The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promisi...The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication performance.It brings on-demand services proximate to the end devices and delivers the requested data in a short time.Fog computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end devices.To this end,fog caching plays an imperative role in addressing data dissemination issues.This study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog computing.Moreover,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing.Besides,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog computing.We also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.展开更多
Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriat...Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate diagnosis.Machine learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis.Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published.Hence,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this study.To carry out the review,six different databases are selected.Inclusion and exclusion criteria are employed to limit the research.Further,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and results.Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis.The ndings of this study show the most used ML methods and the most common diseases that are focused on by researchers.It also shows the increase in use of machine learning for disease diagnosis over the years.These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.展开更多
Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripp...Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field data.In the same vein,smaller lengths of magnetic field data decrease the localization accuracy substantially.The current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user.Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this purpose.Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG Q6.Experiments are performed during different times of the day to analyze the impact of time variability.Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy.Performance comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches.展开更多
The COVID-19 pandemic has caused hundreds of thousands of deaths,millions of infections worldwide,and the loss of trillions of dollars for many large economies.It poses a grave threat to the human population with an e...The COVID-19 pandemic has caused hundreds of thousands of deaths,millions of infections worldwide,and the loss of trillions of dollars for many large economies.It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope.Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses.In the same vein,a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of,and role of age groups and gender on,the probability of COVID-19 infection.This study aimed to review,analyze,and critically appraise published works that report on various factors to explain their relationship with COVID-19.Such studies span a wide range,including descriptive analyses,ratio analyses,cohort,prospective and retrospective studies.Various studies that describe indicators to determine the probability of infection among the general population,as well as the risk factors associated with severe illness and mortality,are critically analyzed and these ndings are discussed in detail.A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19.Studies incorporating important demographic,health,and socioeconomic characteristics are highlighted to emphasize their importance.Predominantly,the lack of an appropriated dataset that contains demographic,personal health,and socioeconomic information implicates the efcacy and efciency of the discussed methods.Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill.展开更多
基金Provincial key platforms and major scientific research projects of universities in Guangdong Province,Peoples R China under Grant No.2017GXJK116.
文摘The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication performance.It brings on-demand services proximate to the end devices and delivers the requested data in a short time.Fog computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end devices.To this end,fog caching plays an imperative role in addressing data dissemination issues.This study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog computing.Moreover,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing.Besides,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog computing.We also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.
基金supported in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2016-0-00312)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)in part by the MSIP(Ministry of Science,ICT&Future Planning),Korea,under the National Program for Excellence in SW(2015-0-00938)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate diagnosis.Machine learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis.Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published.Hence,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this study.To carry out the review,six different databases are selected.Inclusion and exclusion criteria are employed to limit the research.Further,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and results.Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis.The ndings of this study show the most used ML methods and the most common diseases that are focused on by researchers.It also shows the increase in use of machine learning for disease diagnosis over the years.These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2016-0-00313)supervised by the IITP(Institute for Information&communication Technology Promotion)+1 种基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT and Future Planning(2017R1E1A1A01074345).
文摘Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field data.In the same vein,smaller lengths of magnetic field data decrease the localization accuracy substantially.The current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user.Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this purpose.Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG Q6.Experiments are performed during different times of the day to analyze the impact of time variability.Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy.Performance comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches.
基金supported by the Researchers Supporting Project Number(RSP-2020/250),King Saud University,Riyadh,Saudi Arabia.
文摘The COVID-19 pandemic has caused hundreds of thousands of deaths,millions of infections worldwide,and the loss of trillions of dollars for many large economies.It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope.Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses.In the same vein,a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of,and role of age groups and gender on,the probability of COVID-19 infection.This study aimed to review,analyze,and critically appraise published works that report on various factors to explain their relationship with COVID-19.Such studies span a wide range,including descriptive analyses,ratio analyses,cohort,prospective and retrospective studies.Various studies that describe indicators to determine the probability of infection among the general population,as well as the risk factors associated with severe illness and mortality,are critically analyzed and these ndings are discussed in detail.A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19.Studies incorporating important demographic,health,and socioeconomic characteristics are highlighted to emphasize their importance.Predominantly,the lack of an appropriated dataset that contains demographic,personal health,and socioeconomic information implicates the efcacy and efciency of the discussed methods.Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill.