Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)networks.However,several security vulnerabil...Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)networks.However,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G networks.In this regard,UAVs have appeared as a winning candidate to mitigate security risks.In this paper,we leverage UAVs to propose two methods.The first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver devices.Moreover,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,i.e.increasing secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF relay.Additionally,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation distance.Lastly,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.展开更多
Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is crucial.Medical physicians’time is limited in outdoor situations due to many pati...Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is crucial.Medical physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a rescue.The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’experience.Therefore,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays.In medical classifications,deep convolution neural networks are commonly used.This research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and Pneumonia.The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models:Mobile-Net,DenseNet-201,EfficientNet-B0,and VGG-16,which have been finetuned and trained on 5,856 CXR images.The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and f1-score.The model effectively decreases training loss while increasing accuracy.The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%,an accuracy of 92.15%,a recall of 90.90%,an auc-roc score of 90.9%,and f-score of 91.49%with minimal data pre-processing,data augmentation,finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.展开更多
Spam mail classification considered complex and error-prone task in the distributed computing environment.There are various available spam mail classification approaches such as the naive Bayesian classifier,logistic ...Spam mail classification considered complex and error-prone task in the distributed computing environment.There are various available spam mail classification approaches such as the naive Bayesian classifier,logistic regression and support vector machine and decision tree,recursive neural network,and long short-term memory algorithms.However,they do not consider the document when analyzing spam mail content.These approaches use the bagof-words method,which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document frequency.Because there are many words in a document,these approaches consume a massive amount of resources and become infeasible when performing classification on multiple associated mail documents together.Thus,spam mail is not classified fully,and these approaches remain with loopholes.Thus,we propose a term frequency topic inverse document frequency model that considers the meaning of text data in a larger semantic unit by applying weights based on the document’s topic.Moreover,the proposed approach reduces the scarcity problem through a frequency topic-inverse document frequency in singular value decomposition model.Our proposed approach also reduces the dimensionality,which ultimately increases the strength of document classification.Experimental evaluations show that the proposed approach classifies spam mail documents with higher accuracy using individual document-independent processing computation.Comparative evaluations show that the proposed approach performs better than the logistic regression model in the distributed computing environment,with higher document word frequencies of 97.05%,99.17%and 96.59%.展开更多
With the advent of the big data era,security issues in the context of artificial intelligence(AI)and data analysis are attracting research attention.In the metaverse,which will become a virtual asset in the future,us...With the advent of the big data era,security issues in the context of artificial intelligence(AI)and data analysis are attracting research attention.In the metaverse,which will become a virtual asset in the future,users’communication,movement with characters,text elements,etc.,are required to integrate the real and virtual.However,they can be exposed to threats.Particularly,various hacker threats exist.For example,users’assets are exposed through notices and mail alerts regularly sent to users by operators.In the future,hacker threats will increase mainly due to naturally anonymous texts.Therefore,it is necessary to use the natural language processing technology of artificial intelligence,especially term frequency-inverse document frequency,word2vec,gated recurrent unit,recurrent neural network,and long-short term memory.Additionally,several application versions are used.Currently,research on tasks and performance for algorithm application is underway.We propose a grouping algorithm that focuses on securing various bridgehead strategies to secure topics for security and safety within the metaverse.The algorithm comprises three modules:extracting topics from attacks,managing dimensions,and performing grouping.Consequently,we create 24 topic-based models.Assuming normal and spam mail attacks to verify our algorithm,the accuracy of the previous application version was increased by∼0.4%-1.5%.展开更多
have been focused on addressing the Covid-19 pandemic;for example,governments have implemented countermeasures,such as quarantining,pushing vaccine shots to minimize local spread,investigating and analyzing the virus...have been focused on addressing the Covid-19 pandemic;for example,governments have implemented countermeasures,such as quarantining,pushing vaccine shots to minimize local spread,investigating and analyzing the virus’characteristics,and conducting epidemiological investigations through patient management and tracers.Therefore,researchers worldwide require funding to achieve these goals.Furthermore,there is a need for documentation to investigate and trace disease characteristics.However,it is time consuming and resource intensive to work with documents comprising many types of unstructured data.Therefore,in this study,natural language processing technology is used to automatically classify these documents.Currently used statistical methods include data cleansing,query modification,sentiment analysis,and clustering.However,owing to limitations with respect to the data,it is necessary to understand how to perform data analysis suitable for medical documents.To solve this problem,this study proposes a robust in-depth mixed with subject and emotion model comprising three modules.The first is a subject and non-linear emotional module,which extracts topics from the data and supplements them with emotional figures.The second is a subject with singular value decomposition in the emotion model,which is a dimensional decomposition module that uses subject analysis and an emotion model.The third involves embedding with singular value decomposition using an emotion module,which is a dimensional decomposition method that uses emotion learning.The accuracy and other model measurements,such as the F1,area under the curve,and recall are evaluated based on an article on Middle East respiratory syndrome.A high F1 score of approximately 91%is achieved.The proposed joint analysis method is expected to provide a better synergistic effect in the dataset.展开更多
The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), th...The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.展开更多
文摘Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)networks.However,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G networks.In this regard,UAVs have appeared as a winning candidate to mitigate security risks.In this paper,we leverage UAVs to propose two methods.The first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver devices.Moreover,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,i.e.increasing secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF relay.Additionally,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation distance.Lastly,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A1A01052299).
文摘Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is crucial.Medical physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a rescue.The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’experience.Therefore,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays.In medical classifications,deep convolution neural networks are commonly used.This research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and Pneumonia.The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models:Mobile-Net,DenseNet-201,EfficientNet-B0,and VGG-16,which have been finetuned and trained on 5,856 CXR images.The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and f1-score.The model effectively decreases training loss while increasing accuracy.The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%,an accuracy of 92.15%,a recall of 90.90%,an auc-roc score of 90.9%,and f-score of 91.49%with minimal data pre-processing,data augmentation,finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.
文摘Spam mail classification considered complex and error-prone task in the distributed computing environment.There are various available spam mail classification approaches such as the naive Bayesian classifier,logistic regression and support vector machine and decision tree,recursive neural network,and long short-term memory algorithms.However,they do not consider the document when analyzing spam mail content.These approaches use the bagof-words method,which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document frequency.Because there are many words in a document,these approaches consume a massive amount of resources and become infeasible when performing classification on multiple associated mail documents together.Thus,spam mail is not classified fully,and these approaches remain with loopholes.Thus,we propose a term frequency topic inverse document frequency model that considers the meaning of text data in a larger semantic unit by applying weights based on the document’s topic.Moreover,the proposed approach reduces the scarcity problem through a frequency topic-inverse document frequency in singular value decomposition model.Our proposed approach also reduces the dimensionality,which ultimately increases the strength of document classification.Experimental evaluations show that the proposed approach classifies spam mail documents with higher accuracy using individual document-independent processing computation.Comparative evaluations show that the proposed approach performs better than the logistic regression model in the distributed computing environment,with higher document word frequencies of 97.05%,99.17%and 96.59%.
基金This work was supported by the BK21 FOUR Project.W.H.P received the grant。
文摘With the advent of the big data era,security issues in the context of artificial intelligence(AI)and data analysis are attracting research attention.In the metaverse,which will become a virtual asset in the future,users’communication,movement with characters,text elements,etc.,are required to integrate the real and virtual.However,they can be exposed to threats.Particularly,various hacker threats exist.For example,users’assets are exposed through notices and mail alerts regularly sent to users by operators.In the future,hacker threats will increase mainly due to naturally anonymous texts.Therefore,it is necessary to use the natural language processing technology of artificial intelligence,especially term frequency-inverse document frequency,word2vec,gated recurrent unit,recurrent neural network,and long-short term memory.Additionally,several application versions are used.Currently,research on tasks and performance for algorithm application is underway.We propose a grouping algorithm that focuses on securing various bridgehead strategies to secure topics for security and safety within the metaverse.The algorithm comprises three modules:extracting topics from attacks,managing dimensions,and performing grouping.Consequently,we create 24 topic-based models.Assuming normal and spam mail attacks to verify our algorithm,the accuracy of the previous application version was increased by∼0.4%-1.5%.
文摘have been focused on addressing the Covid-19 pandemic;for example,governments have implemented countermeasures,such as quarantining,pushing vaccine shots to minimize local spread,investigating and analyzing the virus’characteristics,and conducting epidemiological investigations through patient management and tracers.Therefore,researchers worldwide require funding to achieve these goals.Furthermore,there is a need for documentation to investigate and trace disease characteristics.However,it is time consuming and resource intensive to work with documents comprising many types of unstructured data.Therefore,in this study,natural language processing technology is used to automatically classify these documents.Currently used statistical methods include data cleansing,query modification,sentiment analysis,and clustering.However,owing to limitations with respect to the data,it is necessary to understand how to perform data analysis suitable for medical documents.To solve this problem,this study proposes a robust in-depth mixed with subject and emotion model comprising three modules.The first is a subject and non-linear emotional module,which extracts topics from the data and supplements them with emotional figures.The second is a subject with singular value decomposition in the emotion model,which is a dimensional decomposition module that uses subject analysis and an emotion model.The third involves embedding with singular value decomposition using an emotion module,which is a dimensional decomposition method that uses emotion learning.The accuracy and other model measurements,such as the F1,area under the curve,and recall are evaluated based on an article on Middle East respiratory syndrome.A high F1 score of approximately 91%is achieved.The proposed joint analysis method is expected to provide a better synergistic effect in the dataset.
文摘The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.
