Spatial database is a spatial information database and is the core component of geographic information systems (GIS). Aiming at the problem that time complexity of k-nearest neighbor (kNN) querying algorithms are proportionate to scale of training samples, an efficient query method for spatial database based on the Spark framework and the reversed k-nearest neighbor (RkNN) is proposed. Firstly, based on the Spark framework, a two-layer indexing structure based on grid and Voronoi diagram is constructed, and an efficient filtering and a refining processing algorithm are proposed. Secondly, the filtering step of proposed algorithm is used to obtain the candidates, and the refining step is used to remove the candidates. Finally, the candidate sets from different regions are merged to get the final result. Results of experiments on real-world datasets validate that the proposed method has better query performance and better stability and significantly improves the processing speed.
{"title":"A Novel Query Method for Spatial Database Based on Improved K-Nearest Neighbor Algorithm","authors":"Huili Xia, Feng Xue","doi":"10.4018/ijdsst.332773","DOIUrl":"https://doi.org/10.4018/ijdsst.332773","url":null,"abstract":"Spatial database is a spatial information database and is the core component of geographic information systems (GIS). Aiming at the problem that time complexity of k-nearest neighbor (kNN) querying algorithms are proportionate to scale of training samples, an efficient query method for spatial database based on the Spark framework and the reversed k-nearest neighbor (RkNN) is proposed. Firstly, based on the Spark framework, a two-layer indexing structure based on grid and Voronoi diagram is constructed, and an efficient filtering and a refining processing algorithm are proposed. Secondly, the filtering step of proposed algorithm is used to obtain the candidates, and the refining step is used to remove the candidates. Finally, the candidate sets from different regions are merged to get the final result. Results of experiments on real-world datasets validate that the proposed method has better query performance and better stability and significantly improves the processing speed.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135167503","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}
This research is focused on the identification, assessment, analysis, and evaluation of the impact of the most prominent out of many roadblocks impeding the implementation of Lean-Green and I4.0 practices in manufacturing industries. The research methodology is underpinned by an extensive literature review with expert interventions to make it comprehensive and far-reaching. Further, this exploratory research to address the broad objectives is based on a large sample size, which is validated statistically and empirically for its aptness. A combination of widely used statistical methods is used to converge, assess, analyze, and evaluate the impact of each roadblock individually and in the group on I4.0 implementation in industry. The study prominently depicts lack of organizational leadership, unclear waste management practices, and missing environment-friendly practices as the most prominent roadblocks hindering the progression of Lean-Green and I4.0 adoption. The novel PCA-ISM Fuzzy MICMAC integrated model developed in this research makes this article unique.
{"title":"Analysis and Evaluation of Roadblocks Hindering Lean-Green and Industry 4.0 Practices in Indian Manufacturing Industries","authors":"Rimalini Gadekar, B. Sarkar, Ashish Gadekar","doi":"10.4018/ijdsst.325350","DOIUrl":"https://doi.org/10.4018/ijdsst.325350","url":null,"abstract":"This research is focused on the identification, assessment, analysis, and evaluation of the impact of the most prominent out of many roadblocks impeding the implementation of Lean-Green and I4.0 practices in manufacturing industries. The research methodology is underpinned by an extensive literature review with expert interventions to make it comprehensive and far-reaching. Further, this exploratory research to address the broad objectives is based on a large sample size, which is validated statistically and empirically for its aptness. A combination of widely used statistical methods is used to converge, assess, analyze, and evaluate the impact of each roadblock individually and in the group on I4.0 implementation in industry. The study prominently depicts lack of organizational leadership, unclear waste management practices, and missing environment-friendly practices as the most prominent roadblocks hindering the progression of Lean-Green and I4.0 adoption. The novel PCA-ISM Fuzzy MICMAC integrated model developed in this research makes this article unique.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42830411","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}
Robots are one of the most commonly used automated material handling equipment (MHE) in an industry, installed to perform a variety of hazardous and repetitive tasks, e.g., loading, unloading, pick-and-place operations, etc. The selection of an appropriate industrial robot is influenced by a number of subjective and objective factors that define its characteristics and working accuracy. As a result, robot selection can be regarded as a multi-criteria decision-making problem. In this article, a new hybrid MCDM model combining COPRAS and ARAS is developed to execute an industrial robot selection process based on three alternatives and five criteria. Fuzzy analytic hierarchy process is integrated to compute the parametric weights. It is discovered that Robot 3 and Robot 1 are coming out to be the best and worst alternative robots from this hybrid model. Finally, comparative analysis among eight other MCDM tools and sensitivity analysis are also performed to assess the stability and robustness of the developed hybrid model and other applied MCDM tools.
{"title":"Developing Fuzzy-AHP-Integrated Hybrid MCDM System of COPRAS-ARAS for Solving an Industrial Robot Selection Problem","authors":"Shankha Shubhra Goswami, D. Behera","doi":"10.4018/ijdsst.324599","DOIUrl":"https://doi.org/10.4018/ijdsst.324599","url":null,"abstract":"Robots are one of the most commonly used automated material handling equipment (MHE) in an industry, installed to perform a variety of hazardous and repetitive tasks, e.g., loading, unloading, pick-and-place operations, etc. The selection of an appropriate industrial robot is influenced by a number of subjective and objective factors that define its characteristics and working accuracy. As a result, robot selection can be regarded as a multi-criteria decision-making problem. In this article, a new hybrid MCDM model combining COPRAS and ARAS is developed to execute an industrial robot selection process based on three alternatives and five criteria. Fuzzy analytic hierarchy process is integrated to compute the parametric weights. It is discovered that Robot 3 and Robot 1 are coming out to be the best and worst alternative robots from this hybrid model. Finally, comparative analysis among eight other MCDM tools and sensitivity analysis are also performed to assess the stability and robustness of the developed hybrid model and other applied MCDM tools.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48152817","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}
Information theory is the study of collecting, storing, and sharing digital information. It is a nexus of disciplines such as statistics, computer science, statistical mechanics, and probability theory. This study pertains to intuitionistic fuzzy sets theory, which is a substantial component of fuzzy set theory. Nonetheless, the motive of the study is to find vague information intuitionistic fuzzy entropy measures. The authors are extend the parametric intuitionistic fuzzy entropy measures by using trigonometric functions and investigate the difference between proposed study & existing entropy measures. Furthermore, discuss the significance analysis and authenticity of the proposed study. It concludes that the proposed measure could be a good perspective for decision-making problems. Using a suitable illustration, the applicability of the proposed study has been demonstrated. Depict the graph of proposed and existing entropy measure together with their average measure. Additionally, these estimations enhance the study of information theory and produce superior information.
{"title":"Generalized Parametric Intuitionistic Fuzzy Measures Based on Trigonometric Functions for Improved Decision-Making Problem","authors":"Pawan Gora, V. P. Tomar","doi":"10.4018/ijdsst.323444","DOIUrl":"https://doi.org/10.4018/ijdsst.323444","url":null,"abstract":"Information theory is the study of collecting, storing, and sharing digital information. It is a nexus of disciplines such as statistics, computer science, statistical mechanics, and probability theory. This study pertains to intuitionistic fuzzy sets theory, which is a substantial component of fuzzy set theory. Nonetheless, the motive of the study is to find vague information intuitionistic fuzzy entropy measures. The authors are extend the parametric intuitionistic fuzzy entropy measures by using trigonometric functions and investigate the difference between proposed study & existing entropy measures. Furthermore, discuss the significance analysis and authenticity of the proposed study. It concludes that the proposed measure could be a good perspective for decision-making problems. Using a suitable illustration, the applicability of the proposed study has been demonstrated. Depict the graph of proposed and existing entropy measure together with their average measure. Additionally, these estimations enhance the study of information theory and produce superior information.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47035685","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 address the high rate of false alarms, this article proposed a voting-based method to efficiently predict intrusions in real time. To carry out this study, an intrusion detection dataset from UNSW was downloaded and preprocessed before being used. Given the number of features at hand and the large size of the dataset, performance was poor while accuracy was low. This low prediction accuracy led to the generation of false alerts, consequently, legitimate alerts used to pass without an action assuming them as false. To deal with large size and false alarms, the proposed voting-based feature reduction approach proved to be highly beneficial in reducing the dataset size by selecting only the features secured majority votes. Outcome collected prior to and following the application of the proposed model were compared. The findings reveal that the proposed approach required less time to predict, at the same time predicted accuracy was higher. The proposed approach will be extremely effective at detecting intrusions in real-time environments and mitigating the cyber-attacks.
{"title":"An Efficient Method to Decide the Malicious Traffic","authors":"Ajay Kumar, Jitendra Singh, Vikas Kumar, Saurabh Shrivastava","doi":"10.4018/ijdsst.323191","DOIUrl":"https://doi.org/10.4018/ijdsst.323191","url":null,"abstract":"To address the high rate of false alarms, this article proposed a voting-based method to efficiently predict intrusions in real time. To carry out this study, an intrusion detection dataset from UNSW was downloaded and preprocessed before being used. Given the number of features at hand and the large size of the dataset, performance was poor while accuracy was low. This low prediction accuracy led to the generation of false alerts, consequently, legitimate alerts used to pass without an action assuming them as false. To deal with large size and false alarms, the proposed voting-based feature reduction approach proved to be highly beneficial in reducing the dataset size by selecting only the features secured majority votes. Outcome collected prior to and following the application of the proposed model were compared. The findings reveal that the proposed approach required less time to predict, at the same time predicted accuracy was higher. The proposed approach will be extremely effective at detecting intrusions in real-time environments and mitigating the cyber-attacks.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42380252","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}
Electric vehicles are an emerging and evolving technology that brings in remarkable environmental gains over conventional vehicles, contributing significantly towards a decrease in fossil fuel dependence. However, infiltrating into the existing automobile market requires huge investment in charging facilities and intricate planning to make it more approachable to the consumers. Identifying the crucial challenges and finding a solution has been a major hurdle to the manufacturers. While various non-government agencies and government policies are urging both consumers and manufacturers to adopt electric mobility, many industries remain unguided. The paper aims to identify, study, and rank 12 of these influential challenges faced by the manufacturers based on their impact on enhancing the manufacturing and sales of electric vehicles in India using the triangular fuzzy number (TFN) method. Results obtained reveal that inadequate charging infrastructure is one of the biggest hurdles.
{"title":"Barriers in Replacement of Conventional Vehicles by Electric Vehicles in India","authors":"Disha Bhattacharyya, Sudeepta Pradhan, Shabbiruddin","doi":"10.4018/ijdsst.323135","DOIUrl":"https://doi.org/10.4018/ijdsst.323135","url":null,"abstract":"Electric vehicles are an emerging and evolving technology that brings in remarkable environmental gains over conventional vehicles, contributing significantly towards a decrease in fossil fuel dependence. However, infiltrating into the existing automobile market requires huge investment in charging facilities and intricate planning to make it more approachable to the consumers. Identifying the crucial challenges and finding a solution has been a major hurdle to the manufacturers. While various non-government agencies and government policies are urging both consumers and manufacturers to adopt electric mobility, many industries remain unguided. The paper aims to identify, study, and rank 12 of these influential challenges faced by the manufacturers based on their impact on enhancing the manufacturing and sales of electric vehicles in India using the triangular fuzzy number (TFN) method. Results obtained reveal that inadequate charging infrastructure is one of the biggest hurdles.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47200887","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}
Cardiovascular diseases (CVDs) are the number one cause of death globally. Coronary artery disease (CAD) is the most common form of CVDs. Abundant research works propose decision support systems for CAD early detection. Most of proposed solutions have their origins in the realm of machine learning and datamining. This paper presents two solutions for CAD prediction. The first solution optimizes a random forest model (RFM) through hyperparameters tuning. The second solution uses a case-based reasoning (CBR) methodology. The CBR solution takes advantage of feature importance to improve the execution time of the retrieve step in the CBR cycle. The experimentations show that the RFM outperformed most recent published models for CAD diagnosis. By reducing the number of attributes, the CBR solution improves the execution time and also performs very well in terms of diagnosis accuracy. The performance of the CBR solution is intended to be enhanced because CBR is a learning methodology.
{"title":"Improving Coronary Artery Disease Prediction: Use of Random Forest, Feature Importance and Case-Based Reasoning","authors":"F. Henni, B. Atmani, F. Atmani, F. Saadi","doi":"10.4018/ijdsst.319307","DOIUrl":"https://doi.org/10.4018/ijdsst.319307","url":null,"abstract":"Cardiovascular diseases (CVDs) are the number one cause of death globally. Coronary artery disease (CAD) is the most common form of CVDs. Abundant research works propose decision support systems for CAD early detection. Most of proposed solutions have their origins in the realm of machine learning and datamining. This paper presents two solutions for CAD prediction. The first solution optimizes a random forest model (RFM) through hyperparameters tuning. The second solution uses a case-based reasoning (CBR) methodology. The CBR solution takes advantage of feature importance to improve the execution time of the retrieve step in the CBR cycle. The experimentations show that the RFM outperformed most recent published models for CAD diagnosis. By reducing the number of attributes, the CBR solution improves the execution time and also performs very well in terms of diagnosis accuracy. The performance of the CBR solution is intended to be enhanced because CBR is a learning methodology.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74088835","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}
The authors present a new approach to decision-making to ensure the proper collaboration of different personnel. Models are used in intelligent integrated decision support systems, especially for actions in emergencies. Behavioral deterministic models are used for synchronization actions of all operators, to support and timely predicting of operators' actions in an emergency. To determine the quantitative characteristics of risk levels, models for collaborative decision making (CDM) under uncertainty by the operators (pilots, air traffic controllers, flight/dispatch), and other invited specialists, have been developed. The decision-making modeling of a group of operators in case of an emergency situation such as “pilot incapacitation” was presented. The methodological basis for CDM in certainty is network planning, in conditions of Stochastic uncertainty – is a decision tree, in conditions of non-stochastic uncertainty – is a matrix of decisions, and outcomes are made this using the expert judgment method for obtaining quantity estimations.
{"title":"Collaborative Decision Making (CDM) in Emergency Caused by Captain Incapacitation: Deterministic and Stochastic Modelling","authors":"T. Shmelova, Maxim Yatsko, Iurii Sierostanov","doi":"10.4018/ijdsst.320477","DOIUrl":"https://doi.org/10.4018/ijdsst.320477","url":null,"abstract":"The authors present a new approach to decision-making to ensure the proper collaboration of different personnel. Models are used in intelligent integrated decision support systems, especially for actions in emergencies. Behavioral deterministic models are used for synchronization actions of all operators, to support and timely predicting of operators' actions in an emergency. To determine the quantitative characteristics of risk levels, models for collaborative decision making (CDM) under uncertainty by the operators (pilots, air traffic controllers, flight/dispatch), and other invited specialists, have been developed. The decision-making modeling of a group of operators in case of an emergency situation such as “pilot incapacitation” was presented. The methodological basis for CDM in certainty is network planning, in conditions of Stochastic uncertainty – is a decision tree, in conditions of non-stochastic uncertainty – is a matrix of decisions, and outcomes are made this using the expert judgment method for obtaining quantity estimations.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80641244","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}
This paper proposes a decision model for a maturity model choice problem via the multiple-criteria decision analysis method. The authors implemented the model in FITradeoff decision support system to select a project management maturity model for a Brazilian industry operating in the sector of distributing natural gas. FITradeoff is a flexible and interactive procedure of elicitation for multi-criteria additive models that requires only partial information from the decision maker (i.e., there is no need to elicit very detailed information from the decision maker, an approach that the decision maker can find laborious and tiring). The authors observed that the use of a multi-criteria approach imposes certain rigor and pattern on the decision process to select a maturity model in project management. Applying the model enabled comparison of information from the four maturity models and therefore selecting a project management maturity model based on the decision-maker preferences.
{"title":"A Multi-Criteria Decision-Making Model for Selecting a Maturity Model","authors":"João Batista Sarmento dos Santos-Neto, A. Costa","doi":"10.4018/ijdsst.319305","DOIUrl":"https://doi.org/10.4018/ijdsst.319305","url":null,"abstract":"This paper proposes a decision model for a maturity model choice problem via the multiple-criteria decision analysis method. The authors implemented the model in FITradeoff decision support system to select a project management maturity model for a Brazilian industry operating in the sector of distributing natural gas. FITradeoff is a flexible and interactive procedure of elicitation for multi-criteria additive models that requires only partial information from the decision maker (i.e., there is no need to elicit very detailed information from the decision maker, an approach that the decision maker can find laborious and tiring). The authors observed that the use of a multi-criteria approach imposes certain rigor and pattern on the decision process to select a maturity model in project management. Applying the model enabled comparison of information from the four maturity models and therefore selecting a project management maturity model based on the decision-maker preferences.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83789764","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}
M. Alvarenga, L. Sincorá, Marcos Paulo Valadares de Oliveira, L. Fantinel, Mauri Leodir Löbler
This paper aims to investigate how past decision-making experiences can improve future decision-making. Nine semi-structured interviews were conducted with profitable professional Poker players. The results point out that it is the knowledge background of the decision-maker that makes him make sense of the situations he experiences. The research findings allowed the identification of three mechanisms that facilitate and make future decisions faster and more appropriate based on past experiences: (1) memory, (2) reflection, and (3) tools and analytical approach. The research contributes by showing evidence that, when supported by analytical tools, decision-makers can improve the quality and speed of the decision-making process. For organizations and supply chains, the paper highlights the importance of recognizing patterns based on the past to make sense of the future. For operations management, in events like COVID-19, companies can take advantage of memory to enact over unprecedented scenarios, prevent disruptions, and recover.
{"title":"What Can Managers Learn From Professional Poker Players About Decision-Making?","authors":"M. Alvarenga, L. Sincorá, Marcos Paulo Valadares de Oliveira, L. Fantinel, Mauri Leodir Löbler","doi":"10.4018/ijdsst.319308","DOIUrl":"https://doi.org/10.4018/ijdsst.319308","url":null,"abstract":"This paper aims to investigate how past decision-making experiences can improve future decision-making. Nine semi-structured interviews were conducted with profitable professional Poker players. The results point out that it is the knowledge background of the decision-maker that makes him make sense of the situations he experiences. The research findings allowed the identification of three mechanisms that facilitate and make future decisions faster and more appropriate based on past experiences: (1) memory, (2) reflection, and (3) tools and analytical approach. The research contributes by showing evidence that, when supported by analytical tools, decision-makers can improve the quality and speed of the decision-making process. For organizations and supply chains, the paper highlights the importance of recognizing patterns based on the past to make sense of the future. For operations management, in events like COVID-19, companies can take advantage of memory to enact over unprecedented scenarios, prevent disruptions, and recover.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91051971","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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