In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative...In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.展开更多
The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardio...The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.展开更多
Smart homes present a serious challenge for the aged and those with mobility issues due to the environment’s inherent danger.Unwary people have the propensity to fall over when bending over in these settings.Here,the...Smart homes present a serious challenge for the aged and those with mobility issues due to the environment’s inherent danger.Unwary people have the propensity to fall over when bending over in these settings.Here,they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for(CM-I and CM-II).The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization criteria.They compared actual and simulated data involving missteps,mishaps,and crashes to gauge the programmers’performance.Results suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future study.The CM-II model had the highest prediction accuracy of any model identified in the literature,at 0.98 when compared to the test parameter.Since the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers,the number of hacks aimed at the IoT has grown dramatically.There is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the attacks.Pursuing a deeper understanding of the technologies,protocols,and architecture of IoT systems,as well as the potential consequences of using infected IoT devices,is the overarching goal of this study.There are many Internet of Things(IoT)systems vulnerable to cybercriminal manipulation,so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.展开更多
Human Action Recognition(HAR)attempts to recognize the human action from images and videos.The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A...Human Action Recognition(HAR)attempts to recognize the human action from images and videos.The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral filters.The proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral information.To create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are combined.Linear Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate data.Finally,a closest neighbor method is used to classify the dataset.Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well.The average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,respectively.The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,respectively.From the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.展开更多
文摘In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.
基金funded by Researchers Supporting ProjectNumber(RSPD2025R947),King Saud University,Riyadh,Saudi Arabia.
文摘The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.
文摘Smart homes present a serious challenge for the aged and those with mobility issues due to the environment’s inherent danger.Unwary people have the propensity to fall over when bending over in these settings.Here,they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for(CM-I and CM-II).The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization criteria.They compared actual and simulated data involving missteps,mishaps,and crashes to gauge the programmers’performance.Results suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future study.The CM-II model had the highest prediction accuracy of any model identified in the literature,at 0.98 when compared to the test parameter.Since the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers,the number of hacks aimed at the IoT has grown dramatically.There is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the attacks.Pursuing a deeper understanding of the technologies,protocols,and architecture of IoT systems,as well as the potential consequences of using infected IoT devices,is the overarching goal of this study.There are many Internet of Things(IoT)systems vulnerable to cybercriminal manipulation,so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.
文摘Human Action Recognition(HAR)attempts to recognize the human action from images and videos.The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral filters.The proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral information.To create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are combined.Linear Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate data.Finally,a closest neighbor method is used to classify the dataset.Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well.The average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,respectively.The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,respectively.From the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.
