4.0 concept is derived from the industrial fourth revolution,i.e.,Industry 4.0.Quality 4.0 is the future of quality,where new digital and disruptive technologies are used to maintain quality in organizatio...The Quality 4.0 concept is derived from the industrial fourth revolution,i.e.,Industry 4.0.Quality 4.0 is the future of quality,where new digital and disruptive technologies are used to maintain quality in organizations.It is also suitable for traditional Chinese medicine(TCM)to maintain quality.This quality revolution aims to improve industrial and service sectors’quality by incorporating emerging technologies to connect physical systems with the natural world.The proposed digital philosophy can update and enhance the entire TCM treatment methodology to become more effective and attractive in the current competitive structure of the pharmaceutical and clinical industries.Thus,in healthcare,this revolution empowers quality treatment during the COVID-19 pandemic.There is a major requirement in healthcare to maintain the quality of medical tools,equipment,and treatment processes during a pandemic.Digital technologies can widely be used to provide innovative products and services with excellent quality for TCM.In this paper,we discuss the significant role of Quality 4.0 and how it can be used to maintain healthcare quality and fulfill challenges during the pandemic.Additionally,we discuss 10 significant applications of Quality 4.0 in healthcare during the COVID-19 pandemic.These technologies will provide unique benefits to maintain the quality of TCM throughout the treatment process.With Quality 4.0,quality can be maintained using innovative and advanced digital technologies.展开更多
在物流4.0以及电子商务时代背景下,仓储配送中心在供应链中扮演着至关重要的角色,其中,订单拣选系统作为仓储作业的核心,备受学术界和工业界的关注。首先,介绍移动机器人(Autonomous Mobile Robots,AMR)订单拣选系统的类型,重点关注AMR-...在物流4.0以及电子商务时代背景下,仓储配送中心在供应链中扮演着至关重要的角色,其中,订单拣选系统作为仓储作业的核心,备受学术界和工业界的关注。首先,介绍移动机器人(Autonomous Mobile Robots,AMR)订单拣选系统的类型,重点关注AMR-assisted系统、AMR-pick系统和移动机器人履行系统的优化内容和优化目标;其次,针对上述三类系统,从布局设计、存储分配、订单分批、任务分配、路径规划以及联合优化等方面综述其研究进展;最后,总结当前研究中存在的不足,并提出若干进一步研究内容。展开更多
Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requi...Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.展开更多
文摘<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process of the virus imply to increase health security (patient and personal health). In this context, healthcare logistics flows require a new and safety organization improving the hospital performance. The purpose of this paper consists in optimizing healthcare logistics flows by solving problems associated to the internal logistics such as reduction of the personal health wasting time and the protection of both patients and personal health. Then, the methodology corresponds to the use of the hospital sustainable digital transformation as a response to healthcare flows and safety problems. Indeed, social, societal and environmental aspects have to be considered in addition to new technologies such as artificial intelligence (AI), Internet of Things (IoTs), Big data and analytics. These parameters could be used in the healthcare for increasing doctor, nurse, caregiver performance during their daily operations, and patient satisfaction. Indeed, this hospital digital transformation requires the use of large data associated to patients and personal health, algorithms, a performance measurement tool (actual and future state) and a general approach for transforming digitally the hospital flows. The paper findings show that the healthcare logistics performance could be improved with a sustainable digital transformation methodology and an intelligent software tool. This paper aims to develop this healthcare logistics 4.0 methodology and to elaborate the intelligent support system. After an introduction presenting the common hospital flows and their main problems, a literature review will be detailed for showing how existing concepts could contribute to the elaboration of a structured methodology. The structure of the intelligent software tool for the healthcare digital transformation and the tool development processes will be presented. An example will be given for illustrating the development of the tool.</span>
基金funded by King Saud University through Researchers Supporting Program Number (RSP2024R499).
文摘The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
文摘The Quality 4.0 concept is derived from the industrial fourth revolution,i.e.,Industry 4.0.Quality 4.0 is the future of quality,where new digital and disruptive technologies are used to maintain quality in organizations.It is also suitable for traditional Chinese medicine(TCM)to maintain quality.This quality revolution aims to improve industrial and service sectors’quality by incorporating emerging technologies to connect physical systems with the natural world.The proposed digital philosophy can update and enhance the entire TCM treatment methodology to become more effective and attractive in the current competitive structure of the pharmaceutical and clinical industries.Thus,in healthcare,this revolution empowers quality treatment during the COVID-19 pandemic.There is a major requirement in healthcare to maintain the quality of medical tools,equipment,and treatment processes during a pandemic.Digital technologies can widely be used to provide innovative products and services with excellent quality for TCM.In this paper,we discuss the significant role of Quality 4.0 and how it can be used to maintain healthcare quality and fulfill challenges during the pandemic.Additionally,we discuss 10 significant applications of Quality 4.0 in healthcare during the COVID-19 pandemic.These technologies will provide unique benefits to maintain the quality of TCM throughout the treatment process.With Quality 4.0,quality can be maintained using innovative and advanced digital technologies.
文摘在物流4.0以及电子商务时代背景下,仓储配送中心在供应链中扮演着至关重要的角色,其中,订单拣选系统作为仓储作业的核心,备受学术界和工业界的关注。首先,介绍移动机器人(Autonomous Mobile Robots,AMR)订单拣选系统的类型,重点关注AMR-assisted系统、AMR-pick系统和移动机器人履行系统的优化内容和优化目标;其次,针对上述三类系统,从布局设计、存储分配、订单分批、任务分配、路径规划以及联合优化等方面综述其研究进展;最后,总结当前研究中存在的不足,并提出若干进一步研究内容。
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS2022-00167197Development of Intelligent 5G/6G Infrastructure Technology for the Smart City)+2 种基金in part by the National Research Foundation of Korea(NRF),Ministry of Education,through Basic Science Research Program under Grant NRF-2020R1I1A3066543in part by BK21 FOUR(Fostering Outstanding Universities for Research)under Grant 5199990914048in part by the Soonchunhyang University Research Fund.
文摘Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.
