H. C. Ukwuoma, A. J. Gabriel, A. Thompson, B. Alese
Abstract Data security in the cloud has been a major issue since the inception and adoption of cloud computing. Various frameworks have been proposed, and yet data breach prevails. With encryption being the dominant method of cloud data security, the advent of quantum computing implies an urgent need to proffer a model that will provide adequate data security for both classical and quantum computing. Thus, most cryptosystems will be rendered susceptible and obsolete, though some cryptosystems will stand the test of quantum computing. The article proposes a model that comprises the application of a variant of McEliece cryptosystem, which has been tipped to replace Rivest–Shamir–Adleman (RSA) in the quantum computing era to secure access control data and the application of a variant of N-th degree truncated polynomial ring units (NTRU) cryptosystem to secure cloud user data. The simulation of the proposed McEliece algorithm showed that the algorithm has a better time complexity than the existing McEliece cryptosystem. Furthermore, the novel tweaking of parameters S and P further improves the security of the proposed algorithms. More so, the simulation of the proposed NTRU algorithm revealed that the existing NTRU cryptosystem had a superior time complexity when juxtaposed with the proposed NTRU cryptosystem.
{"title":"Post-quantum cryptography-driven security framework for cloud computing","authors":"H. C. Ukwuoma, A. J. Gabriel, A. Thompson, B. Alese","doi":"10.1515/comp-2022-0235","DOIUrl":"https://doi.org/10.1515/comp-2022-0235","url":null,"abstract":"Abstract Data security in the cloud has been a major issue since the inception and adoption of cloud computing. Various frameworks have been proposed, and yet data breach prevails. With encryption being the dominant method of cloud data security, the advent of quantum computing implies an urgent need to proffer a model that will provide adequate data security for both classical and quantum computing. Thus, most cryptosystems will be rendered susceptible and obsolete, though some cryptosystems will stand the test of quantum computing. The article proposes a model that comprises the application of a variant of McEliece cryptosystem, which has been tipped to replace Rivest–Shamir–Adleman (RSA) in the quantum computing era to secure access control data and the application of a variant of N-th degree truncated polynomial ring units (NTRU) cryptosystem to secure cloud user data. The simulation of the proposed McEliece algorithm showed that the algorithm has a better time complexity than the existing McEliece cryptosystem. Furthermore, the novel tweaking of parameters S and P further improves the security of the proposed algorithms. More so, the simulation of the proposed NTRU algorithm revealed that the existing NTRU cryptosystem had a superior time complexity when juxtaposed with the proposed NTRU cryptosystem.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49345890","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}
Abstract With the rapid development and progress of big data technology, people can already use big data to judge the transmission and distribution of network information and make better decisions in time, but it also faces major network threats such as Trojan horses and viruses. Traditional network security functions generally wait until the network power is turned on to a certain extent before starting, and it is difficult to ensure the security of big data networks. To protect the network security of big data and improve its ability to defend against attacks, this article introduces the deep learning algorithm into the research of big data network security defense mode. The test results show that the introduction of deep learning algorithms into the research of network security model can enhance the security defense capability of the network by 5.12%, proactively detect, and kill cyber attacks that can pose threats. At the same time, the security defense mode will evaluate the network security of big data and analyze potential network security risks in detail, which will prevent risks before they occur and effectively protect the network security in the context of big data.
{"title":"Big data network security defense mode of deep learning algorithm","authors":"Ying Yu","doi":"10.1515/comp-2022-0257","DOIUrl":"https://doi.org/10.1515/comp-2022-0257","url":null,"abstract":"Abstract With the rapid development and progress of big data technology, people can already use big data to judge the transmission and distribution of network information and make better decisions in time, but it also faces major network threats such as Trojan horses and viruses. Traditional network security functions generally wait until the network power is turned on to a certain extent before starting, and it is difficult to ensure the security of big data networks. To protect the network security of big data and improve its ability to defend against attacks, this article introduces the deep learning algorithm into the research of big data network security defense mode. The test results show that the introduction of deep learning algorithms into the research of network security model can enhance the security defense capability of the network by 5.12%, proactively detect, and kill cyber attacks that can pose threats. At the same time, the security defense mode will evaluate the network security of big data and analyze potential network security risks in detail, which will prevent risks before they occur and effectively protect the network security in the context of big data.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48869891","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}
Abstract Through the analysis of the current research situation at home and abroad, this article finds that there is a lack of evaluation standards and methods in the virtual simulation experiment of e-commerce logistics smart warehousing. Therefore, it seriously affects the standardization and rationality of the experiment. To solve the problems in the evaluation of the current virtual simulation experiment, this article proposes a virtual simulation experiment evaluation model of e-commerce logistics smart warehousing based on multidimensional weighting. This article firstly sorts out the basic process of e-commerce logistics smart warehousing experiment activities and establishes the evaluation object. Then, based on the duality degree of the output results of the experimental steps, it proposes a method that conforms to the corresponding operation steps. Thus, a three-dimensional evaluation model of the completion degree of the operation steps, the reasonable degree of the operation steps, and the completion time of the operation steps are constructed. An automatic scoring evaluation model is proposed based on the combination of three-dimensional weighted evaluation of experimental steps. Finally, the feasibility and convenience of the evaluation model are verified through the experiment analysis.
{"title":"Research on the virtual simulation experiment evaluation model of e-commerce logistics smart warehousing based on multidimensional weighting","authors":"Ganglong Fan, Bo Fan, Hongsheng Xu, Chuqiao Wang","doi":"10.1515/comp-2022-0249","DOIUrl":"https://doi.org/10.1515/comp-2022-0249","url":null,"abstract":"Abstract Through the analysis of the current research situation at home and abroad, this article finds that there is a lack of evaluation standards and methods in the virtual simulation experiment of e-commerce logistics smart warehousing. Therefore, it seriously affects the standardization and rationality of the experiment. To solve the problems in the evaluation of the current virtual simulation experiment, this article proposes a virtual simulation experiment evaluation model of e-commerce logistics smart warehousing based on multidimensional weighting. This article firstly sorts out the basic process of e-commerce logistics smart warehousing experiment activities and establishes the evaluation object. Then, based on the duality degree of the output results of the experimental steps, it proposes a method that conforms to the corresponding operation steps. Thus, a three-dimensional evaluation model of the completion degree of the operation steps, the reasonable degree of the operation steps, and the completion time of the operation steps are constructed. An automatic scoring evaluation model is proposed based on the combination of three-dimensional weighted evaluation of experimental steps. Finally, the feasibility and convenience of the evaluation model are verified through the experiment analysis.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43001277","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}
Abstract The rough set theory is a powerful numerical model used to handle the impreciseness and ambiguity of data. Many existing multigranulation rough set models were derived from the multigranulation decision-theoretic rough set framework. The multigranulation rough set theory is very desirable in many practical applications such as high-dimensional knowledge discovery, distributional information systems, and multisource data processing. So far research works were carried out only for multigranulation rough sets in extraction, selection of features, reduction of data, decision rules, and pattern extraction. The proposed approach mainly focuses on anomaly detection in qualitative data with multiple granules. The approximations of the dataset will be derived through multiequivalence relation, and then, the rough set-based entropy measure with weighted density method is applied on every object and attribute. For detecting outliers, threshold value fixation is performed based on the estimated weight. The performance of the algorithm is evaluated and compared with existing outlier detection algorithms. Datasets such as breast cancer, chess, and car evaluation have been taken from the UCI repository to prove its efficiency and performance.
{"title":"Rough set-based entropy measure with weighted density outlier detection method","authors":"T. Sangeetha, Geetha Mary Amalanathan","doi":"10.1515/comp-2020-0228","DOIUrl":"https://doi.org/10.1515/comp-2020-0228","url":null,"abstract":"Abstract The rough set theory is a powerful numerical model used to handle the impreciseness and ambiguity of data. Many existing multigranulation rough set models were derived from the multigranulation decision-theoretic rough set framework. The multigranulation rough set theory is very desirable in many practical applications such as high-dimensional knowledge discovery, distributional information systems, and multisource data processing. So far research works were carried out only for multigranulation rough sets in extraction, selection of features, reduction of data, decision rules, and pattern extraction. The proposed approach mainly focuses on anomaly detection in qualitative data with multiple granules. The approximations of the dataset will be derived through multiequivalence relation, and then, the rough set-based entropy measure with weighted density method is applied on every object and attribute. For detecting outliers, threshold value fixation is performed based on the estimated weight. The performance of the algorithm is evaluated and compared with existing outlier detection algorithms. Datasets such as breast cancer, chess, and car evaluation have been taken from the UCI repository to prove its efficiency and performance.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48375646","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}
Chuanyun Xu, Yueping Zheng, Yang Zhang, Gang Li, Ying Wang
Abstract Recent object detectors have achieved excellent performance in accuracy and speed. Even with such impressive results, the most advanced detectors are challenging in dense scenes. In this article, we analyze and find the reasons for the decrease in detection accuracy in dense scenes. We started our work in terms of region proposal and location loss. We found that low-quality proposal regions during the training process are the main factors affecting detection accuracy. To prove our research, we established and trained a dense detection model based on Cascade R-CNN. The model achieves an accuracy of mAP 0.413 on the SKU-110K sub-dataset. Our results show that improving the quality of recommended regions can effectively improve the detection accuracy in dense scenes.
{"title":"A method for detecting objects in dense scenes","authors":"Chuanyun Xu, Yueping Zheng, Yang Zhang, Gang Li, Ying Wang","doi":"10.1515/comp-2022-0231","DOIUrl":"https://doi.org/10.1515/comp-2022-0231","url":null,"abstract":"Abstract Recent object detectors have achieved excellent performance in accuracy and speed. Even with such impressive results, the most advanced detectors are challenging in dense scenes. In this article, we analyze and find the reasons for the decrease in detection accuracy in dense scenes. We started our work in terms of region proposal and location loss. We found that low-quality proposal regions during the training process are the main factors affecting detection accuracy. To prove our research, we established and trained a dense detection model based on Cascade R-CNN. The model achieves an accuracy of mAP 0.413 on the SKU-110K sub-dataset. Our results show that improving the quality of recommended regions can effectively improve the detection accuracy in dense scenes.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44086066","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}
Haijie Shen, Y. Li, Xinzhi Tian, Xiaofan Chen, Caihong Li, Qian Bian, Zhenduo Wang, Weihua Wang
Abstract With the rapid development of the Internet of Things, the requirements for massive data processing technology are getting higher and higher. Traditional computer data processing capabilities can no longer deliver fast, simple, and efficient data analysis and processing for today’s massive data processing due to the real-time, massive, polymorphic, and heterogeneous characteristics of Internet of Things data. Mass heterogeneous data of different types of subsystems in the Internet of Things need to be processed and stored uniformly, so the mass data processing method is required to be able to integrate multiple different networks, multiple data sources, and heterogeneous mass data and be able to perform processing on these data. Therefore, this article proposes massive data processing and multidimensional database management based on deep learning to meet the needs of contemporary society for massive data processing. This article has deeply studied the basic technical methods of massive data processing, including MapReduce technology, parallel data technology, database technology based on distributed memory databases, and distributed real-time database technology based on cloud computing technology, and constructed a massive data fusion algorithm based on deep learning. The model and the multidimensional online analytical processing model of the multidimensional database based on deep learning analyze the performance, scalability, load balancing, data query, and other aspects of the multidimensional database based on deep learning. It is concluded that the accuracy of multidimensional database query data is as high as 100%, and the accuracy of the average data query time is only 0.0053 s, which is much lower than the general database query time.
{"title":"Mass data processing and multidimensional database management based on deep learning","authors":"Haijie Shen, Y. Li, Xinzhi Tian, Xiaofan Chen, Caihong Li, Qian Bian, Zhenduo Wang, Weihua Wang","doi":"10.1515/comp-2022-0251","DOIUrl":"https://doi.org/10.1515/comp-2022-0251","url":null,"abstract":"Abstract With the rapid development of the Internet of Things, the requirements for massive data processing technology are getting higher and higher. Traditional computer data processing capabilities can no longer deliver fast, simple, and efficient data analysis and processing for today’s massive data processing due to the real-time, massive, polymorphic, and heterogeneous characteristics of Internet of Things data. Mass heterogeneous data of different types of subsystems in the Internet of Things need to be processed and stored uniformly, so the mass data processing method is required to be able to integrate multiple different networks, multiple data sources, and heterogeneous mass data and be able to perform processing on these data. Therefore, this article proposes massive data processing and multidimensional database management based on deep learning to meet the needs of contemporary society for massive data processing. This article has deeply studied the basic technical methods of massive data processing, including MapReduce technology, parallel data technology, database technology based on distributed memory databases, and distributed real-time database technology based on cloud computing technology, and constructed a massive data fusion algorithm based on deep learning. The model and the multidimensional online analytical processing model of the multidimensional database based on deep learning analyze the performance, scalability, load balancing, data query, and other aspects of the multidimensional database based on deep learning. It is concluded that the accuracy of multidimensional database query data is as high as 100%, and the accuracy of the average data query time is only 0.0053 s, which is much lower than the general database query time.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42526067","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}
Abstract Steganography is a technique that embeds secret information in a suitable cover file such as text, image, audio, and video in such a manner that secret information remains invisible to the outside world. The study of the literature relevant to video steganography reveals that a tradeoff exists in attaining the acceptable values of various evaluation parameters such as a higher capacity usually results in lesser robustness or imperceptibility. In this article, we propose a technique that achieves high capacity along with required robustness. The embedding capacity is increased using singular value decomposition compression. To achieve the desired robustness, we constrain the embedding of the secret message in the region of interest in the cover video file. In this manner, we also succeed in maintaining the required imperceptibility. We prefer Haar-based lifting scheme in the wavelet domain for embedding the information because of its intrinsic benefits. We have implemented our suggested technique using MATLAB. The analysis of results on the prespecified parameters of the steganography justifies the effectiveness of the proposed technique.
{"title":"An ROI-based robust video steganography technique using SVD in wavelet domain","authors":"Urmila Pilania, Rohit Tanwar, Prinima Gupta","doi":"10.1515/comp-2020-0229","DOIUrl":"https://doi.org/10.1515/comp-2020-0229","url":null,"abstract":"Abstract Steganography is a technique that embeds secret information in a suitable cover file such as text, image, audio, and video in such a manner that secret information remains invisible to the outside world. The study of the literature relevant to video steganography reveals that a tradeoff exists in attaining the acceptable values of various evaluation parameters such as a higher capacity usually results in lesser robustness or imperceptibility. In this article, we propose a technique that achieves high capacity along with required robustness. The embedding capacity is increased using singular value decomposition compression. To achieve the desired robustness, we constrain the embedding of the secret message in the region of interest in the cover video file. In this manner, we also succeed in maintaining the required imperceptibility. We prefer Haar-based lifting scheme in the wavelet domain for embedding the information because of its intrinsic benefits. We have implemented our suggested technique using MATLAB. The analysis of results on the prespecified parameters of the steganography justifies the effectiveness of the proposed technique.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43224766","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}
Abstract With the high incidence of the dengue epidemic in developing countries, it is crucial to understand its dynamics from a holistic perspective. This paper analyzes different types of antecedents from a cybernetics perspective using a structural modelling approach. The novelty of this paper is twofold. First, it analyzes antecedents that may be social, institutional, environmental, or economic in nature. Since this type of study has not been done in the context of the dengue epidemic modelling, this paper offers a fresh perspective on this topic. Second, the paper pioneers the use of fuzzy multiple attribute decision making (F-MADM) approaches for the modelling of epidemic antecedents. As such, the paper has provided an avenue for the cross-fertilization of knowledge between scholars working in soft computing and epidemiological modelling domains.
{"title":"Modelling the interdependent relationships among epidemic antecedents using fuzzy multiple attribute decision making (F-MADM) approaches","authors":"Dharyll Prince M. Abellana","doi":"10.1515/comp-2020-0213","DOIUrl":"https://doi.org/10.1515/comp-2020-0213","url":null,"abstract":"Abstract With the high incidence of the dengue epidemic in developing countries, it is crucial to understand its dynamics from a holistic perspective. This paper analyzes different types of antecedents from a cybernetics perspective using a structural modelling approach. The novelty of this paper is twofold. First, it analyzes antecedents that may be social, institutional, environmental, or economic in nature. Since this type of study has not been done in the context of the dengue epidemic modelling, this paper offers a fresh perspective on this topic. Second, the paper pioneers the use of fuzzy multiple attribute decision making (F-MADM) approaches for the modelling of epidemic antecedents. As such, the paper has provided an avenue for the cross-fertilization of knowledge between scholars working in soft computing and epidemiological modelling domains.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/comp-2020-0213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49526661","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}
George Papageorgiou, A. Ioannou, Athanasios Maimaris, Alexander N. Ness
Abstract Information and Communication Technology (ICT), and recent advancements in Computer Science can serve as a catalyst for promoting sustainable means of transport. Through ICT applications, active mobility can be promoted and established as a viable transport mode. This can be achieved by providing relevant information for fostering social capital and promoting physical activity, thus contributing to a higher quality of life. Further, active mobility can greatly contribute to reducing air pollution and improving health status. For this purpose, the implementation of a Smart Pedestrian Network (SPN) information system is proposed. Such an implementation requires the collaboration of various stakeholders including the public, local authorities and local businesses. To convince stake-holders of the viability of implementing SPN, the benefits of active mobility should be clear. This paper proposes a framework to quantify active mobility benefits so that stake-holders can assess the investment that can be realized from implementing SPN. The proposed framework makes use of quantifying benefits in various market conditions. The benefits are shown to be significant and very much in favor of investing in technology and implementing the envisioned SPN system.
{"title":"Evaluation of the Benefits of Implementing a Smart Pedestrian Network System","authors":"George Papageorgiou, A. Ioannou, Athanasios Maimaris, Alexander N. Ness","doi":"10.1515/comp-2020-0127","DOIUrl":"https://doi.org/10.1515/comp-2020-0127","url":null,"abstract":"Abstract Information and Communication Technology (ICT), and recent advancements in Computer Science can serve as a catalyst for promoting sustainable means of transport. Through ICT applications, active mobility can be promoted and established as a viable transport mode. This can be achieved by providing relevant information for fostering social capital and promoting physical activity, thus contributing to a higher quality of life. Further, active mobility can greatly contribute to reducing air pollution and improving health status. For this purpose, the implementation of a Smart Pedestrian Network (SPN) information system is proposed. Such an implementation requires the collaboration of various stakeholders including the public, local authorities and local businesses. To convince stake-holders of the viability of implementing SPN, the benefits of active mobility should be clear. This paper proposes a framework to quantify active mobility benefits so that stake-holders can assess the investment that can be realized from implementing SPN. The proposed framework makes use of quantifying benefits in various market conditions. The benefits are shown to be significant and very much in favor of investing in technology and implementing the envisioned SPN system.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/comp-2020-0127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42404027","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}
Abstract Nowadays, in real-world applications, the dimensions of data are generated dynamically, and the traditional batch feature selection methods are not suitable for streaming data. So, online streaming feature selection methods gained more attention but the existing methods had demerits like low classification accuracy, fails to avoid redundant and irrelevant features, and a higher number of features selected. In this paper, we propose a parallel online feature selection method using multiple sliding-windows and fuzzy fast-mRMR feature selection analysis, which is used for selecting minimum redundant and maximum relevant features, and also overcomes the drawbacks of existing online streaming feature selection methods. To increase the performance speed of the proposed method parallel processing is used. To evaluate the performance of the proposed online feature selection method k-NN, SVM, and Decision Tree Classifiers are used and compared against the state-of-the-art online feature selection methods. Evaluation metrics like Accuracy, Precision, Recall, F1-Score are used on benchmark datasets for performance analysis. From the experimental analysis, it is proved that the proposed method has achieved more than 95% accuracy for most of the datasets and performs well over other existing online streaming feature selection methods and also, overcomes the drawbacks of the existing methods.
{"title":"Fuzzy Rank Based Parallel Online Feature Selection Method using Multiple Sliding Windows","authors":"B. Venkatesh, J. Anuradha","doi":"10.1515/comp-2020-0169","DOIUrl":"https://doi.org/10.1515/comp-2020-0169","url":null,"abstract":"Abstract Nowadays, in real-world applications, the dimensions of data are generated dynamically, and the traditional batch feature selection methods are not suitable for streaming data. So, online streaming feature selection methods gained more attention but the existing methods had demerits like low classification accuracy, fails to avoid redundant and irrelevant features, and a higher number of features selected. In this paper, we propose a parallel online feature selection method using multiple sliding-windows and fuzzy fast-mRMR feature selection analysis, which is used for selecting minimum redundant and maximum relevant features, and also overcomes the drawbacks of existing online streaming feature selection methods. To increase the performance speed of the proposed method parallel processing is used. To evaluate the performance of the proposed online feature selection method k-NN, SVM, and Decision Tree Classifiers are used and compared against the state-of-the-art online feature selection methods. Evaluation metrics like Accuracy, Precision, Recall, F1-Score are used on benchmark datasets for performance analysis. From the experimental analysis, it is proved that the proposed method has achieved more than 95% accuracy for most of the datasets and performs well over other existing online streaming feature selection methods and also, overcomes the drawbacks of the existing methods.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/comp-2020-0169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46106542","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}
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：