Pub Date : 2022-12-23DOI: 10.1142/s0219622023500104
Victor Henrique Alves Ribeiro, G. Reynoso-Meza
{"title":"Multi-criteria Decision Making Techniques for the Selection of Pareto-optimal Machine Learning Models in a Drinking-water Quality Monitoring Problem","authors":"Victor Henrique Alves Ribeiro, G. Reynoso-Meza","doi":"10.1142/s0219622023500104","DOIUrl":"https://doi.org/10.1142/s0219622023500104","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129746396","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 : 2022-12-20DOI: 10.1142/s0219622023500098
Biyu Yang, Longxi Li, Xu Wang, Guangzhu Tan
{"title":"A novel crowdsourcing task recommendation method considering workers' fuzzy expectations: a case of ZBJ.COM","authors":"Biyu Yang, Longxi Li, Xu Wang, Guangzhu Tan","doi":"10.1142/s0219622023500098","DOIUrl":"https://doi.org/10.1142/s0219622023500098","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132199685","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 : 2022-12-14DOI: 10.1142/s0219622023500086
J. Sheela, N. Karthika, B. Janet
{"title":"SSLnDO-Based Deep Residual Network and RV-Coefficient Integrated Deep Fuzzy Clustering for Cotton Crop Classification","authors":"J. Sheela, N. Karthika, B. Janet","doi":"10.1142/s0219622023500086","DOIUrl":"https://doi.org/10.1142/s0219622023500086","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122184357","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 : 2022-12-14DOI: 10.1142/s0219622023500074
Chenyang Song, Zeshui Xu, B. Li
{"title":"Water Eutrophication Evaluation Based on the Improved Projection Pursuit Regression Model under the Hesitant Fuzzy Environment","authors":"Chenyang Song, Zeshui Xu, B. Li","doi":"10.1142/s0219622023500074","DOIUrl":"https://doi.org/10.1142/s0219622023500074","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131926617","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 : 2022-12-09DOI: 10.1142/s0219622023500062
Seyed Mojtaba Saif, M. Samie, A. Hamzeh
{"title":"Detecting overlapping communities in complex networks: an evolutionary label propagation approach","authors":"Seyed Mojtaba Saif, M. Samie, A. Hamzeh","doi":"10.1142/s0219622023500062","DOIUrl":"https://doi.org/10.1142/s0219622023500062","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115919122","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 : 2022-12-09DOI: 10.1142/s0219622023500049
Nagarjun Yadav Vanguri, S. Pazhanirajan, T. Anil Kumar
{"title":"Extraction of technical indicators and data augmentation based stock market prediction using Deep LSTM integrated Competitive Swarm Feedback algorithm","authors":"Nagarjun Yadav Vanguri, S. Pazhanirajan, T. Anil Kumar","doi":"10.1142/s0219622023500049","DOIUrl":"https://doi.org/10.1142/s0219622023500049","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121588777","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 : 2022-12-09DOI: 10.1142/s0219622023500050
M. Guillén, J. Aparicio, Miriam Esteve
{"title":"Performance evaluation of decision making units through boosting methods in the context of Free Disposal Hull: some exact and heuristic algorithms","authors":"M. Guillén, J. Aparicio, Miriam Esteve","doi":"10.1142/s0219622023500050","DOIUrl":"https://doi.org/10.1142/s0219622023500050","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133930250","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 : 2022-12-09DOI: 10.1142/s0219622023500037
Irik Z. Mukhametzyanov
{"title":"Elimination of the domains' displacement of the normalized values in MCDM tasks: the IZ-method","authors":"Irik Z. Mukhametzyanov","doi":"10.1142/s0219622023500037","DOIUrl":"https://doi.org/10.1142/s0219622023500037","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131650303","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 : 2022-12-07DOI: 10.1142/s0219622022500948
Jakub Wiȩckowski, Bartłomiej Kizielewicz, B. Paradowski, A. Shekhovtsov, W. Sałabun
One of the main challenges in the Multi-Criteria Decision Analysis (MCDA) field is how we can identify criteria weights correctly. However, some MCDA methods do not use an explicitly defined vector of criterion weights, leaving the decision-maker lacking knowledge in this area. This is the motivation for our research because, in that case, a decision-maker cannot indicate a detailed justification for the proposed results. In this paper, we focus on the problem of identifying criterion weights in multi-criteria problems. Based on the proposed Characteristic Object Method (COMET) model, we used linear regression to determine the global and local criterion weights in the given situation. The work was directed toward a practical problem, i.e., evaluating Formula One drivers’ performances in races in the 2021 season. The use of the linear regression model allowed for identifying the criterion weights. Thanks to that, the expert using the system based on the COMET method can be equipped with the missing knowledge about the significance of the criteria. The local identification allowed us to establish how small input parameter changes affect the final result. However, the local weights are still highly correlated with global weights. The proposed approach to identifying weights proved to be an effective tool that can be used to fill in the missing knowledge that the expert can use to justify the results in detail. Moreover, weights identified in that way seem to be more reliable than in the classical approach, where we know only global weights. From the research it can be concluded, that the identified global and local weights importance provide highly similar results, while the former one provides more detailed information for the expert. Furthermore, the proposed approach can be used as a support tool in the practical problem as it guarantees additional data for the decision-maker.
{"title":"Application of Multi-Criteria Decision Analysis to Identify Global and Local Importance Weights of Decision Criteria","authors":"Jakub Wiȩckowski, Bartłomiej Kizielewicz, B. Paradowski, A. Shekhovtsov, W. Sałabun","doi":"10.1142/s0219622022500948","DOIUrl":"https://doi.org/10.1142/s0219622022500948","url":null,"abstract":"One of the main challenges in the Multi-Criteria Decision Analysis (MCDA) field is how we can identify criteria weights correctly. However, some MCDA methods do not use an explicitly defined vector of criterion weights, leaving the decision-maker lacking knowledge in this area. This is the motivation for our research because, in that case, a decision-maker cannot indicate a detailed justification for the proposed results. In this paper, we focus on the problem of identifying criterion weights in multi-criteria problems. Based on the proposed Characteristic Object Method (COMET) model, we used linear regression to determine the global and local criterion weights in the given situation. The work was directed toward a practical problem, i.e., evaluating Formula One drivers’ performances in races in the 2021 season. The use of the linear regression model allowed for identifying the criterion weights. Thanks to that, the expert using the system based on the COMET method can be equipped with the missing knowledge about the significance of the criteria. The local identification allowed us to establish how small input parameter changes affect the final result. However, the local weights are still highly correlated with global weights. The proposed approach to identifying weights proved to be an effective tool that can be used to fill in the missing knowledge that the expert can use to justify the results in detail. Moreover, weights identified in that way seem to be more reliable than in the classical approach, where we know only global weights. From the research it can be concluded, that the identified global and local weights importance provide highly similar results, while the former one provides more detailed information for the expert. Furthermore, the proposed approach can be used as a support tool in the practical problem as it guarantees additional data for the decision-maker.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125013008","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 : 2022-11-30DOI: 10.1142/s0219622022500961
S. Koçak, Yusuf Tansel İç, M. Sert, K. D. Atalay, B. Dengiz
The evaluation of Research and Development (R&D) projects consists of many steps depending on the government funding agencies and the support program. It is observed that the reviewer evaluation reports have a crucial impact on the support decisions of the projects. In this study, a decision support system (DSS), namely R&D Reviewer, is developed to help the decision-makers with the assignment of the appropriate reviewer to R&D project proposals. It is aimed to create an artificial intelligence-based decision support system that enables the classification of Turkish R&D projects with natural language processing (NLP) methods. Furthermore, we examine the reviewer ranking process by using fuzzy multi-criteria decision-making methods. The data in the database is processed primarily to classify the R&D projects and the word embedding model NLP, “Word2Vec”. Also, we designed the Convolutional Neural Network (CNN) model to select the features by using the automatic feature learning approach. Moreover, we incorporate a new integrated hesitant fuzzy VIKOR and TOPSIS methodology into the developed DSS for the reviewer ranking process.
{"title":"Development of a Decision Support System for Selection of Reviewers to Evaluate Research and Development Projects","authors":"S. Koçak, Yusuf Tansel İç, M. Sert, K. D. Atalay, B. Dengiz","doi":"10.1142/s0219622022500961","DOIUrl":"https://doi.org/10.1142/s0219622022500961","url":null,"abstract":"The evaluation of Research and Development (R&D) projects consists of many steps depending on the government funding agencies and the support program. It is observed that the reviewer evaluation reports have a crucial impact on the support decisions of the projects. In this study, a decision support system (DSS), namely R&D Reviewer, is developed to help the decision-makers with the assignment of the appropriate reviewer to R&D project proposals. It is aimed to create an artificial intelligence-based decision support system that enables the classification of Turkish R&D projects with natural language processing (NLP) methods. Furthermore, we examine the reviewer ranking process by using fuzzy multi-criteria decision-making methods. The data in the database is processed primarily to classify the R&D projects and the word embedding model NLP, “Word2Vec”. Also, we designed the Convolutional Neural Network (CNN) model to select the features by using the automatic feature learning approach. Moreover, we incorporate a new integrated hesitant fuzzy VIKOR and TOPSIS methodology into the developed DSS for the reviewer ranking process.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128168416","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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