Pub Date : 2023-10-31DOI: 10.1007/s10586-023-04154-z
Vidyapati Jha, Priyanka Tripathi
{"title":"Decentralized knowledge discovery using massive heterogenous data in Cognitive IoT","authors":"Vidyapati Jha, Priyanka Tripathi","doi":"10.1007/s10586-023-04154-z","DOIUrl":"https://doi.org/10.1007/s10586-023-04154-z","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135872512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-28DOI: 10.1007/s10586-023-04160-1
Anusha Nalajala, T. Ragunathan, Ranesh Naha, Sudheer Kumar Battula
{"title":"Application and user-specific data prefetching and parallel read algorithms for distributed file systems","authors":"Anusha Nalajala, T. Ragunathan, Ranesh Naha, Sudheer Kumar Battula","doi":"10.1007/s10586-023-04160-1","DOIUrl":"https://doi.org/10.1007/s10586-023-04160-1","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136158636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-28DOI: 10.1007/s10586-023-04170-z
Nasraldeen Alnor Adam Khleel, Károly Nehéz
Abstract Software defects are a critical issue in software development that can lead to system failures and cause significant financial losses. Predicting software defects is a vital aspect of ensuring software quality. This can significantly impact both saving time and reducing the overall cost of software testing. During the software defect prediction (SDP) process, automated tools attempt to predict defects in the source codes based on software metrics. Several SDP models have been proposed to identify and prevent defects before they occur. In recent years, recurrent neural network (RNN) techniques have gained attention for their ability to handle sequential data and learn complex patterns. Still, these techniques are not always suitable for predicting software defects due to the problem of imbalanced data. To deal with this problem, this study aims to combine a bidirectional long short-term memory (Bi-LSTM) network with oversampling techniques. To establish the effectiveness and efficiency of the proposed model, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR) and mean square error (MSE). The average accuracy of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) was 88%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average accuracy by 6 and 4% compared to the original datasets. The average F-measure of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) were 51%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average F-measure by 43 and 41% compared to the original datasets. The experimental results demonstrated that combining the Bi-LSTM network with oversampling techniques positively affects defect prediction performance in datasets with imbalanced class distributions.
{"title":"Software defect prediction using a bidirectional LSTM network combined with oversampling techniques","authors":"Nasraldeen Alnor Adam Khleel, Károly Nehéz","doi":"10.1007/s10586-023-04170-z","DOIUrl":"https://doi.org/10.1007/s10586-023-04170-z","url":null,"abstract":"Abstract Software defects are a critical issue in software development that can lead to system failures and cause significant financial losses. Predicting software defects is a vital aspect of ensuring software quality. This can significantly impact both saving time and reducing the overall cost of software testing. During the software defect prediction (SDP) process, automated tools attempt to predict defects in the source codes based on software metrics. Several SDP models have been proposed to identify and prevent defects before they occur. In recent years, recurrent neural network (RNN) techniques have gained attention for their ability to handle sequential data and learn complex patterns. Still, these techniques are not always suitable for predicting software defects due to the problem of imbalanced data. To deal with this problem, this study aims to combine a bidirectional long short-term memory (Bi-LSTM) network with oversampling techniques. To establish the effectiveness and efficiency of the proposed model, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR) and mean square error (MSE). The average accuracy of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) was 88%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average accuracy by 6 and 4% compared to the original datasets. The average F-measure of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) were 51%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average F-measure by 43 and 41% compared to the original datasets. The experimental results demonstrated that combining the Bi-LSTM network with oversampling techniques positively affects defect prediction performance in datasets with imbalanced class distributions.","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1007/s10586-023-04164-x
Lin Zhang, Lizhen Ji, Zongfang Ma
{"title":"Target positioning method based on B-spline level set and GC Yolo-v3","authors":"Lin Zhang, Lizhen Ji, Zongfang Ma","doi":"10.1007/s10586-023-04164-x","DOIUrl":"https://doi.org/10.1007/s10586-023-04164-x","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-17DOI: 10.1007/s10586-023-04152-1
Avtar Singh, Harpreet Kaur, Navjot Kaur
{"title":"A novel DDoS detection and mitigation technique using hybrid machine learning model and redirect illegitimate traffic in SDN network","authors":"Avtar Singh, Harpreet Kaur, Navjot Kaur","doi":"10.1007/s10586-023-04152-1","DOIUrl":"https://doi.org/10.1007/s10586-023-04152-1","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135992805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-17DOI: 10.1007/s10586-023-04176-7
Safdar Jamil, Abdul Salam, Awais Khan, Bernd Burgstaller, Sung-Soon Park, Youngjae Kim
{"title":"Correction: Scalable NUMA-aware persistent B+-tree for non-volatile memory devices","authors":"Safdar Jamil, Abdul Salam, Awais Khan, Bernd Burgstaller, Sung-Soon Park, Youngjae Kim","doi":"10.1007/s10586-023-04176-7","DOIUrl":"https://doi.org/10.1007/s10586-023-04176-7","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135994656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-14DOI: 10.1007/s10586-023-04157-w
Sara Amaouche, None AzidineGuezzaz, Said Benkirane, None MouradeAzrour
{"title":"IDS-XGbFS: a smart intrusion detection system using XGboostwith recent feature selection for VANET safety","authors":"Sara Amaouche, None AzidineGuezzaz, Said Benkirane, None MouradeAzrour","doi":"10.1007/s10586-023-04157-w","DOIUrl":"https://doi.org/10.1007/s10586-023-04157-w","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135767334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1007/s10586-023-04158-9
Tao Shen, Qianqi Sun, Chi Zhang, Fenhua Bai
{"title":"SEKad: a scalable protocol for blockchain networks with enhanced broadcast efficiency","authors":"Tao Shen, Qianqi Sun, Chi Zhang, Fenhua Bai","doi":"10.1007/s10586-023-04158-9","DOIUrl":"https://doi.org/10.1007/s10586-023-04158-9","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135854940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：