Parkinson’s disease(PD)is a progressive neurodegenerative disorder characterized by tremors,rigidity,and decreased movement.PD poses risks to individuals’lives and independence.Early detection of PD is essential bec...Parkinson’s disease(PD)is a progressive neurodegenerative disorder characterized by tremors,rigidity,and decreased movement.PD poses risks to individuals’lives and independence.Early detection of PD is essential because it allows timely intervention,which can slow disease progression and improve outcomes.Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD.In addition,the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis.Artificial intelligence(AI)techniques,especially deep and automated learning models,provide promising solutions to address deficiencies in manual diagnosis.This study develops robust systems for PD diagnosis by analyzing handwritten helical and wave graphical images.Handwritten graphic images of the PD dataset are enhanced using two overlapping filters,the average filter and the Laplacian filter,to improve image quality and highlight essential features.The enhanced images are segmented to isolate regions of interest(ROIs)from the rest of the image using a gradient vector flow(GVF)algorithm,which ensures that features are extracted from only relevant regions.The segmented ROIs are fed into convolutional neural network(CNN)models,namely DenseNet169,MobileNet,and VGG16,to extract fine and deep feature maps that capture complex patterns and representations relevant to PD diagnosis.Fine and deep feature maps extracted from individual CNN models are combined into fused feature vectors for DenseNet169-MobileNet,MobileNet-VGG16,DenseNet169-VGG16,and DenseNet169-MobileNet-VGG16 models.This fusion technique aims to combine complementary and robust features from several models,which improves the extracted features.Two feature selection algorithms are considered to remove redundancy and weak correlations within the combined feature set:Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS).These algorithms identify and retain the most strongly correlated features while eliminating redundant and weakly correlated features,thus optimizing the features to improve system performance.The fused and enhanced feature vectors are fed into two powerful classifiers,XGBoost and random forest(RF),for accurate classification and differentiation between individuals with PD and healthy controls.The proposed hybrid systems show superior performance,where the RF classifier used the combined features from the DenseNet169-MobileNet-VGG16 models with the ACO feature selection method,achieving outstanding results:area under the curve(AUC)of 99%,sensitivity of 99.6%,99.3%accuracy,99.35%accuracy,and 99.65%specificity.展开更多
In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be resolved.Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Indus...In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be resolved.Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks.Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs improvement.In this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT(PPBDL-IIoT)on 6G environment.The proposed PPBDLIIoT technique aims at identifying the existence of intrusions in network.Further,PPBDL-IIoT technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the network.Besides,CGO technique is applied to fine tune the hyperparameters in BiGRNN model.CGO algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model.Moreover,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system.The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various measures.The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.展开更多
The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,etc.Besides,the industrial Internet of th...The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,etc.Besides,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large regions.UAVs are commonly employed in the industrial sector to solve several issues and help decision making.But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs.Federated learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user data.In this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT environment.The proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image classification.For UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective communication.In addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers.Finally,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach.The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.展开更多
Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine ...Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.展开更多
The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are ...The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy.These traditional methods,namely therapy,need a large number of visits to hospitals and include medication.Neurogames could be used for the effective treatment of ADHD.It could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain signals.This paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial memory.The proposed system consists of a puzzle game where its complexity increases with each level.EEG signals were acquired using the Neurosky headset;then sent the signals to the designed gaming environment.This neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation measures.The results show that this game improves player overall performance from 74%to 98%by playing each game level.展开更多
Novel coronavirus 2019(COVID-19)has affected the people’s health,their lifestyle and economical status across the globe.The application of advanced Artificial Intelligence(AI)methods in combination with radiological ...Novel coronavirus 2019(COVID-19)has affected the people’s health,their lifestyle and economical status across the globe.The application of advanced Artificial Intelligence(AI)methods in combination with radiological imaging is useful in accurate detection of the disease.It also assists the physicians to take care of remote villages too.The current research paper proposes a novel automatedCOVID-19 analysismethod with the help ofOptimal Hybrid Feature Extraction(OHFE)and Optimal Deep Neural Network(ODNN)called OHFE-ODNN from chest x-ray images.The objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray image.The presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering(MF)-based pre-processed,feature extraction and finally,binary(COVID/Non-COVID)and multiclass(Normal,COVID,SARS)classification.Besides,in OHFE-based feature extraction,Gray Level Co-occurrence Matrix(GLCM)and Histogram of Gradients(HOG)are integrated together.The presented OHFE-ODNN model includes Squirrel Search Algorithm(SSA)for finetuning the parameters of DNN.The performance of the presented OHFEODNN technique is conducted using chest x-rays dataset.The presented OHFE-ODNN method classified the binary classes effectively with a maximumprecision of 95.82%,accuracy of 94.01%and F-score of 96.61%.Besides,multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%,accuracy of 95.60%and an F-score of 95.73%.展开更多
Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by...Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.展开更多
Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribu...Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribute guided vector that enables high visual quality content generation.This research study proposes automatic face image realism from freehand sketches based on Edge GAN.We propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute details.The density level is projected into non-latent space,having a linear controlled function parameter.This assists the user to appropriately devise the variant densities of facial sketches and image synthesis.Composite data set of Large Scale CelebFaces Attributes(ClebA),Labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed approach.The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.展开更多
文摘Parkinson’s disease(PD)is a progressive neurodegenerative disorder characterized by tremors,rigidity,and decreased movement.PD poses risks to individuals’lives and independence.Early detection of PD is essential because it allows timely intervention,which can slow disease progression and improve outcomes.Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD.In addition,the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis.Artificial intelligence(AI)techniques,especially deep and automated learning models,provide promising solutions to address deficiencies in manual diagnosis.This study develops robust systems for PD diagnosis by analyzing handwritten helical and wave graphical images.Handwritten graphic images of the PD dataset are enhanced using two overlapping filters,the average filter and the Laplacian filter,to improve image quality and highlight essential features.The enhanced images are segmented to isolate regions of interest(ROIs)from the rest of the image using a gradient vector flow(GVF)algorithm,which ensures that features are extracted from only relevant regions.The segmented ROIs are fed into convolutional neural network(CNN)models,namely DenseNet169,MobileNet,and VGG16,to extract fine and deep feature maps that capture complex patterns and representations relevant to PD diagnosis.Fine and deep feature maps extracted from individual CNN models are combined into fused feature vectors for DenseNet169-MobileNet,MobileNet-VGG16,DenseNet169-VGG16,and DenseNet169-MobileNet-VGG16 models.This fusion technique aims to combine complementary and robust features from several models,which improves the extracted features.Two feature selection algorithms are considered to remove redundancy and weak correlations within the combined feature set:Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS).These algorithms identify and retain the most strongly correlated features while eliminating redundant and weakly correlated features,thus optimizing the features to improve system performance.The fused and enhanced feature vectors are fed into two powerful classifiers,XGBoost and random forest(RF),for accurate classification and differentiation between individuals with PD and healthy controls.The proposed hybrid systems show superior performance,where the RF classifier used the combined features from the DenseNet169-MobileNet-VGG16 models with the ACO feature selection method,achieving outstanding results:area under the curve(AUC)of 99%,sensitivity of 99.6%,99.3%accuracy,99.35%accuracy,and 99.65%specificity.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/23/42).
文摘In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be resolved.Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks.Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs improvement.In this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT(PPBDL-IIoT)on 6G environment.The proposed PPBDLIIoT technique aims at identifying the existence of intrusions in network.Further,PPBDL-IIoT technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the network.Besides,CGO technique is applied to fine tune the hyperparameters in BiGRNN model.CGO algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model.Moreover,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system.The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various measures.The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.
基金We deeply acknowledge Taif University for supporting this research through Taif University Researchers Supporting Project Number(TURSP-2020/328),Taif University,Taif,Saudi Arabia.
文摘The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,etc.Besides,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large regions.UAVs are commonly employed in the industrial sector to solve several issues and help decision making.But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs.Federated learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user data.In this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT environment.The proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image classification.For UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective communication.In addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers.Finally,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach.The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.
文摘Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.
基金funding for this study under Technology Development Fund(TDF-02-228).supported by AIDA Lab CCIS Prince Sultan University Riyadh Saudi Arabia and authors would also like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges(APC)of this publication.
文摘The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy.These traditional methods,namely therapy,need a large number of visits to hospitals and include medication.Neurogames could be used for the effective treatment of ADHD.It could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain signals.This paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial memory.The proposed system consists of a puzzle game where its complexity increases with each level.EEG signals were acquired using the Neurosky headset;then sent the signals to the designed gaming environment.This neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation measures.The results show that this game improves player overall performance from 74%to 98%by playing each game level.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work underGrant Number(RGP.1/172/42).www.kku.edu.sa。
文摘Novel coronavirus 2019(COVID-19)has affected the people’s health,their lifestyle and economical status across the globe.The application of advanced Artificial Intelligence(AI)methods in combination with radiological imaging is useful in accurate detection of the disease.It also assists the physicians to take care of remote villages too.The current research paper proposes a novel automatedCOVID-19 analysismethod with the help ofOptimal Hybrid Feature Extraction(OHFE)and Optimal Deep Neural Network(ODNN)called OHFE-ODNN from chest x-ray images.The objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray image.The presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering(MF)-based pre-processed,feature extraction and finally,binary(COVID/Non-COVID)and multiclass(Normal,COVID,SARS)classification.Besides,in OHFE-based feature extraction,Gray Level Co-occurrence Matrix(GLCM)and Histogram of Gradients(HOG)are integrated together.The presented OHFE-ODNN model includes Squirrel Search Algorithm(SSA)for finetuning the parameters of DNN.The performance of the presented OHFEODNN technique is conducted using chest x-rays dataset.The presented OHFE-ODNN method classified the binary classes effectively with a maximumprecision of 95.82%,accuracy of 94.01%and F-score of 96.61%.Besides,multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%,accuracy of 95.60%and an F-score of 95.73%.
文摘Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.
基金The authors received no specific funding for this study.
文摘Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribute guided vector that enables high visual quality content generation.This research study proposes automatic face image realism from freehand sketches based on Edge GAN.We propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute details.The density level is projected into non-latent space,having a linear controlled function parameter.This assists the user to appropriately devise the variant densities of facial sketches and image synthesis.Composite data set of Large Scale CelebFaces Attributes(ClebA),Labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed approach.The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.
