Hand gestures are the nonverbal communication done by individuals who cannot represent their thoughts in form of words. It is mainly used during human‐computer interaction (HCI), deaf and mute people interaction, and other robotic interface applications. Gesture recognition is a field of computer science mainly focused on improving the HCI via touch screens, cameras, and kinetic devices. The state‐of‐art systems mainly used computer vision‐based techniques that utilize both the motion sensor and camera to capture the hand gestures in real‐time and interprets them via the usage of the machine learning algorithms. Conventional machine learning algorithms often suffer from the different complexities present in the visible hand gesture images such as skin color, distance, light, hand direction, position, and background. In this article, an adaptive weighted multi‐scale resolution (AWMSR) network with a deep embedded hybrid convolutional neural network and long short term memory network (hybrid CNN‐LSTM) is proposed for identifying the different hand gesture signs with higher recognition accuracy. The proposed methodology is formulated using three steps: input preprocessing, feature extraction, and classification. To improve the complex visual effects present in the input images, a histogram equalization technique is used which improves the size of the gray level pixel in the image and also their occurrence probability. The multi‐block local binary pattern (MB‐LBP) algorithm is employed for feature extraction which extracts the crucial features present in the image such as hand shape structure feature, curvature feature, and invariant movements. The AWMSR with the deep embedded hybrid CNN–LSTM network is applied in the two‐benchmark datasets namely Jochen Triesch static hand posture and NUS hand posture dataset‐II to detect its stability in identifying different hand gestures. The weight function of the deep embedded CNN‐LSTM architecture is optimized using the puzzle optimization algorithm. The efficiency of the proposed methodology is verified in terms of different performance evaluation metrics such as accuracy, loss, confusion matrix, Intersection over the union, and execution time. The proposed methodology offers recognition accuracy of 97.86% and 98.32% for both datasets.
{"title":"An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model","authors":"Gajalakshmi Palanisamy, T. Sharmila","doi":"10.1002/cpe.7109","DOIUrl":"https://doi.org/10.1002/cpe.7109","url":null,"abstract":"Hand gestures are the nonverbal communication done by individuals who cannot represent their thoughts in form of words. It is mainly used during human‐computer interaction (HCI), deaf and mute people interaction, and other robotic interface applications. Gesture recognition is a field of computer science mainly focused on improving the HCI via touch screens, cameras, and kinetic devices. The state‐of‐art systems mainly used computer vision‐based techniques that utilize both the motion sensor and camera to capture the hand gestures in real‐time and interprets them via the usage of the machine learning algorithms. Conventional machine learning algorithms often suffer from the different complexities present in the visible hand gesture images such as skin color, distance, light, hand direction, position, and background. In this article, an adaptive weighted multi‐scale resolution (AWMSR) network with a deep embedded hybrid convolutional neural network and long short term memory network (hybrid CNN‐LSTM) is proposed for identifying the different hand gesture signs with higher recognition accuracy. The proposed methodology is formulated using three steps: input preprocessing, feature extraction, and classification. To improve the complex visual effects present in the input images, a histogram equalization technique is used which improves the size of the gray level pixel in the image and also their occurrence probability. The multi‐block local binary pattern (MB‐LBP) algorithm is employed for feature extraction which extracts the crucial features present in the image such as hand shape structure feature, curvature feature, and invariant movements. The AWMSR with the deep embedded hybrid CNN–LSTM network is applied in the two‐benchmark datasets namely Jochen Triesch static hand posture and NUS hand posture dataset‐II to detect its stability in identifying different hand gestures. The weight function of the deep embedded CNN‐LSTM architecture is optimized using the puzzle optimization algorithm. The efficiency of the proposed methodology is verified in terms of different performance evaluation metrics such as accuracy, loss, confusion matrix, Intersection over the union, and execution time. The proposed methodology offers recognition accuracy of 97.86% and 98.32% for both datasets.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87245218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this research work, Facial Expression Recognition (FER) is used in the analysis of facial expressions during the online learning sessions in the prevailing pandemic situation. An integrated geometric and appearance feature extraction is presented for the FER of the students participating in the online classes. The integrated features provided a low‐dimensional significant feature area for better facial data representation. Feasible Weighted Squirrel Search Optimization (FW‐SSO) algorithm is applied for selecting the optimal features due to its efficient exploration of the search space and enhancement of the dynamic search. The output of the FW‐SSO algorithm is used for tuning the autoencoder. Autoencoder is used for combining the G&A features, for feature optimization process. Classification is done by using Long Short‐Term Memory (LSTM) network with Attention Mechanism (ALSTM), as it is highly efficient in capturing the long‐term dependency of the facial landmarks in the image/video sequences. The proposed fused deep learning method focuses on the fusion of the G&A features for high discrimination. Experimental analysis using FER‐2013 and LIRIS datasets demonstrated that the proposed method achieved maximum accuracy of 85.96% than the existing architectures and maximum accuracy of 88.24% than the VGGNet‐CNN architecture.
{"title":"Fused deep learning based Facial Expression Recognition of students in online learning mode","authors":"C. H. Sumalakshmi, P. Vasuki","doi":"10.1002/cpe.7137","DOIUrl":"https://doi.org/10.1002/cpe.7137","url":null,"abstract":"In this research work, Facial Expression Recognition (FER) is used in the analysis of facial expressions during the online learning sessions in the prevailing pandemic situation. An integrated geometric and appearance feature extraction is presented for the FER of the students participating in the online classes. The integrated features provided a low‐dimensional significant feature area for better facial data representation. Feasible Weighted Squirrel Search Optimization (FW‐SSO) algorithm is applied for selecting the optimal features due to its efficient exploration of the search space and enhancement of the dynamic search. The output of the FW‐SSO algorithm is used for tuning the autoencoder. Autoencoder is used for combining the G&A features, for feature optimization process. Classification is done by using Long Short‐Term Memory (LSTM) network with Attention Mechanism (ALSTM), as it is highly efficient in capturing the long‐term dependency of the facial landmarks in the image/video sequences. The proposed fused deep learning method focuses on the fusion of the G&A features for high discrimination. Experimental analysis using FER‐2013 and LIRIS datasets demonstrated that the proposed method achieved maximum accuracy of 85.96% than the existing architectures and maximum accuracy of 88.24% than the VGGNet‐CNN architecture.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87705763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Polycystic ovary syndrome (PCOS) is a hormonal disorder that affects women at an early age. Manual detection of PCOS is a challenging task for specialists, however, detection of PCOS as quick and accurate could save the lives of millions of women over the world. Current studies use high dimension features which leads to a low estimation accuracy, and high execution time. However, in this article, we develop a new intelligence system to classify PCOS based on k‐means coupled with a LS‐SVM (K‐M‐SVM) using a lower number of features. The original dataset is preprocessed and then k‐means is applied to select the most powerful features based on Euclidean distance to classify PCOS. It was found that the k‐means cluster had a high potential in selection the most influential features and eliminating the poor ones. As a result, a total of six features are chosen to represent PCOS data from the original features. The selected feature set are fed to the LS‐SVM to classify them into healthy and no healthy segments. Our findings showed that the proposed model (K‐M‐SVM) outperformed the state of the art, and it gained an accuracy of 99%.
{"title":"An intelligence model for detection of PCOS based on k‐means coupled with LS‐SVM","authors":"Najlaa Nsrulaah Faris, Firsas Saber Miften","doi":"10.1002/cpe.7139","DOIUrl":"https://doi.org/10.1002/cpe.7139","url":null,"abstract":"Polycystic ovary syndrome (PCOS) is a hormonal disorder that affects women at an early age. Manual detection of PCOS is a challenging task for specialists, however, detection of PCOS as quick and accurate could save the lives of millions of women over the world. Current studies use high dimension features which leads to a low estimation accuracy, and high execution time. However, in this article, we develop a new intelligence system to classify PCOS based on k‐means coupled with a LS‐SVM (K‐M‐SVM) using a lower number of features. The original dataset is preprocessed and then k‐means is applied to select the most powerful features based on Euclidean distance to classify PCOS. It was found that the k‐means cluster had a high potential in selection the most influential features and eliminating the poor ones. As a result, a total of six features are chosen to represent PCOS data from the original features. The selected feature set are fed to the LS‐SVM to classify them into healthy and no healthy segments. Our findings showed that the proposed model (K‐M‐SVM) outperformed the state of the art, and it gained an accuracy of 99%.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82163146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lingpeng Meng, Xiaobo Yao, Qian Chen, Chuanfeng Han
Critical infrastructures provide citizens with lifeline functions such as water, electricity and energy and so forth. These interdependent infrastructure systems require reliable models for vulnerability measurement and topological controllability against usual disruptions and unusual hazards. This article proposes a novel approach, named vulnerability cloud, to describe vulnerability distribution and assess the vulnerability of critical infrastructure systems. A vulnerability distribution network is developed for simulation of negative impact on each node, with which the results are represented in vulnerability cloud by three metrics of vulnerability. The vulnerability cloud of single‐service and multiservice infrastructure system are proposed, respectively. This approach is applied to a case study of “electric‐gas” interdependent critical infrastructure system. Results show that a node's vulnerability and serviceability is closely related to the node's degree, especially the out‐degree, while overall system's vulnerability is greatly affected by descent rate of coverage of each infrastructural service node. This approach, at the same time, generates probabilistic simulation diagrams to show continuous vulnerability distribution in areas covered by the specified critical infrastructure systems.
{"title":"Vulnerability cloud: A novel approach to assess the vulnerability of critical infrastructure systems","authors":"Lingpeng Meng, Xiaobo Yao, Qian Chen, Chuanfeng Han","doi":"10.1002/cpe.7131","DOIUrl":"https://doi.org/10.1002/cpe.7131","url":null,"abstract":"Critical infrastructures provide citizens with lifeline functions such as water, electricity and energy and so forth. These interdependent infrastructure systems require reliable models for vulnerability measurement and topological controllability against usual disruptions and unusual hazards. This article proposes a novel approach, named vulnerability cloud, to describe vulnerability distribution and assess the vulnerability of critical infrastructure systems. A vulnerability distribution network is developed for simulation of negative impact on each node, with which the results are represented in vulnerability cloud by three metrics of vulnerability. The vulnerability cloud of single‐service and multiservice infrastructure system are proposed, respectively. This approach is applied to a case study of “electric‐gas” interdependent critical infrastructure system. Results show that a node's vulnerability and serviceability is closely related to the node's degree, especially the out‐degree, while overall system's vulnerability is greatly affected by descent rate of coverage of each infrastructural service node. This approach, at the same time, generates probabilistic simulation diagrams to show continuous vulnerability distribution in areas covered by the specified critical infrastructure systems.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88181624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application analysis of digital fund prediction model based on neural network","authors":"Yu Liu, Jing Xiao","doi":"10.1002/cpe.7144","DOIUrl":"https://doi.org/10.1002/cpe.7144","url":null,"abstract":"","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82842456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Early and accurately predicting the students' dropout enables schools to recognize the students based on available educational data. The early student dropout prediction is a major concern of education administrators. The existing classification techniques were unable to handle the early stage accurate performance of student dropout prediction with maximum accuracy and minimum time. In order to resolve the issue, a novel technique called rank biserial Otsuka–Ochiai stochastic embedded feature selection based bivariate kernelized regressive bootstrap aggregative classifier (RBOOSEFS‐BKBAC) is motivated to perform student dropout prediction. The aim of the designing RBOOSEFS‐BKBAC is to improve student dropout accuracy and minimal time consumption. Initially, the data preprocessing is to perform the data normalization, data cleaning, and duplicate data removal. Next, rank biserial correlation is used for discovering the correlated features. Followed by, Otsuka–Ochiai stochastic neighbor embedded feature selection is carried out to select significant features. Finally, bivariate kernelized regressive bootstrap aggregative classification technique is to perform classification with help of weak classifier. By using Bucklin voting scheme, the classification outcomes are obtained for increasing prediction accuracy as well as minimizing error. Experimental evaluation is performed by using Student‐Drop‐India2016 dataset with different metrics such as prediction accuracy, precision, recall, F‐measure, as well as time. The result of proposed RBOOSEFS‐BKBAC technique is provided that the higher prediction accuracy by 5% and lesser the prediction time by 15%, as compared to the state‐of‐the‐art methods.
{"title":"Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction","authors":"Rajagopal Chinnasamy, Balasubramanian Thangavel","doi":"10.1002/cpe.7133","DOIUrl":"https://doi.org/10.1002/cpe.7133","url":null,"abstract":"Early and accurately predicting the students' dropout enables schools to recognize the students based on available educational data. The early student dropout prediction is a major concern of education administrators. The existing classification techniques were unable to handle the early stage accurate performance of student dropout prediction with maximum accuracy and minimum time. In order to resolve the issue, a novel technique called rank biserial Otsuka–Ochiai stochastic embedded feature selection based bivariate kernelized regressive bootstrap aggregative classifier (RBOOSEFS‐BKBAC) is motivated to perform student dropout prediction. The aim of the designing RBOOSEFS‐BKBAC is to improve student dropout accuracy and minimal time consumption. Initially, the data preprocessing is to perform the data normalization, data cleaning, and duplicate data removal. Next, rank biserial correlation is used for discovering the correlated features. Followed by, Otsuka–Ochiai stochastic neighbor embedded feature selection is carried out to select significant features. Finally, bivariate kernelized regressive bootstrap aggregative classification technique is to perform classification with help of weak classifier. By using Bucklin voting scheme, the classification outcomes are obtained for increasing prediction accuracy as well as minimizing error. Experimental evaluation is performed by using Student‐Drop‐India2016 dataset with different metrics such as prediction accuracy, precision, recall, F‐measure, as well as time. The result of proposed RBOOSEFS‐BKBAC technique is provided that the higher prediction accuracy by 5% and lesser the prediction time by 15%, as compared to the state‐of‐the‐art methods.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87661173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The necessity of cyber‐security has obtained immense importance in day‐to‐day concerns of network communication. Therefore, several available research works predominantly focus on network security to protect the resources, services, and networks from any unauthorized access. A CPS (cyber‐physical system) model using a dual mutation‐based genetic algorithm, with feature classification through Ada‐Boost and SVM classifier is proposed in this paper. Dual‐mutation based genetic‐algorithm overcomes the issues of conventional techniques including convergence issues and local fine‐tuning of features. In this paper, necessary modifications were made to the existing Genetic Algorithm (GA) method to reduce the random nature of the traditional GA method. Particularly, the goal of this work is to develop the modified reproduction operators with appropriate fitness functions to guide simulations to gain optimal solutions. In floating‐point representation, every chromosome vector has been coded as a floating‐point number vector having the same length as the solution vector. Each element was selected initially, to stand within the desired domain, and operators were designed carefully in satisfying the constraints. As a result, there are various enhancements employed in the dual‐mutation algorithm that handles local fine‐tuned features. The relevant features of dataset samples are extracted and rescaled using feature selection and resampling phase aided by the Markov‐resampling process. Followed by this, a hybrid approach of ESVM (enhanced support‐vector machine) algorithm with Ada‐Boost classifier is implemented for the fault classification process. The performance assessment was explicated in terms of accuracy‐factor, F1‐score, and execution time. Comparative analysis expounded the efficacy of the proposed model than other conventional methods attaining higher accuracy (97%), F1‐score (99%) rates, and less execution time (15.33 s).
{"title":"An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm","authors":"Durgesh M. Sharma, Shishir K. Shandilya","doi":"10.1002/cpe.7134","DOIUrl":"https://doi.org/10.1002/cpe.7134","url":null,"abstract":"The necessity of cyber‐security has obtained immense importance in day‐to‐day concerns of network communication. Therefore, several available research works predominantly focus on network security to protect the resources, services, and networks from any unauthorized access. A CPS (cyber‐physical system) model using a dual mutation‐based genetic algorithm, with feature classification through Ada‐Boost and SVM classifier is proposed in this paper. Dual‐mutation based genetic‐algorithm overcomes the issues of conventional techniques including convergence issues and local fine‐tuning of features. In this paper, necessary modifications were made to the existing Genetic Algorithm (GA) method to reduce the random nature of the traditional GA method. Particularly, the goal of this work is to develop the modified reproduction operators with appropriate fitness functions to guide simulations to gain optimal solutions. In floating‐point representation, every chromosome vector has been coded as a floating‐point number vector having the same length as the solution vector. Each element was selected initially, to stand within the desired domain, and operators were designed carefully in satisfying the constraints. As a result, there are various enhancements employed in the dual‐mutation algorithm that handles local fine‐tuned features. The relevant features of dataset samples are extracted and rescaled using feature selection and resampling phase aided by the Markov‐resampling process. Followed by this, a hybrid approach of ESVM (enhanced support‐vector machine) algorithm with Ada‐Boost classifier is implemented for the fault classification process. The performance assessment was explicated in terms of accuracy‐factor, F1‐score, and execution time. Comparative analysis expounded the efficacy of the proposed model than other conventional methods attaining higher accuracy (97%), F1‐score (99%) rates, and less execution time (15.33 s).","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86566053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, cloud computing has become a priority for organizations that seek to facilitate the management of their increasingly complex information systems (IS) that includes different components: data, services, business processes and hardware. With the large number of cloud providers, the selection of cloud services for each IS component remains a challenge because each one has its own requirements in terms of quality of service which may be different from each other. Cloud providers preferences are generally different from those of organizations, hence the need for a negotiation process. In this article, we propose a framework on which the negotiations between organizations and cloud providers will be based. In this framework, we use rough set theory to predict provider preferences. This method plays an important role in improving the results of negotiations and allows to speed up this process since the preferences of the providers will be known. Additionally, we propose an improvement to an existing negotiation strategy in order to further speed up negotiation process and increase organization utility. Experiments show the effectiveness of our approach in terms of utility, time and success rate.
{"title":"A negotiation framework for the cloud using rough set theory‐based preference prediction","authors":"Hela Malouche, Youssef Ben Halima, H. Ghézala","doi":"10.1002/cpe.7149","DOIUrl":"https://doi.org/10.1002/cpe.7149","url":null,"abstract":"In recent years, cloud computing has become a priority for organizations that seek to facilitate the management of their increasingly complex information systems (IS) that includes different components: data, services, business processes and hardware. With the large number of cloud providers, the selection of cloud services for each IS component remains a challenge because each one has its own requirements in terms of quality of service which may be different from each other. Cloud providers preferences are generally different from those of organizations, hence the need for a negotiation process. In this article, we propose a framework on which the negotiations between organizations and cloud providers will be based. In this framework, we use rough set theory to predict provider preferences. This method plays an important role in improving the results of negotiations and allows to speed up this process since the preferences of the providers will be known. Additionally, we propose an improvement to an existing negotiation strategy in order to further speed up negotiation process and increase organization utility. Experiments show the effectiveness of our approach in terms of utility, time and success rate.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85144092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Velliangiri, S. Pandiaraj, S. Joseph, S. Muthubalaji
Alzheimer's disease (AD) is an advanced neurodegenerative disease of the brain that affects the nerve system of brain. Previously, several feature extraction and classification methods were discussed, but that methods provide high over fitting problem, which leads to minimization of detection accuracy. To overcome these issues, the multi class classification of AD diseases using bag of deep feature reduction technique and twin support vector machine classifier (TSVM) optimized with gradient decent optimizer is proposed in this manuscript for classifying the AD disease as severe AD, mild cognitive impairment, healthy control. At first, the input EEG signals are pre‐processed. To decrease the execution time and processing time with feature size, a bag of deep features reduction technique is used. The reduced feature signals are classified by optimized TSVM. The simulation process is implemented in MATLAB environment. The proposed model achieves higher accuracy 33.84%, 28.93%, 33.03%, 27.93%, higher precision 22.87%, 16.97%, 16.97%, and 36.97%, compared with the existing methods, such as piecewise aggregate approximation support vector machine (MCC‐EEG‐PAA‐SVM), convolutional neural network (MCC‐EEG‐CNN), conformal kernel‐based fuzzy support vector machine (MCC‐EEG‐CKF‐SVM), Pearson correlation coefficient‐based feature selection strategy with linear discriminant analysis classifier (MCC‐EEG‐ PCC‐LDA).
{"title":"Multiclass recognition of AD neurological diseases using a bag of deep reduced features coupled with gradient descent optimized twin support vector machine classifier for early diagnosis","authors":"S. Velliangiri, S. Pandiaraj, S. Joseph, S. Muthubalaji","doi":"10.1002/cpe.7099","DOIUrl":"https://doi.org/10.1002/cpe.7099","url":null,"abstract":"Alzheimer's disease (AD) is an advanced neurodegenerative disease of the brain that affects the nerve system of brain. Previously, several feature extraction and classification methods were discussed, but that methods provide high over fitting problem, which leads to minimization of detection accuracy. To overcome these issues, the multi class classification of AD diseases using bag of deep feature reduction technique and twin support vector machine classifier (TSVM) optimized with gradient decent optimizer is proposed in this manuscript for classifying the AD disease as severe AD, mild cognitive impairment, healthy control. At first, the input EEG signals are pre‐processed. To decrease the execution time and processing time with feature size, a bag of deep features reduction technique is used. The reduced feature signals are classified by optimized TSVM. The simulation process is implemented in MATLAB environment. The proposed model achieves higher accuracy 33.84%, 28.93%, 33.03%, 27.93%, higher precision 22.87%, 16.97%, 16.97%, and 36.97%, compared with the existing methods, such as piecewise aggregate approximation support vector machine (MCC‐EEG‐PAA‐SVM), convolutional neural network (MCC‐EEG‐CNN), conformal kernel‐based fuzzy support vector machine (MCC‐EEG‐CKF‐SVM), Pearson correlation coefficient‐based feature selection strategy with linear discriminant analysis classifier (MCC‐EEG‐ PCC‐LDA).","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89441768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for plagiarism. Source code similarity detection tools are used to prevent such misbehavior. Parallel processing has recently become a viable technique for speeding up the processing of large workloads. This article examines the parallelization of a source code similarity detection method based on the greedy string tiling and Karp–Rabin algorithms. Both CPU and GPU parallelization approaches are discussed. The CPU implementation uses Pthreads, whereas the GPU implementation employs CUDA. Depending on the evaluated dataset which consists of real student assignment codes, speedups of up to seven times over the sequential version of the code are achieved. Evaluation results on both platforms are compared and discussed in detail.
{"title":"Comparison of parallel central processing unit‐ and graphics processing unit‐based implementations of greedy string tiling algorithm for source code plagiarism detection","authors":"M. Mišić, M. Tomasevic","doi":"10.1002/cpe.7135","DOIUrl":"https://doi.org/10.1002/cpe.7135","url":null,"abstract":"Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for plagiarism. Source code similarity detection tools are used to prevent such misbehavior. Parallel processing has recently become a viable technique for speeding up the processing of large workloads. This article examines the parallelization of a source code similarity detection method based on the greedy string tiling and Karp–Rabin algorithms. Both CPU and GPU parallelization approaches are discussed. The CPU implementation uses Pthreads, whereas the GPU implementation employs CUDA. Depending on the evaluated dataset which consists of real student assignment codes, speedups of up to seven times over the sequential version of the code are achieved. Evaluation results on both platforms are compared and discussed in detail.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81271564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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