With the advent and development of database applications such as big data and data mining, how to ensure the availability of data without revealing sensitive information has been a significant problem for database privacy protection. As a critical technology to solve this problem, homomorphic encryption has become a hot research area in information security at home and abroad in recent years. The paper sorted out, analyzed, and summarized the research progress of homomorphic encryption technology in database privacy protection. Moreover, the application of three different types of homomorphic encryption technology in database privacy protection was introduced respectively, and the rationale and characteristics of each technique were analyzed and explained. Ultimately, this research summarized the challenges and development trends of homomorphic encryption technology in the application of database privacy protection, which provides a reference for future research.
{"title":"Research Review on the Application of Homomorphic Encryption in Database Privacy Protection","authors":"Yong Ma, Jiale Zhao, Kangshun Li, Yuanlong Cao, Huyuan Chen, Youcheng Zhang","doi":"10.4018/ijcini.287600","DOIUrl":"https://doi.org/10.4018/ijcini.287600","url":null,"abstract":"With the advent and development of database applications such as big data and data mining, how to ensure the availability of data without revealing sensitive information has been a significant problem for database privacy protection. As a critical technology to solve this problem, homomorphic encryption has become a hot research area in information security at home and abroad in recent years. The paper sorted out, analyzed, and summarized the research progress of homomorphic encryption technology in database privacy protection. Moreover, the application of three different types of homomorphic encryption technology in database privacy protection was introduced respectively, and the rationale and characteristics of each technique were analyzed and explained. Ultimately, this research summarized the challenges and development trends of homomorphic encryption technology in the application of database privacy protection, which provides a reference for future research.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"3 1","pages":"1-22"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83865012","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 : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA3
Shruthi Komarla Rammurthy, S. B. Patil
{"title":"An LSTM-Based Approach to Predict Stock Price Movement for IT Sector Companies","authors":"Shruthi Komarla Rammurthy, S. B. Patil","doi":"10.4018/IJCINI.20211001.OA3","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA3","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"27 1","pages":"1-12"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74008958","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 : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA22
Junjie Bai, Jun Peng, Dedong Tang, Zuojin Li, Kan Luo, Jianxing Li, Xue Zhang
Using thermal tactile sensing mechanism based on semi-infinite body model, and combining with the advantages of maximum proportional controller, fuzzy and PID controller, a thermal tactile perception and reproduction experiment device (TTPRED) was designed based on the composite control strategy of threshold switching. The finger difference threshold measurement experiment of thermal tactile was carried out, and the finger thermal tactile difference threshold was measured. The relationship between thermal tactile sensation and emotion based on temperature cues has been explored. The experiment results show that the temperature control range of TTPRED is from -10°C to 130°C, the temperature resolution and precision are 0.01°C and ±0.1°C respectively, the maximum heating or cooling rate is greater than 12°C, and the TTPRED can realize the temperature output of the specific waveform quickly and accurately. The experiment results of psychophysical experiment will provide the experimental foundations and technical support for the further study of thermal tactile perception and reproduction.
{"title":"Thermal Tactile Perception: Device, Technology, and Experiments","authors":"Junjie Bai, Jun Peng, Dedong Tang, Zuojin Li, Kan Luo, Jianxing Li, Xue Zhang","doi":"10.4018/IJCINI.20211001.OA22","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA22","url":null,"abstract":"Using thermal tactile sensing mechanism based on semi-infinite body model, and combining with the advantages of maximum proportional controller, fuzzy and PID controller, a thermal tactile perception and reproduction experiment device (TTPRED) was designed based on the composite control strategy of threshold switching. The finger difference threshold measurement experiment of thermal tactile was carried out, and the finger thermal tactile difference threshold was measured. The relationship between thermal tactile sensation and emotion based on temperature cues has been explored. The experiment results show that the temperature control range of TTPRED is from -10°C to 130°C, the temperature resolution and precision are 0.01°C and ±0.1°C respectively, the maximum heating or cooling rate is greater than 12°C, and the TTPRED can realize the temperature output of the specific waveform quickly and accurately. The experiment results of psychophysical experiment will provide the experimental foundations and technical support for the further study of thermal tactile perception and reproduction.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"139 1","pages":"1-19"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77296178","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}
According to the psychology literature, there is a strong correlation between the personality traits and the linguistic behavior of people. Due to increase in computer based communication, individuals express their personalities in written forms on social media. Hence, social media became a convenient resource to analyze the relationship between the personality traits and the lingusitic behaviour. Although there is a vast amount of studies on social media, only a small number of them focus on personality prediction. In this work, we aim to model the relationship between the social media messages of individuals and Big Five Personality Traits as a supervised learning problem. We use Twitter posts and user statistics for analysis. We investigated various approaches for user profile representation, explored several supervised learning techniques, and presented comparative analysis results. Our results confirm the findings of psychology literature, and we show that computational analysis of tweets using supervised learning methods can be used to determine the personality of individuals.
{"title":"Personality Analysis Using Classification on Turkish Tweets","authors":"G. Mavis, I. H. Toroslu, P. Senkul","doi":"10.4018/ijcini.287596","DOIUrl":"https://doi.org/10.4018/ijcini.287596","url":null,"abstract":"According to the psychology literature, there is a strong correlation between the personality traits and the linguistic behavior of people. Due to increase in computer based communication, individuals express their personalities in written forms on social media. Hence, social media became a convenient resource to analyze the relationship between the personality traits and the lingusitic behaviour. Although there is a vast amount of studies on social media, only a small number of them focus on personality prediction. In this work, we aim to model the relationship between the social media messages of individuals and Big Five Personality Traits as a supervised learning problem. We use Twitter posts and user statistics for analysis. We investigated various approaches for user profile representation, explored several supervised learning techniques, and presented comparative analysis results. Our results confirm the findings of psychology literature, and we show that computational analysis of tweets using supervised learning methods can be used to determine the personality of individuals.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"29 1","pages":"1-18"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89938083","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 : 2021-10-01DOI: 10.4018/ijcini.20211001.oa21
A. Arpitha, Lalitha Rangarajan
The primary goal in this paper is to automate radiological measurements of Vertebral Body (VB) in Magnetic Resonance Imaging (MRI) spinal scans. It starts by preprocessing the images, then detect and localize the VB regions, next segment and label VBs and finally classify each VB into three cases as being normal or fractured in case 1, benign or malignant in case 2 and normal, benign or malignant in case 3. The task is accomplished by extracting and combining distinct features of VB such as boundary, gray levels, shape and texture features using various Machine Learning techniques. The class balance deficit dataset towards normal and fractures is balanced by data augmentation which provides an enriched dataset for the learning system to perform precise differentiation between classes. On a clinical spine dataset, the method is tested and validated on 535 VBs for segmentation attaining an average accuracy 94.59% and on 315 VBs for classification with an average accuracy of 96.07% for case 1, 93.23% for case 2 and 92.3% for case 3.
{"title":"Computational Analysis of Vertebral Body for Compression Fracture Using Texture and Shape Features","authors":"A. Arpitha, Lalitha Rangarajan","doi":"10.4018/ijcini.20211001.oa21","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa21","url":null,"abstract":"The primary goal in this paper is to automate radiological measurements of Vertebral Body (VB) in Magnetic Resonance Imaging (MRI) spinal scans. It starts by preprocessing the images, then detect and localize the VB regions, next segment and label VBs and finally classify each VB into three cases as being normal or fractured in case 1, benign or malignant in case 2 and normal, benign or malignant in case 3. The task is accomplished by extracting and combining distinct features of VB such as boundary, gray levels, shape and texture features using various Machine Learning techniques. The class balance deficit dataset towards normal and fractures is balanced by data augmentation which provides an enriched dataset for the learning system to perform precise differentiation between classes. On a clinical spine dataset, the method is tested and validated on 535 VBs for segmentation attaining an average accuracy 94.59% and on 315 VBs for classification with an average accuracy of 96.07% for case 1, 93.23% for case 2 and 92.3% for case 3.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"3 1","pages":"1-24"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78731465","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}
Firefly algorithm is a meta-heuristic stochastic search algorithm with strong robustness and easy implementation. However, it also has some shortcomings, such as the “oscillation” phenomenon caused by too many attractions, which makes the convergence speed too slow or premature. In the original FA, the full attraction model makes the algorithm consume a lot of evaluation times, and the time complexity is high. Therefore, in this paper, a novel firefly algorithm (EMDmFA) based on Euclidean metric (EM) and dimensional mutation (DM) is proposed. The EM strategy makes the firefly learn from its nearest neighbors. When the firefly is better than its neighbors, it learns from the best individuals in the population. It improves the FA attraction model and dramatically reduces the computational time complexity. At the same time, DM strategy improves the ability of the algorithm to jump out of the local optimum. The experimental results show that the proposed EMDmFA significantly improves the accuracy of the solution and better than most state-of-the-art FA variants.
{"title":"Firefly Algorithm Based on Euclidean Metric and Dimensional Mutation","authors":"Jing Wang, Yanfeng Ji","doi":"10.4018/IJCINI.286769","DOIUrl":"https://doi.org/10.4018/IJCINI.286769","url":null,"abstract":"Firefly algorithm is a meta-heuristic stochastic search algorithm with strong robustness and easy implementation. However, it also has some shortcomings, such as the “oscillation” phenomenon caused by too many attractions, which makes the convergence speed too slow or premature. In the original FA, the full attraction model makes the algorithm consume a lot of evaluation times, and the time complexity is high. Therefore, in this paper, a novel firefly algorithm (EMDmFA) based on Euclidean metric (EM) and dimensional mutation (DM) is proposed. The EM strategy makes the firefly learn from its nearest neighbors. When the firefly is better than its neighbors, it learns from the best individuals in the population. It improves the FA attraction model and dramatically reduces the computational time complexity. At the same time, DM strategy improves the ability of the algorithm to jump out of the local optimum. The experimental results show that the proposed EMDmFA significantly improves the accuracy of the solution and better than most state-of-the-art FA variants.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"78 1","pages":"1-19"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78087792","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}
{"title":"Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware","authors":"Winfred Yaokumah, J. K. Appati, D. Kumah","doi":"10.4018/IJCINI.286768","DOIUrl":"https://doi.org/10.4018/IJCINI.286768","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"42 1","pages":"1-18"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91182730","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 : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA46
G. Siddesh, T. Gururaj
{"title":"Classification of Gene Expression Data Using Feature Selection Based on Type Combination Approach Model With Advanced Feature Selection Technology","authors":"G. Siddesh, T. Gururaj","doi":"10.4018/IJCINI.20211001.OA46","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA46","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"264 1","pages":"1-18"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77774486","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 : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA31
Bo Wei, Ying Xing, Xuewen Xia, Ling Gui
{"title":"A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP","authors":"Bo Wei, Ying Xing, Xuewen Xia, Ling Gui","doi":"10.4018/IJCINI.20211001.OA31","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA31","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"2 1","pages":"1-17"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76298254","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}
To overcome the shortcomings of the standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, the whole population is divided into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. The dynamic sub-swarms achieve information interaction and sharing among themselves through the randomly regrouping strategy. The global sub-swarm evolves independently and learns from the optimal individuals of the dynamic sub-swarm with dominant characteristics. During the evolution process of the population, the variances and average fitness values of dynamic sub-swarms are used for measuring the distribution of the particles, by which the dominant one and the optimal individual can be detected easily. The comparison results among DMS-PSO-GD and other 5 well-known algorithms suggest that it demonstrates superior performance for solving different types of functions.
{"title":"A Dynamic Multi-Swarm Particle Swarm Optimization With Global Detection Mechanism","authors":"Bo Wei, Yichao Tang, Xiao Jin, Mingfeng Jiang, Zuohua Ding, Yanrong Huang","doi":"10.4018/ijcini.294566","DOIUrl":"https://doi.org/10.4018/ijcini.294566","url":null,"abstract":"To overcome the shortcomings of the standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, the whole population is divided into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. The dynamic sub-swarms achieve information interaction and sharing among themselves through the randomly regrouping strategy. The global sub-swarm evolves independently and learns from the optimal individuals of the dynamic sub-swarm with dominant characteristics. During the evolution process of the population, the variances and average fitness values of dynamic sub-swarms are used for measuring the distribution of the particles, by which the dominant one and the optimal individual can be detected easily. The comparison results among DMS-PSO-GD and other 5 well-known algorithms suggest that it demonstrates superior performance for solving different types of functions.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"52 1","pages":"1-23"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81126710","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：