Journal of Supercomputing最新文献_第9页

Stock exchange trading optimization algorithm: a human-inspired method for global optimization. 证券交易所交易优化算法:一种人类启发的全局优化方法。

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2022-01-01 DOI: 10.1007/s11227-021-03943-w

Hojjat Emami

{"title":"Stock exchange trading optimization algorithm: a human-inspired method for global optimization.","authors":"Hojjat Emami","doi":"10.1007/s11227-021-03943-w","DOIUrl":"https://doi.org/10.1007/s11227-021-03943-w","url":null,"abstract":"In this paper, a human-inspired optimization algorithm called stock exchange trading optimization (SETO) for solving numerical and engineering problems is introduced. The inspiration source of this optimizer is the behavior of traders and stock price changes in the stock market. Traders use various fundamental and technical analysis methods to gain maximum profit. SETO mathematically models the technical trading strategy of traders to perform optimization. It contains three main actuators including rising, falling, and exchange. These operators navigate the search agents toward the global optimum. The proposed algorithm is compared with seven popular meta-heuristic optimizers on forty single-objective unconstraint numerical functions and four engineering design problems. The statistical results obtained on test problems show that SETO is capable of providing competitive and promising performances compared with counterpart algorithms in solving optimization problems of different dimensions, especially 1000-dimension problems. Out of 40 numerical functions, the SETO algorithm has achieved the global optimum on 36 functions, and out of 4 engineering problems, it has obtained the best results on 3 problems.","PeriodicalId":50034,"journal":{"name":"Journal of Supercomputing","volume":"78 2","pages":"2125-2174"},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11227-021-03943-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10654294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intelligent malware detection based on graph convolutional network. 基于图卷积网络的恶意软件智能检测。

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2022-01-01 Epub Date: 2021-08-24 DOI: 10.1007/s11227-021-04020-y

Shanxi Li, Qingguo Zhou, Rui Zhou, Qingquan Lv

{"title":"Intelligent malware detection based on graph convolutional network.","authors":"Shanxi Li, Qingguo Zhou, Rui Zhou, Qingquan Lv","doi":"10.1007/s11227-021-04020-y","DOIUrl":"https://doi.org/10.1007/s11227-021-04020-y","url":null,"abstract":"Malware has seriously threatened the safety of computer systems for a long time. Due to the rapid development of anti-detection technology, traditional detection methods based on static analysis and dynamic analysis have limited effects. With its better predictive performance, AI-based malware detection has been increasingly used to deal with malware in recent years. However, due to the diversity of malware, it is difficult to extract feature from malware, which make malware detection not conductive to the application of AI technology. To solve the problem, a malware classifier based on graph convolutional network is designed to adapt to the difference of malware characteristics. The specific method is to firstly extract the API call sequence from the malware code and generate a directed cycle graph, then use the Markov chain and principal component analysis method to extract the feature map of the graph, and design a classifier based on graph convolutional network, and finally analyze and compare the performance of the method. The results show that the method has better performance in most detection, and the highest accuracy is <math><mrow><mn>98.32</mn> <mo>%</mo></mrow> </math> , compared with existing methods, our model is superior to other methods in terms of FPR and accuracy. It is also stable to deal with the development and growth of malware.","PeriodicalId":50034,"journal":{"name":"Journal of Supercomputing","volume":"78 3","pages":"4182-4198"},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11227-021-04020-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39364630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Impact of second-order network motif on online social networks. 二阶网络母题对在线社交网络的影响。

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2022-01-01 Epub Date: 2021-09-24 DOI: 10.1007/s11227-021-04079-7

Sankhamita Sinha, Subhayan Bhattacharya, Sarbani Roy

{"title":"Impact of second-order network motif on online social networks.","authors":"Sankhamita Sinha, Subhayan Bhattacharya, Sarbani Roy","doi":"10.1007/s11227-021-04079-7","DOIUrl":"https://doi.org/10.1007/s11227-021-04079-7","url":null,"abstract":"The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.","PeriodicalId":50034,"journal":{"name":"Journal of Supercomputing","volume":"78 4","pages":"5450-5478"},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8461152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39467272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Innate immune memory and its application to artificial immune systems. 先天免疫记忆及其在人工免疫系统中的应用。

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2022-01-01 Epub Date: 2022-02-16 DOI: 10.1007/s11227-021-04295-1

Dongmei Wang, Yiwen Liang, Hongbin Dong, Chengyu Tan, Zhenhua Xiao, Sai Liu

{"title":"Innate immune memory and its application to artificial immune systems.","authors":"Dongmei Wang, Yiwen Liang, Hongbin Dong, Chengyu Tan, Zhenhua Xiao, Sai Liu","doi":"10.1007/s11227-021-04295-1","DOIUrl":"https://doi.org/10.1007/s11227-021-04295-1","url":null,"abstract":"The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either manually pre-defined usually provided by the immunologists, or empirically derived from the training dataset, and result in poor self-adaptation and self-learning. The fundamental reason is that the original innate immune mechanisms lack adaptive biological theory. To solve this problem, a theory called â€˜Trained Immunity™ or Innate Immune Memory (IIM)™ that thinks innate immunity can also build immunological memory to enhance the immune system™s learning and adaptive reactions to the second stimulus is introduced into AIS to improve the innate immune algorithms™ adaptability. In this study, we present an overview of IIM with particular emphasis on analogies in the AIS world, and a modified DCA with an effective automated tuning mechanism based on IIM (IIM-DCA) to optimize migration threshold of DCA. The migration threshold of Dendritic Cells (DCs) determines the lifespan of the antigen collected by DCs, and directly affect the detection speed and accuracy of DCA. Experiments on real datasets show that our proposed IIM-DCA which integrates Innate Immune Memory mechanism delivers more accurate results.","PeriodicalId":50034,"journal":{"name":"Journal of Supercomputing","volume":"78 9","pages":"11680-11701"},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8852961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39642080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A cloud-based mobile payment system using identity-based signature providing key revocation 一种基于云的移动支付系统，使用基于身份的签名提供密钥撤销

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2021-07-01 DOI: 10.1007/S11227-021-03830-4

Fatemeh Alidadi Shamsabadi, S. B. Chehelcheshmeh

引用次数: 1

$hbox {E}^{2}hbox {M}^{3}$: energy-efficient massive MIMO-MISO 5G HetNet using Stackelberg game $hbox {E}^{2}hbox {M}^{3}$:节能大规模MIMO-MISO 5G HetNet使用Stackelberg游戏

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2021-04-28 DOI: 10.1007/S11227-021-03809-1

Subhankar Ghosh, D. De

引用次数: 6

Multi-criteria decision-making for controller placement in software-defined wide-area networks 软件定义广域网中控制器布局的多准则决策

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2021-04-26 DOI: 10.1007/S11227-021-03815-3

A. A. Seyedkolaei, S. Hosseini-Seno

引用次数: 4

An efficient query optimization technique in big data using σ-ANFIS load balancer and CaM-BW optimizer 基于σ-ANFIS负载均衡器和CaM-BW优化器的大数据查询优化技术

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2021-04-19 DOI: 10.1007/S11227-021-03793-6

D. Kumar, V. Jha

引用次数: 2

Analysis of blockchain system based on $hbox {M}/(hbox {M}_1, hbox {M}_2)/1$ vacation queueing model 基于$hbox {M}/(hbox {M}_1， hbox {M}_2)/1$假期排队模型的区块链系统分析

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2021-04-01 DOI: 10.1007/s11227-020-03408-6

Jiaqi Fan, Zhanyou Ma, Yang Zhang, Changzhen Zhang

引用次数: 2

N-version of the neutrosophic cubic set: application in the negative influences of Internet 中性立方集的n -版本:在互联网负面影响中的应用

IF 3.3 3区计算机科学

Journal of Supercomputing Pub Date : 2021-03-24 DOI: 10.1007/S11227-020-03615-1

M. Gulistan, A. Elmoasry, N. Yaqoob

引用次数: 4