Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame...Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.展开更多
Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top caus...Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death worldwide.The malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival rates.The study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological images.It uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the lung.In addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and epochs.The proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological images.This study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.展开更多
Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this...Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime analysis.This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.展开更多
Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and conditions.Visual attributes are understandable semantic information to help improve the iss...Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and conditions.Visual attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and occlusions.This paper proposes an end-to-end framework of deep learning for attribute-based person re-id.In the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features.Moreover,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model.The coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of features.The combined features are mapped into the Euclidean space.The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same.Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.展开更多
Genres are one of the key features that categorize music based on specific series of patterns.However,the Arabic music content on the web is poorly defined into its genres,making the automatic classification of Arabic...Genres are one of the key features that categorize music based on specific series of patterns.However,the Arabic music content on the web is poorly defined into its genres,making the automatic classification of Arabic audio genres challenging.For this reason,in this research,our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres,which are:Eastern Takht,Rai,Muwashshah,the poem,and Mawwal,and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks(CNNs)architectures on Arabic music genres classification.In this work,to utilize CNNs to develop a practical classification system,the audio data is transformed into a visual representation(spectrogram)using Short Time Fast Fourier Transformation(STFT),then several audio features are extracted using Mel Frequency Cepstral Coefficients(MFCC).Performance evaluation of classifiers is measured with the accuracy score,time to build,and Matthew’s correlation coefficient(MCC).The concluded results demonstrated that AlexNet is considered among the topperforming five CNNs classifiers studied:LeNet5,AlexNet,VGG,ResNet-50,and LSTM-CNN,with an overall accuracy of 96%.展开更多
The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landm...The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam<sup>(R)</sup> Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data.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
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R234)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.
文摘Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death worldwide.The malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival rates.The study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological images.It uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the lung.In addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and epochs.The proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological images.This study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.
基金This work is supported,in part,by the National Natural Science Foundation of China under grant numbers U1536206,U1405254,61772283,61602253,61672294,61502242In part,by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20150925 and BK20151530+1 种基金In part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundIn part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime analysis.This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.
基金supported by the National Natural Science Foundation of China(6147115461876057)the Fundamental Research Funds for Central Universities(JZ2018YYPY0287)
文摘Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and conditions.Visual attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and occlusions.This paper proposes an end-to-end framework of deep learning for attribute-based person re-id.In the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features.Moreover,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model.The coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of features.The combined features are mapped into the Euclidean space.The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same.Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.
文摘Genres are one of the key features that categorize music based on specific series of patterns.However,the Arabic music content on the web is poorly defined into its genres,making the automatic classification of Arabic audio genres challenging.For this reason,in this research,our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres,which are:Eastern Takht,Rai,Muwashshah,the poem,and Mawwal,and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks(CNNs)architectures on Arabic music genres classification.In this work,to utilize CNNs to develop a practical classification system,the audio data is transformed into a visual representation(spectrogram)using Short Time Fast Fourier Transformation(STFT),then several audio features are extracted using Mel Frequency Cepstral Coefficients(MFCC).Performance evaluation of classifiers is measured with the accuracy score,time to build,and Matthew’s correlation coefficient(MCC).The concluded results demonstrated that AlexNet is considered among the topperforming five CNNs classifiers studied:LeNet5,AlexNet,VGG,ResNet-50,and LSTM-CNN,with an overall accuracy of 96%.
文摘The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam<sup>(R)</sup> Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data.
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.
