Pub Date : 2023-10-13DOI: 10.1080/1206212x.2023.2267840
D Rambabu, A Govardhan
AbstractCloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness.KEYWORDS: Task schedulingdata replicationcloudimproved correlationoptimization Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Task scheduling and data replication in cloud with improved correlation strategy","authors":"D Rambabu, A Govardhan","doi":"10.1080/1206212x.2023.2267840","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2267840","url":null,"abstract":"AbstractCloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness.KEYWORDS: Task schedulingdata replicationcloudimproved correlationoptimization Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856862","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}
Pub Date : 2023-10-09DOI: 10.1080/1206212x.2023.2263689
Sridevi Malipatil, T. Hanumantha Reddy
ABSTRACTApplications like business basket analysis, digital service analytics, bio-informatics, and mobile commerce have greatly benefited from the information retrieval of significant features from massive databases for improved decision-making. Item set mining is used to find intriguing patterns in databases. Discovering item sets in an uncertain database is a tedious task. Only mathematical correlations between the elements in an item set are the exclusive subject of recurring item set mining research. The finding is direct to optimal. This article introduces an ant colony that maps the viable solution space to a directed graph with quadratic space complexity. The proposed model evaluates an uncertain transaction database's item set. Compared to the current methods, the findings demonstrate the importance of the proposed model.KEYWORDS: Patternsassociation rule miningfrequent itemsdatabase Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Discovery of interesting frequent item sets in an uncertain database using ant colony optimization","authors":"Sridevi Malipatil, T. Hanumantha Reddy","doi":"10.1080/1206212x.2023.2263689","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2263689","url":null,"abstract":"ABSTRACTApplications like business basket analysis, digital service analytics, bio-informatics, and mobile commerce have greatly benefited from the information retrieval of significant features from massive databases for improved decision-making. Item set mining is used to find intriguing patterns in databases. Discovering item sets in an uncertain database is a tedious task. Only mathematical correlations between the elements in an item set are the exclusive subject of recurring item set mining research. The finding is direct to optimal. This article introduces an ant colony that maps the viable solution space to a directed graph with quadratic space complexity. The proposed model evaluates an uncertain transaction database's item set. Compared to the current methods, the findings demonstrate the importance of the proposed model.KEYWORDS: Patternsassociation rule miningfrequent itemsdatabase Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093214","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}
Pub Date : 2023-10-03DOI: 10.1080/1206212x.2023.2262786
N. J. Subashini, K. Venkatesh
ABSTRACTThis research presents an advanced approach to enhance disease diagnosis using imbalanced medical datasets. Feature selection techniques, LASSO and Relief, are applied to identify relevant features from the UCI dataset and missing values are handled appropriately. To address the class imbalance, SMOTEENN is used, creating a new combined dataset with selected features. Three deep learning models, FNNs, LSTMs, and GBMs, are employed and trained on the combined dataset, achieving remarkable accuracy (1.0). Evaluating the models on LASSO and Relief datasets independently, FNN/MLP obtains perfect accuracy, GBM performs well (0.9888 on LASSO and 1.0 on Relief), and LSTM shows good results (0.9663 on LASSO and 1.0 on Relief). This study demonstrates the effectiveness of combining LASSO and Relief for feature selection and highlights the impact of SMOTEENN on model performance. The achieved accuracy with all models on the combined dataset showcases deep learning's potential for accurate disease diagnosis even with imbalanced data, offering promising insights for robust medical diagnosis systems.KEYWORDS: Chronic kidney diseaseMultimodal deep learningLASSOReliefSMOTEENN Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsN. J. SubashiniN. J. Subashini is a Research scholar in Networking and Communications department, SRM Institute of Science and Technology. Her research interests include Data Mining, Artificial Intelligence, Deep Learning and Machine Learning.K. VenkateshK. Venkatesh is Associate Professor in Networking and Communications department, SRM Institute of Science and Technology. His research interests include Networking, Cloud Computing, Data Mining, Artificial Intelligence, and Machine Learning. He is the Program Coordinator for B. Tech CSE specialization with a focus on Computer Networking. Additionally, he serves as an Alumni Coordinator in the Department of Networking and Communications. He is a Cisco certified CCNA Lead Instructor and Academy Contact for SRM Institute of Science and Technology, formerly known as SRM University, Networking Academy.
{"title":"Multimodal deep learning for chronic kidney disease prediction: leveraging feature selection algorithms and ensemble models","authors":"N. J. Subashini, K. Venkatesh","doi":"10.1080/1206212x.2023.2262786","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2262786","url":null,"abstract":"ABSTRACTThis research presents an advanced approach to enhance disease diagnosis using imbalanced medical datasets. Feature selection techniques, LASSO and Relief, are applied to identify relevant features from the UCI dataset and missing values are handled appropriately. To address the class imbalance, SMOTEENN is used, creating a new combined dataset with selected features. Three deep learning models, FNNs, LSTMs, and GBMs, are employed and trained on the combined dataset, achieving remarkable accuracy (1.0). Evaluating the models on LASSO and Relief datasets independently, FNN/MLP obtains perfect accuracy, GBM performs well (0.9888 on LASSO and 1.0 on Relief), and LSTM shows good results (0.9663 on LASSO and 1.0 on Relief). This study demonstrates the effectiveness of combining LASSO and Relief for feature selection and highlights the impact of SMOTEENN on model performance. The achieved accuracy with all models on the combined dataset showcases deep learning's potential for accurate disease diagnosis even with imbalanced data, offering promising insights for robust medical diagnosis systems.KEYWORDS: Chronic kidney diseaseMultimodal deep learningLASSOReliefSMOTEENN Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsN. J. SubashiniN. J. Subashini is a Research scholar in Networking and Communications department, SRM Institute of Science and Technology. Her research interests include Data Mining, Artificial Intelligence, Deep Learning and Machine Learning.K. VenkateshK. Venkatesh is Associate Professor in Networking and Communications department, SRM Institute of Science and Technology. His research interests include Networking, Cloud Computing, Data Mining, Artificial Intelligence, and Machine Learning. He is the Program Coordinator for B. Tech CSE specialization with a focus on Computer Networking. Additionally, he serves as an Alumni Coordinator in the Department of Networking and Communications. He is a Cisco certified CCNA Lead Instructor and Academy Contact for SRM Institute of Science and Technology, formerly known as SRM University, Networking Academy.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135738729","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}
Pub Date : 2023-10-03DOI: 10.1080/1206212x.2023.2260619
Amal Shaheen, Mustafa Hammad, Wael Elmedany, Riadh Ksantini, Saeed Sharif
A model with high accuracy of machine failure prediction is important for any machine life cycle. In this paper, a prediction model based on machine learning methods is proposed. The used method is a combination of machine learning algorithms and techniques. The machine learning algorithm is a data mining technique that has been widely used as a prediction model for classifying problems. Five algorithms have been tested including JRIP, logistic, KStar, Bayes network and decision table machine learning. The evaluation process is done by applying the algorithms on a predictive dataset using different performance measures. In the proposed model, the feature selection and voting techniques are used and applied in the classification process for each classifier. From the comparison of the result, the feature selection shows the best performance result. Paired t-test evaluation measures were considered to confirm our conclusion. The best accuracy result among the five classifiers shows that joint reserve intelligence classifier can be used to predict the failure with an accuracy high as 0.983. Applying classifier subset evaluation using the JRIP classifier can enhance the accuracy result to be 0.985. The finding shows that the proposed model improves the results of the classifiers.
{"title":"Machine failure prediction using joint reserve intelligence with feature selection technique","authors":"Amal Shaheen, Mustafa Hammad, Wael Elmedany, Riadh Ksantini, Saeed Sharif","doi":"10.1080/1206212x.2023.2260619","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2260619","url":null,"abstract":"A model with high accuracy of machine failure prediction is important for any machine life cycle. In this paper, a prediction model based on machine learning methods is proposed. The used method is a combination of machine learning algorithms and techniques. The machine learning algorithm is a data mining technique that has been widely used as a prediction model for classifying problems. Five algorithms have been tested including JRIP, logistic, KStar, Bayes network and decision table machine learning. The evaluation process is done by applying the algorithms on a predictive dataset using different performance measures. In the proposed model, the feature selection and voting techniques are used and applied in the classification process for each classifier. From the comparison of the result, the feature selection shows the best performance result. Paired t-test evaluation measures were considered to confirm our conclusion. The best accuracy result among the five classifiers shows that joint reserve intelligence classifier can be used to predict the failure with an accuracy high as 0.983. Applying classifier subset evaluation using the JRIP classifier can enhance the accuracy result to be 0.985. The finding shows that the proposed model improves the results of the classifiers.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696469","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}
AbstractPaddy disease recognition presents challenges in the agricultural industry, and existing algorithms struggle to accurately identify diseases in complex scenarios. In this paper, we propose a precise object detection framework to address the challenges of severe overlap, multi-disease detection, morphological irregularities, multi-scale object classification, and complex scenarios in real-world environments in paddy disease detection. The proposed model is based on an improved version of the DEtection TRansformer (Detr) algorithm. The enhanced network architecture fuses multi-scale features by adding a feature fusion module after the backbone network, which is able to retain more original information of the images and greatly improves the detection accuracy; the use of deformable attention module greatly reduces the computational complexity of the model. To evaluate the PDN, a dedicated paddy disease detection dataset with 1200 images is created. Experimental results demonstrate that the proposed model obtained a precision value of 100%, a recall value of 89.3%, F1-score of 94.3%, and a mean average precision (mAP) value of 60.2%. The model outperforms the existing state-of-the-art detection models in detection accuracy.KEYWORDS: Paddy disease recognitionTransformermachine vision detection Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported in part by the Jiangsu Basic Science (Natural Science) Research Projects in Higher Education Institutions (No.23KJB460034), Jiangsu province Youth Fund Project (No.BK2023040059), the China Postdoctoral Science Foundation Funded Project (No. 2022M721185), Jiangsu Agriculture Science and Technology Innovation Fund (No. CX(21)3145).Notes on contributorsXinyu ZhangXinyu Zhang is currently a master's student in mechanical engineering at the School of Mechanical Engineering, Yangzhou University. His research interest is machine learning.Hang DongDr. Hang Dong is a lecturer at Yangzhou University. He received his PhD degree in Mechanical Manufacture and Automation from Dalian University of Technology (2019). His research interests include the deep learning, machine learning, and robotics. Hang Dong is the corresponding author and can be contacted at hdong@yzu.edu.cn.Liang GongLiang Gong was born in Maanshan City, Anhui Province, China on October 26, 1999. He received his bachelor's degree from Anhui Polytechnic University in 2021. He is currently a master's student in mechanical engineering at the School of Mechanical Engineering, Yangzhou University. His research interests are machine vision and machine learning.Xin ChengXin Cheng was born in Lian Yungang, China, in 2002.He is currently a student in Yangzhou University.His research interests include computer vision,natural language processing.Zhenghui GeZhenghui Ge is currently an associate professor at Yangzhou University, China. He received his PhD degree from Nanjing Unive
{"title":"Multiple paddy disease recognition methods based on deformable transformer attention mechanism in complex scenarios","authors":"Xinyu Zhang, Hang Dong, Liang Gong, Xin Cheng, Zhenghui Ge, Liangchao Guo","doi":"10.1080/1206212x.2023.2263254","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2263254","url":null,"abstract":"AbstractPaddy disease recognition presents challenges in the agricultural industry, and existing algorithms struggle to accurately identify diseases in complex scenarios. In this paper, we propose a precise object detection framework to address the challenges of severe overlap, multi-disease detection, morphological irregularities, multi-scale object classification, and complex scenarios in real-world environments in paddy disease detection. The proposed model is based on an improved version of the DEtection TRansformer (Detr) algorithm. The enhanced network architecture fuses multi-scale features by adding a feature fusion module after the backbone network, which is able to retain more original information of the images and greatly improves the detection accuracy; the use of deformable attention module greatly reduces the computational complexity of the model. To evaluate the PDN, a dedicated paddy disease detection dataset with 1200 images is created. Experimental results demonstrate that the proposed model obtained a precision value of 100%, a recall value of 89.3%, F1-score of 94.3%, and a mean average precision (mAP) value of 60.2%. The model outperforms the existing state-of-the-art detection models in detection accuracy.KEYWORDS: Paddy disease recognitionTransformermachine vision detection Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported in part by the Jiangsu Basic Science (Natural Science) Research Projects in Higher Education Institutions (No.23KJB460034), Jiangsu province Youth Fund Project (No.BK2023040059), the China Postdoctoral Science Foundation Funded Project (No. 2022M721185), Jiangsu Agriculture Science and Technology Innovation Fund (No. CX(21)3145).Notes on contributorsXinyu ZhangXinyu Zhang is currently a master's student in mechanical engineering at the School of Mechanical Engineering, Yangzhou University. His research interest is machine learning.Hang DongDr. Hang Dong is a lecturer at Yangzhou University. He received his PhD degree in Mechanical Manufacture and Automation from Dalian University of Technology (2019). His research interests include the deep learning, machine learning, and robotics. Hang Dong is the corresponding author and can be contacted at hdong@yzu.edu.cn.Liang GongLiang Gong was born in Maanshan City, Anhui Province, China on October 26, 1999. He received his bachelor's degree from Anhui Polytechnic University in 2021. He is currently a master's student in mechanical engineering at the School of Mechanical Engineering, Yangzhou University. His research interests are machine vision and machine learning.Xin ChengXin Cheng was born in Lian Yungang, China, in 2002.He is currently a student in Yangzhou University.His research interests include computer vision,natural language processing.Zhenghui GeZhenghui Ge is currently an associate professor at Yangzhou University, China. He received his PhD degree from Nanjing Unive","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135745014","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}
Pub Date : 2023-09-28DOI: 10.1080/1206212x.2023.2260616
Koppula Geeta, V. Kamakshi Prasad
AbstractIn this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence.ABBREVIATION: CDC, cloud data center; CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing; CSP, Cloud service providers; CSSA, Chaotic Squirrel Search Algorithm; DA, Dragonfly Algorithm; ED, Euclidean Distance; EDA-GA, Estimation Of Distribution Algorithm And GA; FF, FireFly algorithm; GA, Genetic Algorithm; HHO, Harris Hawk Optimization; IaaS, Infrastructure-as-a-Service; MGWO, Modified Mean Grey Wolf Optimization Algorithm; MMHHO, Mantaray modified multi-objective Harris Hawk optimization; MRFO, Manta Ray Forging Optimization; PaaS, Platform-as-a-Service; PM, Physical Machine; PSO, Particle Swarm Optimization; SaaS, Software-as-a-Service; SAW, Sample additive weighting; SLA-LB, Service Level Agreement-Based Load Balancing; TBTS, Threshold-Based Task Scheduling Algorithm; TS, Task SchedulingKEYWORDS: Cloud computingload balancingDCCOpower consumptionmemory utilizationmigration cost Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKoppula GeetaKoppula Geeta, Currently working as Assistant Professor of Computer Science & Engineering at Rajiv Gandhi University of Knowledge Technologies Basar, She is having 18 years of teaching experience. Received her B.Tech, M.Tech from JNTUH. Currently she is pursuing PhD in JNTUH, Hyderabad. Her main research interests includes Cloud computing, Data mining.V. Kamakshi PrasadProfessor V. Kamakshi Prasad currently serving as a Senior Professor of Computer Science & Engineering at JNTUH College of Engineering Science & Technology in Hyderabad, has 31 years of teaching and research experience. He obtained his B.Tech., M.Tech., and Ph.D. degrees from KLCE, Andhra University College of Enginee
{"title":"Multi-objective cloud load-balancing with hybrid optimization","authors":"Koppula Geeta, V. Kamakshi Prasad","doi":"10.1080/1206212x.2023.2260616","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2260616","url":null,"abstract":"AbstractIn this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence.ABBREVIATION: CDC, cloud data center; CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing; CSP, Cloud service providers; CSSA, Chaotic Squirrel Search Algorithm; DA, Dragonfly Algorithm; ED, Euclidean Distance; EDA-GA, Estimation Of Distribution Algorithm And GA; FF, FireFly algorithm; GA, Genetic Algorithm; HHO, Harris Hawk Optimization; IaaS, Infrastructure-as-a-Service; MGWO, Modified Mean Grey Wolf Optimization Algorithm; MMHHO, Mantaray modified multi-objective Harris Hawk optimization; MRFO, Manta Ray Forging Optimization; PaaS, Platform-as-a-Service; PM, Physical Machine; PSO, Particle Swarm Optimization; SaaS, Software-as-a-Service; SAW, Sample additive weighting; SLA-LB, Service Level Agreement-Based Load Balancing; TBTS, Threshold-Based Task Scheduling Algorithm; TS, Task SchedulingKEYWORDS: Cloud computingload balancingDCCOpower consumptionmemory utilizationmigration cost Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKoppula GeetaKoppula Geeta, Currently working as Assistant Professor of Computer Science & Engineering at Rajiv Gandhi University of Knowledge Technologies Basar, She is having 18 years of teaching experience. Received her B.Tech, M.Tech from JNTUH. Currently she is pursuing PhD in JNTUH, Hyderabad. Her main research interests includes Cloud computing, Data mining.V. Kamakshi PrasadProfessor V. Kamakshi Prasad currently serving as a Senior Professor of Computer Science & Engineering at JNTUH College of Engineering Science & Technology in Hyderabad, has 31 years of teaching and research experience. He obtained his B.Tech., M.Tech., and Ph.D. degrees from KLCE, Andhra University College of Enginee","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385411","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}
Pub Date : 2023-09-27DOI: 10.1080/1206212x.2023.2260617
Taha Etem, Turgay Kaya
AbstractBlock encryption algorithms are among the most preferred applications in cryptographic systems. Block ciphers should have accomplished some requirements for a secure communication system. They should be evaluated in terms of cryptanalysis methods for widespread usage. The aim of this paper is to introduce a new secure and fast block encryption algorithm for images. For this purpose, a new block cipher, which offers an innovative encryption structure for key generation systems and can use S-boxes with different methods, is proposed. A Dynamic S-Box is used in the algorithm for both substitution and key generation purposes. Linear and differential cryptanalysis methods were performed successfully. UACI and NPCR tests show that the proposed symmetric block cipher algorithm is compatible with image encryption systems. The 512-bit key length provides the highest security for block encryption. Additionally, information entropy test, correlation coefficients, mean-squared error, and peak signal-to-noise ratio analyses were concluded successfully. The novelty of the paper is building a cryptanalysis attack-resistant block cipher algorithm that presents a lightweight cryptographic solution for image encryption systems.KEYWORDS: Block ciphersymmetric encryptionS-BoxNPCR and UACIimage processing AcknowledgementsThis study has been produced from the doctoral dissertation of Taha Etem. Authors’ contributions: T.E. conceived and designed the analysis, collected the data, contributed analysis tools, and wrote the paper. T.K. edited the paper, controlled the analysis, and supervised.Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialData sharing is not applicable to this article as no new data were created or analyzed in this study.Additional informationNotes on contributorsTaha EtemTaha Etem received the B.Sc. degree in Electrical-Electronics Engineering from Firat University, Elazig, Turkey, in 2013, and the M.Sc. degree in Electrical-Electronics Engineering from Inonu University, Malatya, Turkey, in 2017, and received the Ph.D. degree in Electrical-Electronics Engineering from Firat University, Elazig, Turkey, in 2022. He was with Cankiri Karatekin University, Cankiri, Turkey, as a Faculty Member. He is currently an Assistant Professor in the Computer Engineering Department. His research interests include encryption systems, random number generators and radio-frequency systems.Turgay KayaTurgay Kaya was born in Elazig, Turkey, in 1982. He received the B.Sc., M.Sc. and Ph.D. degrees in electrical-electronics engineering from the Firat University in 2003, 2006 and 2011, respectively. From 2004 to 20013, he was a Research Assistant at department of electrical-electronics engineering, Firat University, Elazig, Turkey. Since 2013, he has been an Associate Professor same department. His research interests include digital and analog filter design, biomedical signal processing, signal and image
{"title":"Fast image encryption algorithm with random structures","authors":"Taha Etem, Turgay Kaya","doi":"10.1080/1206212x.2023.2260617","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2260617","url":null,"abstract":"AbstractBlock encryption algorithms are among the most preferred applications in cryptographic systems. Block ciphers should have accomplished some requirements for a secure communication system. They should be evaluated in terms of cryptanalysis methods for widespread usage. The aim of this paper is to introduce a new secure and fast block encryption algorithm for images. For this purpose, a new block cipher, which offers an innovative encryption structure for key generation systems and can use S-boxes with different methods, is proposed. A Dynamic S-Box is used in the algorithm for both substitution and key generation purposes. Linear and differential cryptanalysis methods were performed successfully. UACI and NPCR tests show that the proposed symmetric block cipher algorithm is compatible with image encryption systems. The 512-bit key length provides the highest security for block encryption. Additionally, information entropy test, correlation coefficients, mean-squared error, and peak signal-to-noise ratio analyses were concluded successfully. The novelty of the paper is building a cryptanalysis attack-resistant block cipher algorithm that presents a lightweight cryptographic solution for image encryption systems.KEYWORDS: Block ciphersymmetric encryptionS-BoxNPCR and UACIimage processing AcknowledgementsThis study has been produced from the doctoral dissertation of Taha Etem. Authors’ contributions: T.E. conceived and designed the analysis, collected the data, contributed analysis tools, and wrote the paper. T.K. edited the paper, controlled the analysis, and supervised.Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialData sharing is not applicable to this article as no new data were created or analyzed in this study.Additional informationNotes on contributorsTaha EtemTaha Etem received the B.Sc. degree in Electrical-Electronics Engineering from Firat University, Elazig, Turkey, in 2013, and the M.Sc. degree in Electrical-Electronics Engineering from Inonu University, Malatya, Turkey, in 2017, and received the Ph.D. degree in Electrical-Electronics Engineering from Firat University, Elazig, Turkey, in 2022. He was with Cankiri Karatekin University, Cankiri, Turkey, as a Faculty Member. He is currently an Assistant Professor in the Computer Engineering Department. His research interests include encryption systems, random number generators and radio-frequency systems.Turgay KayaTurgay Kaya was born in Elazig, Turkey, in 1982. He received the B.Sc., M.Sc. and Ph.D. degrees in electrical-electronics engineering from the Firat University in 2003, 2006 and 2011, respectively. From 2004 to 20013, he was a Research Assistant at department of electrical-electronics engineering, Firat University, Elazig, Turkey. Since 2013, he has been an Associate Professor same department. His research interests include digital and analog filter design, biomedical signal processing, signal and image","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135539022","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}
Pub Date : 2023-09-19DOI: 10.1080/1206212x.2023.2258329
Vishruth B. Gowda, M. T. Gopalakrishna, J. Megha, Shilpa Mohankumar
AbstractBackground initialization is used in video processing applications to extract a scene without the foreground scene. In recent times, the issue of background initialization has gained researchers’ attention in different fields such as video surveillance, video segmentation, computational photography, and so on. The initialization of the background is affected due to different complex dissimilarities such as shadow, intermittent movement, illumination, camera jitter, and clutter. To overcome the aforementioned issues, this paper proposes a decomposition using the combination of the Singular Value Decomposition (SVD) and Robust Principal Component Analysis (RPCA) for Singular Spectrum Analysis (SSA) to perform an effective background initialization. The incorporation of RPCA in SVD is used to overcome the issues related to non-Gaussian noise and it also uses an effective structural knowledge of the video input i.e. sparse and low rank matrix which improves the Peak-Signal-to-Noise-Ratio (PSNR) of the background image. The SBI dataset was used to analyze the performances of SSA-SVDRPCA. The SSA-SVDRPCA is analyzed using MultiScale Structural Similarity Index (MSSSIM), Average gray-level error (AGE), Percentage of clustered error pixels (pCEPS), Percentage of error pixels (pEPs), and PSNR. The existing approaches such as Background Initialization Singular Spectrum Analysis (BISSA) and Quaternion-based Dynamic Mode Decomposition (Q-DMD) are used to compare with the SSA-SVDRPCA method. The PSNR of the SSA-SVDRPCA for Board class is 30.39 dB which is higher than the BISSA and Q-DMD.KEYWORDS: Background initializationdecompositionpeak-signal-to-noise-ratiorobust principal component analysissingular spectrum analysissingular value decomposition Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe datasets generated during and/or analyzed during the current study are available in the Scene Background Initialization (SBI) dataset.SBI datasethttps://sbmi2015.na.icar.cnr.it/SBIdataset.htmlAdditional informationNotes on contributorsVishruth B. GowdaVishruth B. Gowda completed his BE in AIEMS, bangalore, karnataka and Mtech from EWIT. He currently works as an assistant professor in Department of ISE,SJB Institute of technology. He is also pursuing his research in VTU, Belagavi, Karnataka under the supervision of Dr. M T Gopalakrishna. His research area falls under the domain of comuter vision and image processing.M. T. GopalakrishnaM. T. Gopalakrishna received B. E degree (Computer Science & Engineering) from M. S Ramaiah Institute of Technology, India, M. Tech degree from Visvesvaraya Technological University, Karnataka, India and PhD from Visvesvaraya Technological University, Karnataka, India. He has more than 22 years of teaching experience. He is currently Professor & Head, Department of Artificial Intelligence and Machine Learning in SJB Institute of Technology, Bangalore, India. He has
摘要背景初始化在视频处理应用中用于提取没有前景的场景。近年来，背景初始化问题在视频监控、视频分割、计算摄影等不同领域受到了研究人员的关注。背景的初始化受到各种复杂差异的影响，如阴影、间歇运动、照明、相机抖动和杂波。为了克服上述问题，本文提出了一种结合奇异值分解(SVD)和鲁棒主成分分析(RPCA)进行奇异谱分析(SSA)的分解方法来进行有效的背景初始化。将RPCA结合到SVD中用于克服与非高斯噪声相关的问题，并且它还使用了视频输入的有效结构知识，即稀疏和低秩矩阵，从而提高了背景图像的峰值信噪比(PSNR)。使用SBI数据集分析SSA-SVDRPCA的性能。采用多尺度结构相似指数(MSSSIM)、平均灰度误差(AGE)、聚类误差像素百分比(pCEPS)、误差像素百分比(pEPs)和PSNR对SSA-SVDRPCA进行分析。利用背景初始化奇异谱分析(BISSA)和基于四元数的动态模态分解(Q-DMD)等现有方法与SSA-SVDRPCA方法进行比较。SSA-SVDRPCA的PSNR为30.39 dB，高于bisa和Q-DMD。关键词:背景初始化、分解、峰值信噪比、抗噪主成分分析、奇异谱分析、奇异值分解披露声明作者未报告潜在利益冲突。数据可用性声明在当前研究期间生成和/或分析的数据集在场景背景初始化(SBI)数据集中可用。作者简介:davishruth B. Gowda完成了他在AIEMS、班加罗尔、卡纳塔克邦和Mtech的论文。现为上海工学院电子工程系助理教授。他还在M T Gopalakrishna博士的指导下，在卡纳塔克邦Belagavi的VTU进行研究。他的研究领域属于计算机视觉和图像处理领域。t . GopalakrishnaM。T. Gopalakrishna获得印度Ramaiah理工学院计算机科学与工程学士学位，印度卡纳塔克邦Visvesvaraya理工大学技术硕士学位和印度卡纳塔克邦Visvesvaraya理工大学博士学位。他有超过22年的教学经验。他目前是印度班加罗尔SJB理工学院人工智能和机器学习系的教授兼系主任。在各类国际期刊、国际会议和国内会议上发表论文63余篇。主要研究方向为模式识别、数字图像处理与计算机视觉。MeghaJ。现为印度理工大学人工智能与机器学习系助理教授。研究重点是模式识别和图像处理。Shilpa Mohankumar是BIT印度理工学院的助理教授。主要研究方向为图像处理、计算机视觉和模式识别。
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Pub Date : 2023-09-19DOI: 10.1080/1206212x.2023.2258328
Subhajit Das, Sourav Mandal, Rohini Basak
AbstractBecause of the rapid advancement of technology over the last several years, the number of internet users is growing at an exponential rate, and as a result, email communication has become popular as a means of exchanging information over the internet. Sending data and communicating with peers via email is the most cost-effective method. These email services also cause problems for users by sending electronic junk mail, often known as spam mail. Spam email is a privacy concern that is linked to a slew of commercial and dangerous websites, causing phishing, virus distribution, and a slew of other problems. This study examines several aspects that have been used for email spam classification, as well as offering an overview of a handful of classifiers or algorithms that have been successfully evaluated, as well as exploratory data analysis. The proposed email spam classifier uses three parallel layers of machine learning and deep learning techniques, followed by a decision function to determine whether or not the emails are spam. During testing, it was found that the proposed classifier beats similar systems on the standard dataset with an accuracy of 98.4%.KEYWORDS: Content-based spam classificationemail spam classificationtext classificationmachine learningdeep learning Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://github.com/tensorflow/estimator2 https://nlp.stanford.edu/projects/glove/3 http://nlp.cs.aueb.gr/software_and_datasets/Enron-Spam/index.html4 https://www.tensorflow.org/Additional informationNotes on contributorsSubhajit DasSubhajit Das is an Information Technology Engineer with more than 11 years of experience in software Development. He has completed Master of Engineering from Jadavpur University, Kolakta, India on Software Engineering and received a bachelor's degree in Computer Science and Engineering from West Bengal University of Technology, India. He presently holds the position of Senior Software Engineer at Cognizant Technology Solutions. He is also interested in building the architecture of contemporary systems using cloud and GenAI solutions, addressing difficult problems, migrating technologies, and optimizing algorithms.Sourav MandalSourav Mandal has been an Assistant Professor at XIM University's School of Computer Science and Engineering (SCSE), in Bhubaneswar, Odisha, India since October 2020. Prior to that, he had been employed since 2006 as an Assistant Professor in the Department of Computer Science and Engineering at the Haldia Institute of Technology in Haldia, India. Among his research interests in the natural language processing (NLP) and artificial intelligence (AI) field are natural language understanding, information extraction, text classification, text summarization, etc. with data science, machine learning, and deep learning. Sourav Mandal earned a bachelor's degree in Computer Science & Engineering from The University of Burdwan in Burdwan, India,
摘要由于近年来科技的飞速发展，互联网用户的数量呈指数级增长，因此，电子邮件通信作为一种在互联网上交换信息的手段已经变得流行起来。通过电子邮件发送数据和与同行通信是最经济有效的方法。这些电子邮件服务还通过发送电子垃圾邮件(通常被称为垃圾邮件)给用户带来问题。垃圾邮件是一种隐私问题，它与大量商业和危险网站有关，导致网络钓鱼、病毒传播和一系列其他问题。本研究考察了用于垃圾邮件分类的几个方面，并概述了一些已成功评估的分类器或算法，以及探索性数据分析。提出的垃圾邮件分类器使用机器学习和深度学习技术的三个并行层，然后是一个决策函数来确定电子邮件是否为垃圾邮件。在测试过程中，发现所提出的分类器以98.4%的准确率击败了标准数据集上的类似系统。关键词:基于内容的垃圾邮件分类电子邮件垃圾邮件分类文本分类机器学习深度学习披露声明作者未报告潜在的利益冲突。注1 https://github.com/tensorflow/estimator2 https://nlp.stanford.edu/projects/glove/3 http://nlp.cs.aueb.gr/software_and_datasets/Enron-Spam/index.html4 https://www.tensorflow.org/Additional信息贡献者说明subhajit DasSubhajit Das是一名信息技术工程师，在软件开发方面拥有超过11年的经验。他获得了印度Kolakta Jadavpur大学软件工程硕士学位，并获得了印度西孟加拉邦科技大学计算机科学与工程学士学位。他目前担任Cognizant Technology Solutions的高级软件工程师。他还对使用云和GenAI解决方案构建当代系统架构、解决难题、迁移技术和优化算法感兴趣。Sourav Mandal自2020年10月起担任印度奥里萨邦布巴内斯瓦尔的XIM大学计算机科学与工程学院(SCSE)的助理教授。在此之前，他自2006年以来一直担任位于印度Haldia的Haldia Institute of Technology的计算机科学与工程系助理教授。他在自然语言处理(NLP)和人工智能(AI)领域的研究兴趣包括自然语言理解、信息提取、文本分类、文本摘要等与数据科学、机器学习和深度学习的结合。Sourav Mandal于2003年在印度布尔德万大学获得计算机科学与工程学士学位，2005年在印度加尔各答贾达夫普尔大学获得多媒体开发硕士学位，并于2020年在印度加尔各答贾达夫普尔大学获得工程学博士学位。Rohini BasakRohini Basak自2018年起担任印度贾达夫普尔大学信息技术系助理教授。她于2020年在同一所大学获得计算机科学与工程博士学位。她的研究兴趣包括自然语言处理、计算语言学、情感分析、深度学习等。至今已指导硕士生10名。主要讲授面向对象编程(c++)、面向对象系统(Java)、数据结构与算法、计算机组织与网络等。
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Pub Date : 2023-09-14DOI: 10.1080/1206212x.2023.2256048
Sylvia W. Azumah, Nelly Elsayed, Zag ElSayed, Murat Ozer
AbstractTechnological advancements have resulted in an exponential increase in the use of online social networks (OSNs) worldwide. While online social networks provide a great communication medium, they also increase the user's exposure to life-threatening situations such as suicide, eating disorder, cybercrime, compulsive behavior, anxiety, and depression. To tackle the issue of cyberbullying, most existing literature focuses on developing approaches to identifying factors and understanding the textual factors associated with cyberbullying. While most of these approaches have brought great success in cyberbullying research, data availability needed to develop model detection remains a challenge in the research space. This paper conducts a comprehensive literature review to provide an understanding of cyberbullying detection.Keywords: Cyberbullyingcybercrimetext detectiondeep learningsocial media Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSylvia W. AzumahSylvia W. Azumah is a Ghanaian PhD candidate in Information Technology, specializing in cybersecurity at the University of Cincinnati. She holds a master's in IT from the same university, where she was recognized as the most outstanding student and a bachelors in IT at Bluecrest University from Ghana, West Africa An avid coder, mentor to women in cybersecurity.Nelly ElsayedDr. Nelly Elsayed is an Assistant Professor at the School of Information Technology. She is the Leader and Founder of the Applied Machine Learning and Intelligence Lab. She received a BS. and MS. degree in Computer Science from Alexandria University, and she received her MS. Eng. and Ph.D. degrees from the University of Louisiana at Lafayette. She is an IEEE Computational Intelligence Society active member. She has served as a principal investigator and co-principle investigator in different federal, educational, and industrial level-funded research projects. She received the Faculty Incentive Award for Research and Scholarship from the CECH, UC, recognizing her research contributions, journal and conference peer-reviewed publications, and professional presentations in 2020-2021. She received the Love of Learning Award from the Honor Society Phi Kappa Phi in 2019, 2021 and 2023. She received the Golden Apple Award for Excellence in Teaching (Graduate Level), CECH. She received the UCAADA Sarah Grant Barber Outstanding Advising Faculty Award for the academic year 2021-2022 University of Cincinnati. She has been an Ambassador for Goodwill of Lafayette, Louisiana, since 2017.Zag ElSayedDr Zag ElSayed was born in Odessa, USSR; he is a computer engineering scientist specializing in the Brain Machine Interface, Artificial Intelligence, Cybersecurity for Cyber-Physical Systems and I2oT as well as VLSI Digital Design. He received his B.S. and M.S. with Distinction degree of Honor from Alexandria University in 2005 where he introduced the early framework
{"title":"Cyberbullying in text content detection: an analytical review","authors":"Sylvia W. Azumah, Nelly Elsayed, Zag ElSayed, Murat Ozer","doi":"10.1080/1206212x.2023.2256048","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2256048","url":null,"abstract":"AbstractTechnological advancements have resulted in an exponential increase in the use of online social networks (OSNs) worldwide. While online social networks provide a great communication medium, they also increase the user's exposure to life-threatening situations such as suicide, eating disorder, cybercrime, compulsive behavior, anxiety, and depression. To tackle the issue of cyberbullying, most existing literature focuses on developing approaches to identifying factors and understanding the textual factors associated with cyberbullying. While most of these approaches have brought great success in cyberbullying research, data availability needed to develop model detection remains a challenge in the research space. This paper conducts a comprehensive literature review to provide an understanding of cyberbullying detection.Keywords: Cyberbullyingcybercrimetext detectiondeep learningsocial media Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSylvia W. AzumahSylvia W. Azumah is a Ghanaian PhD candidate in Information Technology, specializing in cybersecurity at the University of Cincinnati. She holds a master's in IT from the same university, where she was recognized as the most outstanding student and a bachelors in IT at Bluecrest University from Ghana, West Africa An avid coder, mentor to women in cybersecurity.Nelly ElsayedDr. Nelly Elsayed is an Assistant Professor at the School of Information Technology. She is the Leader and Founder of the Applied Machine Learning and Intelligence Lab. She received a BS. and MS. degree in Computer Science from Alexandria University, and she received her MS. Eng. and Ph.D. degrees from the University of Louisiana at Lafayette. She is an IEEE Computational Intelligence Society active member. She has served as a principal investigator and co-principle investigator in different federal, educational, and industrial level-funded research projects. She received the Faculty Incentive Award for Research and Scholarship from the CECH, UC, recognizing her research contributions, journal and conference peer-reviewed publications, and professional presentations in 2020-2021. She received the Love of Learning Award from the Honor Society Phi Kappa Phi in 2019, 2021 and 2023. She received the Golden Apple Award for Excellence in Teaching (Graduate Level), CECH. She received the UCAADA Sarah Grant Barber Outstanding Advising Faculty Award for the academic year 2021-2022 University of Cincinnati. She has been an Ambassador for Goodwill of Lafayette, Louisiana, since 2017.Zag ElSayedDr Zag ElSayed was born in Odessa, USSR; he is a computer engineering scientist specializing in the Brain Machine Interface, Artificial Intelligence, Cybersecurity for Cyber-Physical Systems and I2oT as well as VLSI Digital Design. He received his B.S. and M.S. with Distinction degree of Honor from Alexandria University in 2005 where he introduced the early framework","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135487152","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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