Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,w...Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.展开更多
The development of highly efficient OER catalysts with superior durability for seawater electrolysis and Zn-air battery is important but challenging.Herein,the vacancy-modified heterostructured bimetallic Fe Mo S_(x)/...The development of highly efficient OER catalysts with superior durability for seawater electrolysis and Zn-air battery is important but challenging.Herein,the vacancy-modified heterostructured bimetallic Fe Mo S_(x)/Co Ni P_(x)OER electrocatalyst is exploited.Benefiting from the electron redistribution and reaction kinetics modulation resulting from vacancy introduction and heterojunction formation,it yields ultralow OER overpotentials of 196,276,303 m V in 1 M KOH and 197,318,348 m V in 1 M KOH+seawater at 10,500,1000 m A cm^(-2),respectively,surviving 600 h at 800 m A cm^(-2)without obvious decay.Further,FeMoS_(x)/CoNiP_(x)-based Zn-air battery not only affords the high peak power density of 214.5 m W cm^(-2)but also exhibits the small voltage gap of 0.698 V and long lifetime of 500 h at 10 m A cm^(-2),overmatching overwhelming majority of reported advanced catalysts.It is revealed experimentally that the OER process on rationally designed Fe Mo S_(x)/Co Ni P_(x)follows the adsorbate evolution mechanism and the ratedetermining step shifts from^(*)OOH formation in individual building blocks to^(*)OOH deprotonation process in FeMoS_(x)/CoNiP_(x),providing the directly proof of how the vacancy introduction and heterojunction formation affect the reaction kinetics.展开更多
The anonymity of the darknet makes it attractive to secure communication lines from censorship.The analysis,monitoring,and categorization of Internet network traffic are essential for detecting darknet traffic that ca...The anonymity of the darknet makes it attractive to secure communication lines from censorship.The analysis,monitoring,and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime.Furthermore,classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur.This paper presents a two-stage deep network chain for detecting and classifying darknet traffic.In the first stage,anonymized darknet traffic,including VPN and Tor traffic related to hidden services provided by darknets,is detected.In the second stage,traffic related to VPNs and Tor services is classified based on their respective applications.The methodology of this paper was verified on a benchmark dataset containing VPN and Tor traffic.It achieved an accuracy of 96.8%and 94.4%in the detection and classification stages,respectively.Optimization and parameter tuning were performed in both stages to achieve more accurate results,enabling practitioners to combat alleged malicious activities and further detect such activities after outbreaks.In the classification stage,it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time protocols.However,in cases where it is desired to distinguish between such activities accurately,the presented deep chain classifier can accommodate additional classifiers.Furthermore,additional classifiers could be added to the chain to categorize specific activities of interest further.展开更多
基金via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.
基金supported by the National Natural Science Foundation of China (21975136,22102076)the Fundamental Research Funds for the Central Universities (63185015)+2 种基金the Shenzhen Science,Technology and Innovation Committee (JCYJ20190808151603654,JCYJ20210324121002007)the Open Funds from National Engineering Lab for Mobile Source Emission Control Technology (NELMS2020A12)the Open Fund for Key Laboratory of Civil Aviation Thermal Hazards Prevention and Emergency Response (RZH2021-KF-03)。
文摘The development of highly efficient OER catalysts with superior durability for seawater electrolysis and Zn-air battery is important but challenging.Herein,the vacancy-modified heterostructured bimetallic Fe Mo S_(x)/Co Ni P_(x)OER electrocatalyst is exploited.Benefiting from the electron redistribution and reaction kinetics modulation resulting from vacancy introduction and heterojunction formation,it yields ultralow OER overpotentials of 196,276,303 m V in 1 M KOH and 197,318,348 m V in 1 M KOH+seawater at 10,500,1000 m A cm^(-2),respectively,surviving 600 h at 800 m A cm^(-2)without obvious decay.Further,FeMoS_(x)/CoNiP_(x)-based Zn-air battery not only affords the high peak power density of 214.5 m W cm^(-2)but also exhibits the small voltage gap of 0.698 V and long lifetime of 500 h at 10 m A cm^(-2),overmatching overwhelming majority of reported advanced catalysts.It is revealed experimentally that the OER process on rationally designed Fe Mo S_(x)/Co Ni P_(x)follows the adsorbate evolution mechanism and the ratedetermining step shifts from^(*)OOH formation in individual building blocks to^(*)OOH deprotonation process in FeMoS_(x)/CoNiP_(x),providing the directly proof of how the vacancy introduction and heterojunction formation affect the reaction kinetics.
文摘The anonymity of the darknet makes it attractive to secure communication lines from censorship.The analysis,monitoring,and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime.Furthermore,classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur.This paper presents a two-stage deep network chain for detecting and classifying darknet traffic.In the first stage,anonymized darknet traffic,including VPN and Tor traffic related to hidden services provided by darknets,is detected.In the second stage,traffic related to VPNs and Tor services is classified based on their respective applications.The methodology of this paper was verified on a benchmark dataset containing VPN and Tor traffic.It achieved an accuracy of 96.8%and 94.4%in the detection and classification stages,respectively.Optimization and parameter tuning were performed in both stages to achieve more accurate results,enabling practitioners to combat alleged malicious activities and further detect such activities after outbreaks.In the classification stage,it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time protocols.However,in cases where it is desired to distinguish between such activities accurately,the presented deep chain classifier can accommodate additional classifiers.Furthermore,additional classifiers could be added to the chain to categorize specific activities of interest further.
