Open Computer Science最新文献_第6页

Post-quantum cryptography-driven security framework for cloud computing 后量子密码学驱动的云计算安全框架

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0235

H. C. Ukwuoma, A. J. Gabriel, A. Thompson, B. Alese

{"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}

引用次数: 3

Big data network security defense mode of deep learning algorithm 大数据网络安全防御模式的深度学习算法

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0257

Ying Yu

引用次数: 1

Research on the virtual simulation experiment evaluation model of e-commerce logistics smart warehousing based on multidimensional weighting 基于多维加权的电子商务物流智能仓储虚拟仿真实验评价模型研究

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0249

Ganglong Fan, Bo Fan, Hongsheng Xu, Chuqiao Wang

{"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}

引用次数: 0

Rough set-based entropy measure with weighted density outlier detection method 基于粗糙集的熵测度与加权密度离群点检测方法

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2020-0228

T. Sangeetha, Geetha Mary Amalanathan

{"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}

引用次数: 3

A method for detecting objects in dense scenes 一种在密集场景中检测物体的方法

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0231

Chuanyun Xu, Yueping Zheng, Yang Zhang, Gang Li, Ying Wang

引用次数: 2

Mass data processing and multidimensional database management based on deep learning 基于深度学习的海量数据处理和多维数据库管理

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0251

Haijie Shen, Y. Li, Xinzhi Tian, Xiaofan Chen, Caihong Li, Qian Bian, Zhenduo Wang, Weihua Wang

{"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}

引用次数: 2

An ROI-based robust video steganography technique using SVD in wavelet domain 基于ROI的小波域SVD鲁棒视频隐写技术

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2020-0229

Urmila Pilania, Rohit Tanwar, Prinima Gupta

引用次数: 1

Modelling the interdependent relationships among epidemic antecedents using fuzzy multiple attribute decision making (F-MADM) approaches 使用模糊多属性决策方法（F-MADM）建模流行病前因之间的相互依赖关系

IF 1.5

Open Computer Science Pub Date : 2021-01-01 DOI: 10.1515/comp-2020-0213

Dharyll Prince M. Abellana

引用次数: 3

Evaluation of the Benefits of Implementing a Smart Pedestrian Network System 实施智能行人网络系统的效益评估

IF 1.5

Open Computer Science Pub Date : 2021-01-01 DOI: 10.1515/comp-2020-0127

George Papageorgiou, A. Ioannou, Athanasios Maimaris, Alexander N. Ness

{"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}

引用次数: 0

Fuzzy Rank Based Parallel Online Feature Selection Method using Multiple Sliding Windows 基于模糊秩的多滑动窗口并行在线特征选择方法

IF 1.5

Open Computer Science Pub Date : 2021-01-01 DOI: 10.1515/comp-2020-0169

B. Venkatesh, J. Anuradha

{"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}

引用次数: 3