Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
Dementia is a neurological disorder that affects the brain and its functioning,and women experience its effects more than men do.Preventive care often requires non-invasive and rapid tests,yet conventional diagnostic ...Dementia is a neurological disorder that affects the brain and its functioning,and women experience its effects more than men do.Preventive care often requires non-invasive and rapid tests,yet conventional diagnostic techniques are time-consuming and invasive.One of the most effective ways to diagnose dementia is by analyzing a patient’s speech,which is cheap and does not require surgery.This research aims to determine the effectiveness of deep learning(DL)and machine learning(ML)structures in diagnosing dementia based on women’s speech patterns.The study analyzes data drawn from the Pitt Corpus,which contains 298 dementia files and 238 control files from the Dementia Bank database.Deep learning models and SVM classifiers were used to analyze the available audio samples in the dataset.Our methodology used two methods:a DL-ML model and a single DL model for the classification of diabetics and a single DL model.The deep learning model achieved an astronomic level of accuracy of 99.99%with an F1 score of 0.9998,Precision of 0.9997,and recall of 0.9998.The proposed DL-ML fusion model was equally impressive,with an accuracy of 99.99%,F1 score of 0.9995,Precision of 0.9998,and recall of 0.9997.Also,the study reveals how to apply deep learning and machine learning models for dementia detection from speech with high accuracy and low computational complexity.This research work,therefore,concludes by showing the possibility of using speech-based dementia detection as a possibly helpful early diagnosis mode.For even further enhanced model performance and better generalization,future studies may explore real-time applications and the inclusion of other components of speech.展开更多
The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the ...The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.展开更多
Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies a...Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies among human joints while ignoring the temporal cues and the complex relationships across non-consecutive frames.These limitations hinder the model’s ability to generate accurate predictions over longer time horizons and in scenarios with complex motion patterns.To address the above problems,we proposed a novel multi-level spatial and temporal learning model,which consists of a Cross Spatial Dependencies Encoding Module(CSM)and a Dynamic Temporal Connection Encoding Module(DTM).Specifically,the CSM is designed to capture complementary local and global spatial dependent information at both the joint level and the joint pair level.We further present DTM to encode diverse temporal evolution contexts and compress motion features to a deep level,enabling the model to capture both short-term and long-term dependencies efficiently.Extensive experiments conducted on the Human 3.6M and CMU Mocap datasets demonstrate that our model achieves state-of-the-art performance in both short-term and long-term predictions,outperforming existing methods by up to 20.3% in accuracy.Furthermore,ablation studies confirm the significant contributions of the CSM and DTM in enhancing prediction accuracy.展开更多
Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision.Glaucoma ranks as the second most prevalent cause of permanent blindness.Traditional glaucoma...Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision.Glaucoma ranks as the second most prevalent cause of permanent blindness.Traditional glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait time.For automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio.Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features.Localizing the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease detection.The approach presented in this study is based on deep classifiers for OD segmentation and glaucoma detection.First,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy metrics.The OD region is then fed into the second stage for glaucoma detection.Therefore,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc area.For this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and fine-tuned.We also used the Support Vector Machine Classifier.The feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor Filters.The final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is provided.Classification metrics including accuracy and ROC curve are compared for each classification method.The novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global method.Moreover,the fusion-based decision system uses the glaucoma detection result obtained using several convolutional deep neural networks and the support vector machine classifier.These classification methods contribute to producing robust classification results.This method was evaluated using well-known retinal images available for research work and a combined dataset including retinal images with and without pathology.The performance of the models was tested on two public datasets and a combined dataset and was compared to similar research.The research findings show the potential of this methodology in the early detection of glaucoma,which will reduce diagnosis time and increase detection efficiency.The glaucoma assessment achieves about 98%accuracy in the classification rate,which is close to and even higher than that of state-of-the-art methods.The designed detection model may be used in telemedicine,healthcare,and computer-aided diagnosis systems.展开更多
The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals.Unfortunately,personal air quality measurement remains challenging.In this st...The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals.Unfortunately,personal air quality measurement remains challenging.In this study,we investigate the use of first-person photos for the prediction of air quality.The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction.AirStackNet consists of two layers and four regression models,where the first layer generates meta-data fromLight Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting Regression(XGBoost)and CatBoost Regression(CatBoost),whereas the second layer computes the final prediction from the meta-data of the first layer using Extra Tree Regression(ET).The performance of the proposed AirStackNet model is validated using public Personal Air Quality Dataset(PAQD).Our experiments are evaluated using Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Coefficient of Determination(R2),Mean Squared Error(MSE),Root Mean Squared Logarithmic Error(RMSLE),and Mean Absolute Percentage Error(MAPE).Experimental Results indicate that the proposed AirStackNet model not only can effectively improve air pollution prediction performance by overcoming the Bias-Variance tradeoff,but also outperforms baseline and state of the art models.展开更多
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant No.(RGP-1444-0057).
文摘Dementia is a neurological disorder that affects the brain and its functioning,and women experience its effects more than men do.Preventive care often requires non-invasive and rapid tests,yet conventional diagnostic techniques are time-consuming and invasive.One of the most effective ways to diagnose dementia is by analyzing a patient’s speech,which is cheap and does not require surgery.This research aims to determine the effectiveness of deep learning(DL)and machine learning(ML)structures in diagnosing dementia based on women’s speech patterns.The study analyzes data drawn from the Pitt Corpus,which contains 298 dementia files and 238 control files from the Dementia Bank database.Deep learning models and SVM classifiers were used to analyze the available audio samples in the dataset.Our methodology used two methods:a DL-ML model and a single DL model for the classification of diabetics and a single DL model.The deep learning model achieved an astronomic level of accuracy of 99.99%with an F1 score of 0.9998,Precision of 0.9997,and recall of 0.9998.The proposed DL-ML fusion model was equally impressive,with an accuracy of 99.99%,F1 score of 0.9995,Precision of 0.9998,and recall of 0.9997.Also,the study reveals how to apply deep learning and machine learning models for dementia detection from speech with high accuracy and low computational complexity.This research work,therefore,concludes by showing the possibility of using speech-based dementia detection as a possibly helpful early diagnosis mode.For even further enhanced model performance and better generalization,future studies may explore real-time applications and the inclusion of other components of speech.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Funding Program,Grant No.(FRP-1443-15).
文摘The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.
基金supported by the Urgent Need for Overseas Talent Project of Jiangxi Province(Grant No.20223BCJ25040)the Thousand Talents Plan of Jiangxi Province(Grant No.jxsg2023101085)+3 种基金the National Natural Science Foundation of China(Grant No.62106093)the Natural Science Foundation of Jiangxi(Grant Nos.20224BAB212011,20232BAB212008,20242BAB25078,and 20232BAB202051)The Youth Talent Cultivation Innovation Fund Project of Nanchang University(Grant No.XX202506030015)funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R759),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies among human joints while ignoring the temporal cues and the complex relationships across non-consecutive frames.These limitations hinder the model’s ability to generate accurate predictions over longer time horizons and in scenarios with complex motion patterns.To address the above problems,we proposed a novel multi-level spatial and temporal learning model,which consists of a Cross Spatial Dependencies Encoding Module(CSM)and a Dynamic Temporal Connection Encoding Module(DTM).Specifically,the CSM is designed to capture complementary local and global spatial dependent information at both the joint level and the joint pair level.We further present DTM to encode diverse temporal evolution contexts and compress motion features to a deep level,enabling the model to capture both short-term and long-term dependencies efficiently.Extensive experiments conducted on the Human 3.6M and CMU Mocap datasets demonstrate that our model achieves state-of-the-art performance in both short-term and long-term predictions,outperforming existing methods by up to 20.3% in accuracy.Furthermore,ablation studies confirm the significant contributions of the CSM and DTM in enhancing prediction accuracy.
基金Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding after Publication,Grant No(43-PRFA-P-31).
文摘Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision.Glaucoma ranks as the second most prevalent cause of permanent blindness.Traditional glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait time.For automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio.Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features.Localizing the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease detection.The approach presented in this study is based on deep classifiers for OD segmentation and glaucoma detection.First,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy metrics.The OD region is then fed into the second stage for glaucoma detection.Therefore,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc area.For this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and fine-tuned.We also used the Support Vector Machine Classifier.The feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor Filters.The final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is provided.Classification metrics including accuracy and ROC curve are compared for each classification method.The novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global method.Moreover,the fusion-based decision system uses the glaucoma detection result obtained using several convolutional deep neural networks and the support vector machine classifier.These classification methods contribute to producing robust classification results.This method was evaluated using well-known retinal images available for research work and a combined dataset including retinal images with and without pathology.The performance of the models was tested on two public datasets and a combined dataset and was compared to similar research.The research findings show the potential of this methodology in the early detection of glaucoma,which will reduce diagnosis time and increase detection efficiency.The glaucoma assessment achieves about 98%accuracy in the classification rate,which is close to and even higher than that of state-of-the-art methods.The designed detection model may be used in telemedicine,healthcare,and computer-aided diagnosis systems.
基金the Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia for funding this research through project number PNU-DRI-RI-20-033.
文摘The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals.Unfortunately,personal air quality measurement remains challenging.In this study,we investigate the use of first-person photos for the prediction of air quality.The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction.AirStackNet consists of two layers and four regression models,where the first layer generates meta-data fromLight Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting Regression(XGBoost)and CatBoost Regression(CatBoost),whereas the second layer computes the final prediction from the meta-data of the first layer using Extra Tree Regression(ET).The performance of the proposed AirStackNet model is validated using public Personal Air Quality Dataset(PAQD).Our experiments are evaluated using Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Coefficient of Determination(R2),Mean Squared Error(MSE),Root Mean Squared Logarithmic Error(RMSLE),and Mean Absolute Percentage Error(MAPE).Experimental Results indicate that the proposed AirStackNet model not only can effectively improve air pollution prediction performance by overcoming the Bias-Variance tradeoff,but also outperforms baseline and state of the art models.
