System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significan...System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.展开更多
Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteris...Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in healthcare.The proposed study has superior performance and effective results.展开更多
The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clin...The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.展开更多
Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magneti...Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magnetic resonance imaging(MRI)is the leading modality used for the diagnosis of AD.Deep learning based approaches have produced impressive results in this domain.The early diagnosis of AD depends on the efficient use of classification approach.To address this issue,this study proposes a system using two convolutional neural networks(CNN)based approaches for an early diagnosis of AD automatically.In the proposed system,we use segmented MRI scans.Input data samples of three classes include 110 normal control(NC),110 mild cognitive impairment(MCI)and 105 AD subjects are used in this paper.The data is acquired from the ADNI database and gray matter(GM)images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models.The proposed approaches segregate among NC,MCI,and AD.While testing both methods applied on the segmented data samples,the highest performance results of the classification in terms of accuracy on NC vs.AD are 95.33%and 89.87%,respectively.The proposed methods distinguish between NC vs.MCI and MCI vs.AD patients with a classification accuracy of 90.74%and 86.69%.The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.展开更多
The recent unprecedented threat from COVID-19 and past epidemics,such as SARS,AIDS,and Ebola,has affected millions of people in multiple countries.Countries have shut their borders,and their nationals have been advise...The recent unprecedented threat from COVID-19 and past epidemics,such as SARS,AIDS,and Ebola,has affected millions of people in multiple countries.Countries have shut their borders,and their nationals have been advised to self-quarantine.The variety of responses to the pandemic has given rise to data privacy concerns.Infection prevention and control strategies as well as disease control measures,especially real-time contact tracing for COVID-19,require the identification of people exposed to COVID-19.Such tracing frameworks use mobile apps and geolocations to trace individuals.However,while the motive may be well intended,the limitations and security issues associated with using such a technology are a serious cause of concern.There are growing concerns regarding the privacy of an individual’s location and personal identifiable information(PII)being shared with governments and/or health agencies.This study presents a real-time,trust-based contact-tracing framework that operateswithout the use of an individual’sPII,location sensing,or gathering GPS logs.The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range.The research validates the trust-based framework using Bluetooth as practical and privacy-aware.Using our proposed methodology,personal information,health logs,and location data will be secure and not abused.This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.展开更多
The outbreak of the novel coronavirus has spread worldwide,and millions of people are being infected.Image or detection classification is one of the first application areas of deep learning,which has a significant con...The outbreak of the novel coronavirus has spread worldwide,and millions of people are being infected.Image or detection classification is one of the first application areas of deep learning,which has a significant contribution to medical image analysis.In classification detection,one or more images(detection)are usually used as input,and diagnostic variables(such as whether there is a disease)are used as output.The novel coronavirus has spread across the world,infecting millions of people.Early-stage detection of critical cases of COVID-19 is essential.X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early.For extracting the discriminative features through these modalities,deep convolutional neural networks(CNNs)are used.A siamese convolutional neural network model(COVID-3D-SCNN)is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans.To extract the useful features,we used three consecutive models working in parallel in the proposed approach.We acquired 575 COVID-19,1200 non-COVID,and 1400 pneumonia images,which are publicly available.In our framework,augmentation is used to enlarge the dataset.The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%,specificity 95.55%,and sensitivity 96.62%over(COVID-19 vs.non-COVID19 vs.Pneumonia).展开更多
基金funded by the Researchers Supporting Project No.(RSP.2021/102)King Saud University,Riyadh,Saudi ArabiaThis work was supported in part by the National Natural Science Foundation of China under Grant 61802030+2 种基金Natural Science Foundation of Hunan Province under Grant 2020JJ5602the Research Foundation of Education Bureau of Hunan Province under Grant 19B005the International Cooperative Project for“Double First-Class”,CSUST under Grant 2018IC24.
文摘System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.
基金This work was supported by the Institute for Social and Economic Research(ISER),Zayed University,Under Policy Research Incentive Plan,2017。
文摘Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in healthcare.The proposed study has superior performance and effective results.
基金supported in part by Zayed University,office of research under Grant No.R17089.
文摘The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.
基金supported by the Researchers Supporting Project(No.RSP-2021/395),King Saud University,Riyadh,Saudi Arabia.
文摘Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magnetic resonance imaging(MRI)is the leading modality used for the diagnosis of AD.Deep learning based approaches have produced impressive results in this domain.The early diagnosis of AD depends on the efficient use of classification approach.To address this issue,this study proposes a system using two convolutional neural networks(CNN)based approaches for an early diagnosis of AD automatically.In the proposed system,we use segmented MRI scans.Input data samples of three classes include 110 normal control(NC),110 mild cognitive impairment(MCI)and 105 AD subjects are used in this paper.The data is acquired from the ADNI database and gray matter(GM)images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models.The proposed approaches segregate among NC,MCI,and AD.While testing both methods applied on the segmented data samples,the highest performance results of the classification in terms of accuracy on NC vs.AD are 95.33%and 89.87%,respectively.The proposed methods distinguish between NC vs.MCI and MCI vs.AD patients with a classification accuracy of 90.74%and 86.69%.The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.
基金The author would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2021-131.
文摘The recent unprecedented threat from COVID-19 and past epidemics,such as SARS,AIDS,and Ebola,has affected millions of people in multiple countries.Countries have shut their borders,and their nationals have been advised to self-quarantine.The variety of responses to the pandemic has given rise to data privacy concerns.Infection prevention and control strategies as well as disease control measures,especially real-time contact tracing for COVID-19,require the identification of people exposed to COVID-19.Such tracing frameworks use mobile apps and geolocations to trace individuals.However,while the motive may be well intended,the limitations and security issues associated with using such a technology are a serious cause of concern.There are growing concerns regarding the privacy of an individual’s location and personal identifiable information(PII)being shared with governments and/or health agencies.This study presents a real-time,trust-based contact-tracing framework that operateswithout the use of an individual’sPII,location sensing,or gathering GPS logs.The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range.The research validates the trust-based framework using Bluetooth as practical and privacy-aware.Using our proposed methodology,personal information,health logs,and location data will be secure and not abused.This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.
基金This work was supported by the Researchers Supporting Project(No.RSP-2021/395),King Saud University,Riyadh,Saudi Arabia.
文摘The outbreak of the novel coronavirus has spread worldwide,and millions of people are being infected.Image or detection classification is one of the first application areas of deep learning,which has a significant contribution to medical image analysis.In classification detection,one or more images(detection)are usually used as input,and diagnostic variables(such as whether there is a disease)are used as output.The novel coronavirus has spread across the world,infecting millions of people.Early-stage detection of critical cases of COVID-19 is essential.X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early.For extracting the discriminative features through these modalities,deep convolutional neural networks(CNNs)are used.A siamese convolutional neural network model(COVID-3D-SCNN)is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans.To extract the useful features,we used three consecutive models working in parallel in the proposed approach.We acquired 575 COVID-19,1200 non-COVID,and 1400 pneumonia images,which are publicly available.In our framework,augmentation is used to enlarge the dataset.The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%,specificity 95.55%,and sensitivity 96.62%over(COVID-19 vs.non-COVID19 vs.Pneumonia).
