Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intr...Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.展开更多
In machine learning and statistics, classification is the a new observation belongs, on the basis of a training set of data problem of identifying to which of a set of categories （sub-populations） containing observa...In machine learning and statistics, classification is the a new observation belongs, on the basis of a training set of data problem of identifying to which of a set of categories （sub-populations） containing observations （or instances） whose category membership is known. SVM （support vector machines） are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes fon^as the output, making it a non-probabilistic binary linear classifier. In pattern recognition problem, the selection of the features used for characterization an object to be classified is importance. Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, impticitly perform a nonlinear mapping 4~ of the input data in Rainto a high-dimensional feature space H. Cover＇s theorem states that if the transformation is nonlinear and the dimensionality of the feature space is high enough, then the input space may be transformed into a new feature space where the patterns are linearly separable with high probability.展开更多
Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods...Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.展开更多
Problem:Chest radiography is a crucial tool for diagnosing thoracic disorders,but interpretation errors and a lack of qualified practitioners can cause delays in treatment.Aim:This study aimed to develop a reliable mu...Problem:Chest radiography is a crucial tool for diagnosing thoracic disorders,but interpretation errors and a lack of qualified practitioners can cause delays in treatment.Aim:This study aimed to develop a reliable multi-classification artificial intelligence(AI)tool to improve the accuracy and efficiency of chest radiograph diagnosis.Methods:We developed a convolutional neural network(CNN)capable of distinguishing among 26 thoracic diagnoses.The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries.Results:The CNN model achieved an average area under the curve(AUC)of 0.961 across all 26 diagnoses in the testing set.COVID-19 detection achieved perfect accuracy(AUC 1.000,[95%confidence interval{CI},1.000 to 1.000]),while effusion or pleural effusion detection showed the lowest accuracy(AUC 0.8453,[95%CI,0.8417 to 0.8489]).In external validation,the model demonstrated strong reproducibility and generalizability within the local dataset,achieving an AUC of 0.9634 for lung opacity detection(95%CI,0.9423 to 0.9702).The CNN outperformed both radiologists and nonradiological physicians,particularly in trans-device image recognition.Even for diseases not specifically trained on,such as aortic dissection,the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels(all P<0.05).Additionally,our model exhibited no gender bias(P>0.05).Conclusion:The developed AI algorithm,now available as professional web-based software,substantively improves chest radiograph interpretation.This research advances medical imaging and offers substantial diagnostic support in clinical settings.展开更多
Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing proce...Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature;hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is neces-sary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classifica-tion of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented.展开更多
The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convol...The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convolutional Neural Networks (CNNs) in the diagnosis of COVID-19 from chest X-ray and CT images, focusing on the impact of varying learning rates and optimization strategies. Despite the abundance of chest X-ray datasets from various institutions, the lack of a dedicated COVID-19 dataset for computational analysis presents a significant challenge. Our work introduces an empirical analysis across four distinct learning rate policies—Cyclic, Step Based, Time-Based, and Epoch Based—each tested with four different optimizers: Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The performance of these configurations was evaluated in terms of training and validation accuracy over 100 epochs. Our results demonstrate significant differences in model performance, with the Cyclic learning rate policy combined with SGD optimizer achieving the highest validation accuracy of 83.33%. This study contributes to the existing body of knowledge by outlining effective CNN configurations for COVID-19 image dataset analysis, offering insights into the optimization of machine learning models for the diagnosis of infectious diseases. Our findings underscore the potential of CNNs in supplementing traditional PCR tests, providing a computational approach to identify patterns in chest X-rays and CT scans indicative of COVID-19, thereby aiding in the swift and accurate diagnosis of the virus.展开更多
文摘Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.
文摘In machine learning and statistics, classification is the a new observation belongs, on the basis of a training set of data problem of identifying to which of a set of categories （sub-populations） containing observations （or instances） whose category membership is known. SVM （support vector machines） are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes fon^as the output, making it a non-probabilistic binary linear classifier. In pattern recognition problem, the selection of the features used for characterization an object to be classified is importance. Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, impticitly perform a nonlinear mapping 4~ of the input data in Rainto a high-dimensional feature space H. Cover＇s theorem states that if the transformation is nonlinear and the dimensionality of the feature space is high enough, then the input space may be transformed into a new feature space where the patterns are linearly separable with high probability.
基金Ministry of Education,Youth and Sports of the Chezk Republic,Grant/Award Numbers:SP2023/039,SP2023/042the European Union under the REFRESH,Grant/Award Number:CZ.10.03.01/00/22_003/0000048。
文摘Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.
基金supported by the Fundamental Research Funds for the Central Universities(2019PT350005)National Natural Science Foundation of China(nos.81970444 and 82300345)+6 种基金Beijing Municipal Science and Technology Project(Z201100005420030)National high level talents special supportplan(2020-RSW02)CAMS Innovation Fund for MedicalSciences(2021-I2M-1-065)Sanming Project of Medicine in Shenzhen(SZSM202011013)the project for the distinguishing academic discipline of Fuwai Hospital(2022-FWQN16)the National High Level Hospital Clinical Research Funding(2023-GSP-QN-23)the National High Level Hospital Clinical Research Funding(2023-GSP-RC-04).
文摘Problem:Chest radiography is a crucial tool for diagnosing thoracic disorders,but interpretation errors and a lack of qualified practitioners can cause delays in treatment.Aim:This study aimed to develop a reliable multi-classification artificial intelligence(AI)tool to improve the accuracy and efficiency of chest radiograph diagnosis.Methods:We developed a convolutional neural network(CNN)capable of distinguishing among 26 thoracic diagnoses.The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries.Results:The CNN model achieved an average area under the curve(AUC)of 0.961 across all 26 diagnoses in the testing set.COVID-19 detection achieved perfect accuracy(AUC 1.000,[95%confidence interval{CI},1.000 to 1.000]),while effusion or pleural effusion detection showed the lowest accuracy(AUC 0.8453,[95%CI,0.8417 to 0.8489]).In external validation,the model demonstrated strong reproducibility and generalizability within the local dataset,achieving an AUC of 0.9634 for lung opacity detection(95%CI,0.9423 to 0.9702).The CNN outperformed both radiologists and nonradiological physicians,particularly in trans-device image recognition.Even for diseases not specifically trained on,such as aortic dissection,the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels(all P<0.05).Additionally,our model exhibited no gender bias(P>0.05).Conclusion:The developed AI algorithm,now available as professional web-based software,substantively improves chest radiograph interpretation.This research advances medical imaging and offers substantial diagnostic support in clinical settings.
文摘Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature;hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is neces-sary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classifica-tion of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented.
文摘The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convolutional Neural Networks (CNNs) in the diagnosis of COVID-19 from chest X-ray and CT images, focusing on the impact of varying learning rates and optimization strategies. Despite the abundance of chest X-ray datasets from various institutions, the lack of a dedicated COVID-19 dataset for computational analysis presents a significant challenge. Our work introduces an empirical analysis across four distinct learning rate policies—Cyclic, Step Based, Time-Based, and Epoch Based—each tested with four different optimizers: Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The performance of these configurations was evaluated in terms of training and validation accuracy over 100 epochs. Our results demonstrate significant differences in model performance, with the Cyclic learning rate policy combined with SGD optimizer achieving the highest validation accuracy of 83.33%. This study contributes to the existing body of knowledge by outlining effective CNN configurations for COVID-19 image dataset analysis, offering insights into the optimization of machine learning models for the diagnosis of infectious diseases. Our findings underscore the potential of CNNs in supplementing traditional PCR tests, providing a computational approach to identify patterns in chest X-rays and CT scans indicative of COVID-19, thereby aiding in the swift and accurate diagnosis of the virus.
