Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorit...Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.展开更多
The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling c...The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.展开更多
Cloud computing(CC)is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services.In addition,it aims to strengthen systems and make them ...Cloud computing(CC)is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services.In addition,it aims to strengthen systems and make them useful.Regardless of these advantages,cloud providers suffer from many security limits.Particularly,the security of resources and services represents a real challenge for cloud technologies.For this reason,a set of solutions have been implemented to improve cloud security by monitoring resources,services,and networks,then detect attacks.Actually,intrusion detection system(IDS)is an enhanced mechanism used to control traffic within networks and detect abnormal activities.This paper presents a cloud-based intrusion detection model based on random forest(RF)and feature engineering.Specifically,the RF classifier is obtained and integrated to enhance accuracy(ACC)of the proposed detection model.The proposed model approach has been evaluated and validated on two datasets and gives 98.3%ACC and 99.99%ACC using Bot-IoT and NSL-KDD datasets,respectively.Consequently,the obtained results present good performances in terms of ACC,precision,and recall when compared to the recent related works.展开更多
This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independe...This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independent Component Analysis(ICA),Singular Value Decomposition(SVD),and a dimension-reduction technique like Nonnegative Matrix Factorization(NMF).Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s,the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy.Furthermore,we can disentangle the various components of fetal ECG,which serve as inputs to the CNN model to optimize the actual FECG signal,denoted by FECGr,which is recovered using the SVD-ICA process.The findings demonstrate the efficiency of this innovative approach,which may be deployed in real-time.展开更多
Date palm production is critical to oasis agriculture,owing to its economic importance and nutritional advantages.Numerous diseases endanger this precious tree,putting a strain on the economy and environment.White sca...Date palm production is critical to oasis agriculture,owing to its economic importance and nutritional advantages.Numerous diseases endanger this precious tree,putting a strain on the economy and environment.White scale Parlatoria blanchardi is a damaging bug that degrades the quality of dates.When an infestation reaches a specific degree,it might result in the tree’s death.To counter this threat,precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is necessary.This decision is crucial for farmers who wish to minimize yield losses while preserving production quality.For this purpose,we propose a feature extraction and machine learning(ML)technique based framework for classifying the stages of infestation by white scale disease(WSD)in date palm trees by investigating their leaflets images.80 gray level co-occurrence matrix(GLCM)texture features and 9 hue,saturation,and value(HSV)color moments features are extracted from both grayscale and color images of the used dataset.To classify the WSD into its four classes(healthy,low infestation degree,medium infestation degree,and high infestation degree),two types of ML algorithms were tested;classical machine learning methods,namely,support vector machine(SVM)and k-nearest neighbors(KNN),and ensemble learning methods such as random forest(RF)and light gradient boosting machine(LightGBM).The ML models were trained and evaluated using two datasets:the first is composed of the extracted GLCM features only,and the second combines GLCM and HSV descriptors.The results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%.The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease(DPWSD)and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.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
Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficult...Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficulty choosing the film they want to watch.The process of choosing or searching for a film in a large film database is currently time-consuming and tedious.Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste.This happens especially because humans are confused about choosing things and quickly change their minds.Hence,the recommendation system becomes critical.This study aims to reduce user effort and facilitate the movie research task.Further,we used the root mean square error scale to evaluate and compare different models adopted in this paper.These models were employed with the aim of developing a classification model for predicting movies.Thus,we tested and evaluated several cooperative filtering techniques.We used four approaches to implement sparse matrix completion algorithms:k-nearest neighbors,matrix factorization,co-clustering,and slope-one.展开更多
文摘Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.
文摘The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.
文摘Cloud computing(CC)is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services.In addition,it aims to strengthen systems and make them useful.Regardless of these advantages,cloud providers suffer from many security limits.Particularly,the security of resources and services represents a real challenge for cloud technologies.For this reason,a set of solutions have been implemented to improve cloud security by monitoring resources,services,and networks,then detect attacks.Actually,intrusion detection system(IDS)is an enhanced mechanism used to control traffic within networks and detect abnormal activities.This paper presents a cloud-based intrusion detection model based on random forest(RF)and feature engineering.Specifically,the RF classifier is obtained and integrated to enhance accuracy(ACC)of the proposed detection model.The proposed model approach has been evaluated and validated on two datasets and gives 98.3%ACC and 99.99%ACC using Bot-IoT and NSL-KDD datasets,respectively.Consequently,the obtained results present good performances in terms of ACC,precision,and recall when compared to the recent related works.
文摘This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independent Component Analysis(ICA),Singular Value Decomposition(SVD),and a dimension-reduction technique like Nonnegative Matrix Factorization(NMF).Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s,the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy.Furthermore,we can disentangle the various components of fetal ECG,which serve as inputs to the CNN model to optimize the actual FECG signal,denoted by FECGr,which is recovered using the SVD-ICA process.The findings demonstrate the efficiency of this innovative approach,which may be deployed in real-time.
文摘Date palm production is critical to oasis agriculture,owing to its economic importance and nutritional advantages.Numerous diseases endanger this precious tree,putting a strain on the economy and environment.White scale Parlatoria blanchardi is a damaging bug that degrades the quality of dates.When an infestation reaches a specific degree,it might result in the tree’s death.To counter this threat,precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is necessary.This decision is crucial for farmers who wish to minimize yield losses while preserving production quality.For this purpose,we propose a feature extraction and machine learning(ML)technique based framework for classifying the stages of infestation by white scale disease(WSD)in date palm trees by investigating their leaflets images.80 gray level co-occurrence matrix(GLCM)texture features and 9 hue,saturation,and value(HSV)color moments features are extracted from both grayscale and color images of the used dataset.To classify the WSD into its four classes(healthy,low infestation degree,medium infestation degree,and high infestation degree),two types of ML algorithms were tested;classical machine learning methods,namely,support vector machine(SVM)and k-nearest neighbors(KNN),and ensemble learning methods such as random forest(RF)and light gradient boosting machine(LightGBM).The ML models were trained and evaluated using two datasets:the first is composed of the extracted GLCM features only,and the second combines GLCM and HSV descriptors.The results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%.The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease(DPWSD)and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.
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.
文摘Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficulty choosing the film they want to watch.The process of choosing or searching for a film in a large film database is currently time-consuming and tedious.Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste.This happens especially because humans are confused about choosing things and quickly change their minds.Hence,the recommendation system becomes critical.This study aims to reduce user effort and facilitate the movie research task.Further,we used the root mean square error scale to evaluate and compare different models adopted in this paper.These models were employed with the aim of developing a classification model for predicting movies.Thus,we tested and evaluated several cooperative filtering techniques.We used four approaches to implement sparse matrix completion algorithms:k-nearest neighbors,matrix factorization,co-clustering,and slope-one.
