Meetings are one of the most common collaboration formats for complex problem-solving (CPS). This research aims to formulate cognitive-oriented guidelines for productive synchronous CPS discussions. The study proposes a method to analyze the cognitive process and identifies the cognitive process associated with better CPS discussions. A conversation-analysis method was developed. Two indicators—source–outcome retrieval ratio and count of overlapped solution utterances—were proposed to evaluate the CPS discussion’s efficiency and effectiveness. Sixteen experimental CPS discussions were analyzed using this method. Correlation coefficients were applied to ascertain the cognitive features in CPS discussions with different levels of effectiveness and confirmed the applicability and reliability of the proposed methods. The results revealed that a good CPS discussion includes a regular progress summary, discussion conclusion, and high utilization of cognitive sources.
{"title":"An Empirical Investigation of the Underlying Cognitive Process in Complex Problem Solving: A Proposal of Problem-Solving Discussion Performance Evaluation Methods","authors":"Yingting Chen, T. Kanno, K. Furuta","doi":"10.4018/ijcini.301204","DOIUrl":"https://doi.org/10.4018/ijcini.301204","url":null,"abstract":"Meetings are one of the most common collaboration formats for complex problem-solving (CPS). This research aims to formulate cognitive-oriented guidelines for productive synchronous CPS discussions. The study proposes a method to analyze the cognitive process and identifies the cognitive process associated with better CPS discussions. A conversation-analysis method was developed. Two indicators—source–outcome retrieval ratio and count of overlapped solution utterances—were proposed to evaluate the CPS discussion’s efficiency and effectiveness. Sixteen experimental CPS discussions were analyzed using this method. Correlation coefficients were applied to ascertain the cognitive features in CPS discussions with different levels of effectiveness and confirmed the applicability and reliability of the proposed methods. The results revealed that a good CPS discussion includes a regular progress summary, discussion conclusion, and high utilization of cognitive sources.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"15 1","pages":"1-25"},"PeriodicalIF":0.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88881520","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}
Computational models of emotion (CMEs) are software systems designed to emulate specific aspects of the human emotions process. The underlying components of CMEs interact with cognitive components of cognitive agent architectures to produce realistic behaviors in intelligent agents. However, in contemporary CMEs, the interaction between affective and cognitive components occurs in ad-hoc manner, which leads to difficulties when new affective or cognitive components should be added in the CME. This paper presents a framework that facilitates taking into account in CMEs the cognitive information generated by cognitive components implemented in cognitive agent architectures. The framework is designed to allow researchers define how cognitive information biases the internal workings of affective components. This framework is inspired in software interoperability practices to enable communication and interpretation of cognitive information and standardize the cognitive-affective communication process by ensuring semantic communication channels used to modulate affective mechanisms of CMEs
{"title":"An Interoperable Framework for Computational Models of Emotion","authors":"Enrique Osuna, Sergio Castellanos, Jonathan-Hernando Rosales, Luis-Felipe Rodríguez","doi":"10.4018/ijcini.296257","DOIUrl":"https://doi.org/10.4018/ijcini.296257","url":null,"abstract":"Computational models of emotion (CMEs) are software systems designed to emulate specific aspects of the human emotions process. The underlying components of CMEs interact with cognitive components of cognitive agent architectures to produce realistic behaviors in intelligent agents. However, in contemporary CMEs, the interaction between affective and cognitive components occurs in ad-hoc manner, which leads to difficulties when new affective or cognitive components should be added in the CME. This paper presents a framework that facilitates taking into account in CMEs the cognitive information generated by cognitive components implemented in cognitive agent architectures. The framework is designed to allow researchers define how cognitive information biases the internal workings of affective components. This framework is inspired in software interoperability practices to enable communication and interpretation of cognitive information and standardize the cognitive-affective communication process by ensuring semantic communication channels used to modulate affective mechanisms of CMEs","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"2 1","pages":"1-15"},"PeriodicalIF":0.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78523279","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.OA24
Jun Peng, Shangzhu Jin, Shaoning Pang, Du Zhang, Lixiao Feng, Zuojin Li, Yingxu Wang
For a security system built on symmetric-key cryptography algorithms, the substitution box (S-box) plays a crucial role to resist cryptanalysis. This article incorporates quantum chaos and PWLCM chaotic map into a new method of S-box design. The secret key is transformed to generate a sextuple system parameter, which is involved in the generation process of chaotic sequences of two chaotic systems. The output of one chaotic system will disturb the parameters of another chaotic system in order to improve the complexity of encryption sequence. S-box is obtained by XOR operation of the output of two chaotic systems. Over the obtained 500 key-dependent S-boxes, the authors test the S-box cryptographical properties on bijection, nonlinearity, SAC, BIC, differential approximation probability, respectively. Performance comparison of proposed S-box with those chaos-based one in the literature has been made. The results show that the cryptographic characteristics of proposed S-box has met the design objectives and can be applied to data encryption, user authentication and system access control.
{"title":"S-Box Construction Method Based on the Combination of Quantum Chaos and PWLCM Chaotic Map","authors":"Jun Peng, Shangzhu Jin, Shaoning Pang, Du Zhang, Lixiao Feng, Zuojin Li, Yingxu Wang","doi":"10.4018/IJCINI.20211001.OA24","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA24","url":null,"abstract":"For a security system built on symmetric-key cryptography algorithms, the substitution box (S-box) plays a crucial role to resist cryptanalysis. This article incorporates quantum chaos and PWLCM chaotic map into a new method of S-box design. The secret key is transformed to generate a sextuple system parameter, which is involved in the generation process of chaotic sequences of two chaotic systems. The output of one chaotic system will disturb the parameters of another chaotic system in order to improve the complexity of encryption sequence. S-box is obtained by XOR operation of the output of two chaotic systems. Over the obtained 500 key-dependent S-boxes, the authors test the S-box cryptographical properties on bijection, nonlinearity, SAC, BIC, differential approximation probability, respectively. Performance comparison of proposed S-box with those chaos-based one in the literature has been made. The results show that the cryptographic characteristics of proposed S-box has met the design objectives and can be applied to data encryption, user authentication and system access control.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"38 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":"86833717","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.OA36
Baranidharan Balakrishnan, C. Kumar
Cardio vascular diseases (CVD) are the major reason for the death of the majority of the people in the world. Earlier diagnosis of disease will reduce the mortality rate. Machine learning (ML) algorithms are giving promising results in the disease diagnosis, and they are now widely accepted by medical experts as their clinical decision support system. In this work, the most popular ML models are investigated and compared with one other for heart disease prediction based on various metrics. The base classifiers such as support vector machine (SVM), logistic regression, naïve Bayes, decision tree, k-nearest neighbour are used for predicting heart disease. In this paper, bagging and boosting techniques are applied over these individual classifiers to improve the performance of the system. With the Cleveland and Statlog datasets, naive Bayes as the individual classifier gives the maximum accuracy of 85.13%and 84.81%, respectively. Bagging technique improves the accuracy of the decision tree, which is identified as a weak classifier by 7%, and it is a significant improvement in identifying CVD. that Bayes, Support Vector Machine and Logistic are strong classifiers more than 80% accuracy and Decision Tree and K Nearest Neighbours as weak classifiers. Bagging and boosting techniques the performance of weak classifiers Decision Tree and K Nearest Neighbours. Bagging technique improved the accuracy of the decision tree algorithm 7.77% maximum for Statlog dataset. In future, feature selection is to be applied to find out the most relevant features of the data set and applying over the ensemble models over it will give better-improved accuracy.
{"title":"A Comprehensive Performance Analysis of Various Classifier Models for Coronary Artery Disease Prediction","authors":"Baranidharan Balakrishnan, C. Kumar","doi":"10.4018/IJCINI.20211001.OA36","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA36","url":null,"abstract":"Cardio vascular diseases (CVD) are the major reason for the death of the majority of the people in the world. Earlier diagnosis of disease will reduce the mortality rate. Machine learning (ML) algorithms are giving promising results in the disease diagnosis, and they are now widely accepted by medical experts as their clinical decision support system. In this work, the most popular ML models are investigated and compared with one other for heart disease prediction based on various metrics. The base classifiers such as support vector machine (SVM), logistic regression, naïve Bayes, decision tree, k-nearest neighbour are used for predicting heart disease. In this paper, bagging and boosting techniques are applied over these individual classifiers to improve the performance of the system. With the Cleveland and Statlog datasets, naive Bayes as the individual classifier gives the maximum accuracy of 85.13%and 84.81%, respectively. Bagging technique improves the accuracy of the decision tree, which is identified as a weak classifier by 7%, and it is a significant improvement in identifying CVD. that Bayes, Support Vector Machine and Logistic are strong classifiers more than 80% accuracy and Decision Tree and K Nearest Neighbours as weak classifiers. Bagging and boosting techniques the performance of weak classifiers Decision Tree and K Nearest Neighbours. Bagging technique improved the accuracy of the decision tree algorithm 7.77% maximum for Statlog dataset. In future, feature selection is to be applied to find out the most relevant features of the data set and applying over the ensemble models over it will give better-improved accuracy.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"43 1","pages":"1-14"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80090038","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.OA33
H Hadjadj, H. Sayoud
Dealing with imbalanced data represents a great challenge in data mining as well as in machine learning task. In this investigation, the authors are interested in the problem of class imbalance in authorship attribution (AA) task, with specific application on Arabic text data. This article proposes a new hybrid approach based on principal components analysis (PCA) and synthetic minority over-sampling technique (SMOTE), which considerably improve the performances of authorship attribution on imbalanced data. The used dataset contains seven Arabic books written by seven different scholars, which are segmented into text segments of the same size, with an average length of 2,900 words per text. The obtained results of the experiments show that the proposed approach using the SMO-SVM classifier presents high performance in terms of authorship attribution accuracy (100%), especially with starting character-bigrams. In addition, the proposed method appears quite interesting by improving the AA performances in imbalanced datasets, mainly with function words.
{"title":"Arabic Authorship Attribution Using Synthetic Minority Over-Sampling Technique and Principal Components Analysis for Imbalanced Documents","authors":"H Hadjadj, H. Sayoud","doi":"10.4018/IJCINI.20211001.OA33","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA33","url":null,"abstract":"Dealing with imbalanced data represents a great challenge in data mining as well as in machine learning task. In this investigation, the authors are interested in the problem of class imbalance in authorship attribution (AA) task, with specific application on Arabic text data. This article proposes a new hybrid approach based on principal components analysis (PCA) and synthetic minority over-sampling technique (SMOTE), which considerably improve the performances of authorship attribution on imbalanced data. The used dataset contains seven Arabic books written by seven different scholars, which are segmented into text segments of the same size, with an average length of 2,900 words per text. The obtained results of the experiments show that the proposed approach using the SMO-SVM classifier presents high performance in terms of authorship attribution accuracy (100%), especially with starting character-bigrams. In addition, the proposed method appears quite interesting by improving the AA performances in imbalanced datasets, mainly with function words.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"519 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":"77193876","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.oa16
Wanzhi Wen, Shiqiang Wang, Bingqing Ye, XingYu Zhu, Yitao Hu, Xiaohong Lu, Bin Zhang
Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.
{"title":"API Recommendation Based on WII-WMD","authors":"Wanzhi Wen, Shiqiang Wang, Bingqing Ye, XingYu Zhu, Yitao Hu, Xiaohong Lu, Bin Zhang","doi":"10.4018/ijcini.20211001.oa16","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa16","url":null,"abstract":"Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"43 1","pages":"1-20"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87037836","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}
Artificial Intelligence is becoming more attractive to resolve nontrivial problems including the well known real time scheduling (RTS) problem for Embedded Systems (ES). The latter is considered as a hard multi-objective optimization problem because it must optimize at the same time three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction and reliability enhancement. In this paper, we firstly present the necessary background to well understand the problematic of RTS in the context of ES, then we present our enriched taxonomies for real time, energy and faults tolerance aware scheduling algorithms for ES. After that, we survey the most pertinent existing works of literature targeting the application of AI methods to resolve the RTS problem for ES notably Constraint Programming, Game theory, Machine learning, Fuzzy logic, Artificial Immune Systems, Cellular Automata, Evolutionary algorithms, Multi-agent Systems and Swarm Intelligence. We end this survey by a discussion putting the light on the main challenges and the future directions.
{"title":"AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems: A Review","authors":"Fateh Boutekkouk","doi":"10.4018/ijcini.290308","DOIUrl":"https://doi.org/10.4018/ijcini.290308","url":null,"abstract":"Artificial Intelligence is becoming more attractive to resolve nontrivial problems including the well known real time scheduling (RTS) problem for Embedded Systems (ES). The latter is considered as a hard multi-objective optimization problem because it must optimize at the same time three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction and reliability enhancement. In this paper, we firstly present the necessary background to well understand the problematic of RTS in the context of ES, then we present our enriched taxonomies for real time, energy and faults tolerance aware scheduling algorithms for ES. After that, we survey the most pertinent existing works of literature targeting the application of AI methods to resolve the RTS problem for ES notably Constraint Programming, Game theory, Machine learning, Fuzzy logic, Artificial Immune Systems, Cellular Automata, Evolutionary algorithms, Multi-agent Systems and Swarm Intelligence. We end this survey by a discussion putting the light on the main challenges and the future directions.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"36 1","pages":"1-44"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74135758","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}
Yingchi Liu, Du Jiang, Yibo Liu, Juntong Yun, D. Bai, Gongfa Li, Dalin Zhou
{"title":"Towards Multi-Finger Dexterous Hand Mechanics Control and Tactile Feedback","authors":"Yingchi Liu, Du Jiang, Yibo Liu, Juntong Yun, D. Bai, Gongfa Li, Dalin Zhou","doi":"10.4018/IJCINI.286770","DOIUrl":"https://doi.org/10.4018/IJCINI.286770","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"20 1","pages":"1-11"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72957600","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.OA44
Yi Wang, Kangshun Li
Multilevel thresholding image segmentation has been a hot issue of research in the last several years since it has a plenty of applications. The meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur’s entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA, and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.
{"title":"A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy","authors":"Yi Wang, Kangshun Li","doi":"10.4018/IJCINI.20211001.OA44","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA44","url":null,"abstract":"Multilevel thresholding image segmentation has been a hot issue of research in the last several years since it has a plenty of applications. The meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur’s entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA, and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"12 1","pages":"1-20"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79905268","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.oa2
Shuxing Yang, Kaili Zhu
Incorporate contextual information into recommendation systems can obtain better accuracy of recommendation, however, the users’ individual privacy may be disclosed by attackers. In order to resolve this problem, the authors propose a context-aware recommendation system that integrates Differential Privacy and Bayesian Network technologies (DPBCF). Firstly, the paper uses k-means algorithm to cluster items to relieve sparsity of rating matrix. Next, for the sake of protecting users’ privacy, the paper adds Laplace noises to ratings. And then adopts Bayesian Network technology to calculate the probability that users like a type of item with contextual information. At last, the authors illustrate the experimental evaluations to show that the proposed algorithm can provide a stronger privacy protection while improving the accuracy of recommendations.
{"title":"Differential Privacy and Bayesian for Context-Aware Recommender Systems","authors":"Shuxing Yang, Kaili Zhu","doi":"10.4018/ijcini.20211001.oa2","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa2","url":null,"abstract":"Incorporate contextual information into recommendation systems can obtain better accuracy of recommendation, however, the users’ individual privacy may be disclosed by attackers. In order to resolve this problem, the authors propose a context-aware recommendation system that integrates Differential Privacy and Bayesian Network technologies (DPBCF). Firstly, the paper uses k-means algorithm to cluster items to relieve sparsity of rating matrix. Next, for the sake of protecting users’ privacy, the paper adds Laplace noises to ratings. And then adopts Bayesian Network technology to calculate the probability that users like a type of item with contextual information. At last, the authors illustrate the experimental evaluations to show that the proposed algorithm can provide a stronger privacy protection while improving the accuracy of recommendations.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"41 1","pages":"1-13"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86246676","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}
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