International Journal of Enterprise Modelling最新文献

Optimizing dataset classification through hybrid grid partition and rough set method for fuzzy rule generation 通过混合网格划分和粗糙集方法优化数据集分类，生成模糊规则

International Journal of Enterprise Modelling Pub Date : 2023-05-30 DOI: 10.35335/emod.v17i2.22

Randrianja Velo, Jérôme Tamatave, Solofo Sahambala

{"title":"Optimizing dataset classification through hybrid grid partition and rough set method for fuzzy rule generation","authors":"Randrianja Velo, Jérôme Tamatave, Solofo Sahambala","doi":"10.35335/emod.v17i2.22","DOIUrl":"https://doi.org/10.35335/emod.v17i2.22","url":null,"abstract":"This research presents a novel approach for optimizing dataset classification through the integration of a hybrid grid partition and rough set method for fuzzy rule generation. The objective is to improve classification accuracy and interpretability while effectively handling uncertainty in the dataset. The proposed approach combines grid partitioning, rough set theory, and fuzzy logic to identify relevant attributes within each grid cell, generate accurate fuzzy rules, and perform classification based on fuzzy inference. The research demonstrates the improved accuracy of the hybrid approach compared to traditional methods, along with enhanced interpretability of the generated fuzzy rules. The scalability and generalizability of the approach are validated through its application to a case example in customer churn prediction in the telecommunications industry. However, certain limitations, such as the selection of the partitioning scheme, computational complexity, and handling of missing data, need to be considered. Further research is required to address these limitations and benchmark the approach against state-of-the-art techniques. The proposed hybrid approach contributes to the field of dataset classification by offering an effective and interpretable methodology for improved classification performance and actionable insights in real-world applications","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114939858","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}

引用次数: 0

An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory 基于混合网格划分和粗糙集理论的数据集分类模糊规则生成集成方法

International Journal of Enterprise Modelling Pub Date : 2023-05-30 DOI: 10.35335/emod.v17i2.19

Tokpa Braxton Ferguson

{"title":"An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory","authors":"Tokpa Braxton Ferguson","doi":"10.35335/emod.v17i2.19","DOIUrl":"https://doi.org/10.35335/emod.v17i2.19","url":null,"abstract":"This research presents an integrated approach for fuzzy rule generation in dataset classification by combining hybrid grid partitioning and rough set theory. The objective is to enhance the accuracy and interpretability of classification models. The approach leverages hybrid grid partitioning to achieve localized rule generation, capturing the local characteristics and patterns within different regions of the feature space. Furthermore, rough set theory is applied for attribute reduction, identifying the most relevant features and reducing the complexity of the classification problem. The generated fuzzy rules provide interpretable and understandable classification rules that facilitate domain expert interpretation. The research contributes to the field by proposing a comprehensive framework that improves both accuracy and interpretability of dataset classification. The findings demonstrate the effectiveness of the integrated approach, although certain limitations exist. Future research should focus on parameter selection, scalability challenges, and the applicability of the approach to diverse problem domains. The integrated approach presents a promising methodology for enhancing the accuracy and interpretability of dataset classification, with potential applications in various domains where accurate and interpretable classification models are crucial.","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117238546","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}

引用次数: 0

Integrating hybrid grid partition and rough set method for fuzzy rule generation: a novel approach for accurate dataset classification 结合混合网格划分和粗糙集方法的模糊规则生成:一种精确数据集分类的新方法

International Journal of Enterprise Modelling Pub Date : 2023-05-30 DOI: 10.35335/emod.v17i2.21

Luke Joseph, Meiser Llywellenie O'Leary, Bisani Zagré

{"title":"Integrating hybrid grid partition and rough set method for fuzzy rule generation: a novel approach for accurate dataset classification","authors":"Luke Joseph, Meiser Llywellenie O'Leary, Bisani Zagré","doi":"10.35335/emod.v17i2.21","DOIUrl":"https://doi.org/10.35335/emod.v17i2.21","url":null,"abstract":"Accurate dataset classification is a critical task in various domains, and combining different methodologies can enhance classification performance. This research presents a novel approach that integrates Hybrid Grid Partition and Rough Set methods for fuzzy rule generation, aiming to improve accuracy and interpretability in dataset classification. The proposed approach leverages Hybrid Grid Partition to discretize continuous attributes and Rough Set attribute reduction to identify essential attributes, enabling accurate classification while handling uncertainty and imprecision. The generated fuzzy rules provide interpretability, aiding decision-making processes and providing insights into classification factors. The approach's robustness and generalization capabilities are demonstrated through experiments on diverse datasets, indicating its potential applicability in real-world scenarios. However, limitations such as the absence of specific evaluation metrics and the need for further validation on larger datasets are acknowledged. Overall, this research contributes to accurate dataset classification by offering a novel integrated approach and highlighting areas for future investigation and refinement","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133491600","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}

引用次数: 0

Hybridizing grid partitioning, rough set theory, and feature selection for fuzzy rule generation in dataset classification 混合网格划分、粗糙集理论和特征选择在数据集分类中的模糊规则生成

International Journal of Enterprise Modelling Pub Date : 2023-05-30 DOI: 10.35335/emod.v13i1.20

Ogange Lawrence

{"title":"Hybridizing grid partitioning, rough set theory, and feature selection for fuzzy rule generation in dataset classification","authors":"Ogange Lawrence","doi":"10.35335/emod.v13i1.20","DOIUrl":"https://doi.org/10.35335/emod.v13i1.20","url":null,"abstract":"This research investigates the hybridization of Grid Partitioning, Rough Set Theory, and Feature Selection for Fuzzy Rule Generation in Dataset Classification. The objective is to improve classification accuracy and interpretability by integrating multiple techniques. Grid partitioning is employed to divide the dataset into regions, allowing localized analysis. Rough set theory is utilized for attribute reduction and feature selection, identifying informative features within each region. Fuzzy rule generation is applied to generate interpretable classification rules using linguistic terms and membership functions. The hybrid model is optimized using metaheuristic algorithms to maximize classification performance. The research demonstrates the potential of the hybrid approach through experiments on the Iris flower dataset. The findings reveal improved classification accuracy, enhanced interpretability, and effective handling of complex datasets. The research contributes to the field by integrating these techniques into a cohesive framework and highlights the importance of parameter settings, computational complexity, and real-world applications. Future work should address these limitations and validate the approach on diverse datasets. The hybridization of Grid Partitioning, Rough Set Theory, and Feature Selection for Fuzzy Rule Generation holds promise for advancing classification models in various domains","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829997","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}

引用次数: 0

Exploring the synergistic effects of hybrid grid partitioning and rough set method for fuzzy rule generation in dataset classification 探索混合网格划分和粗糙集方法在数据集分类模糊规则生成中的协同效应

International Journal of Enterprise Modelling Pub Date : 2023-05-30 DOI: 10.35335/emod.v17i2.18

Abubakullo Abubakullo, Aisyah Alesha

{"title":"Exploring the synergistic effects of hybrid grid partitioning and rough set method for fuzzy rule generation in dataset classification","authors":"Abubakullo Abubakullo, Aisyah Alesha","doi":"10.35335/emod.v17i2.18","DOIUrl":"https://doi.org/10.35335/emod.v17i2.18","url":null,"abstract":"This research explores the synergistic effects of hybrid grid partitioning and the rough set method for fuzzy rule generation in dataset classification. The aim is to improve the accuracy and interpretability of the classification process. The rough set-based feature selection technique is employed to identify the most relevant features for classification, leading to a focused and informative feature subset. The hybrid grid partitioning approach combines clustering algorithms and grid-based methods to create an efficient grid structure, capturing the intrinsic data distribution. This enhances the representation and separation of data regions, improving classification accuracy. The generated fuzzy rule base provides interpretable decision rules, enabling domain experts to gain insights into the classification process. The proposed approach strikes a balance between accuracy and interpretability, making it valuable for various domains. However, limitations such as generalizability and scalability should be considered. Comparative analysis with existing methods and real-world case studies would further validate the effectiveness of the approach. Overall, this research contributes to the advancement of dataset classification and provides a novel integrated approach for accurate and interpretable classification.","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"89 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116302749","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}

引用次数: 0

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty 不确定性下分布鲁棒随机变分不等式的鲁棒学习与优化

International Journal of Enterprise Modelling Pub Date : 2023-01-01 DOI: 10.35335/emod.v17i1.70

Hengki Tamando Sihotang, Patrisius Michaud Felix Marsoit, Patrisia Teresa Marsoit

{"title":"Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty","authors":"Hengki Tamando Sihotang, Patrisius Michaud Felix Marsoit, Patrisia Teresa Marsoit","doi":"10.35335/emod.v17i1.70","DOIUrl":"https://doi.org/10.35335/emod.v17i1.70","url":null,"abstract":"Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty is a crucial research area that addresses the challenge of making optimal decisions in the presence of distributional ambiguity. This research explores the development of methodologies and algorithms to handle uncertainty in variational inequalities, incorporating a distributionally robust framework that considers a range of possible distributions or uncertainty sets. By minimizing the worst-case expected performance across these distributions, the proposed approaches ensure robustness and optimality in decision-making under uncertainty. The research encompasses theoretical analysis, algorithm development, and empirical evaluations to demonstrate the effectiveness of the proposed methodologies in various domains, such as portfolio optimization and supply chain management. The outcomes of this research contribute to the advancement of robust optimization techniques, enabling decision-makers to make reliable and robust decisions in complex real-world systems \u0000 ","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122788759","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}

引用次数: 1

Modeling and optimization of multi-altitude leo satellite networks using cox point processes 基于cox点过程的多高度leo卫星网络建模与优化

International Journal of Enterprise Modelling Pub Date : 2023-01-01 DOI: 10.35335/emod.v17i1.71

Titus Gramacy Zhu, Shi-soon Solosa, Periera Maniani

{"title":"Modeling and optimization of multi-altitude leo satellite networks using cox point processes","authors":"Titus Gramacy Zhu, Shi-soon Solosa, Periera Maniani","doi":"10.35335/emod.v17i1.71","DOIUrl":"https://doi.org/10.35335/emod.v17i1.71","url":null,"abstract":"This research focuses on the modeling and optimization of multi-altitude Low Earth Orbit (LEO) satellite networks using Cox point processes to achieve efficient coverage and performance analysis. LEO satellite networks have gained attention for their potential to provide global connectivity with reduced latency and increased network capacity. Accurately modeling the spatial distribution of satellites at different altitudes and optimizing their deployment pose significant challenges. This research proposes a mathematical framework based on Cox point processes to capture the randomness and irregularity of satellite deployments. Optimization algorithms, such as genetic algorithms, are employed to determine the optimal satellite locations, altitude allocation, and network parameters. Performance analysis considers metrics such as coverage probability, signal strength, interference levels, capacity, and quality of service. The research contributes to the development of advanced modeling techniques, optimization algorithms, and performance analysis frameworks, enabling efficient coverage and performance optimization in multi-altitude LEO satellite networks. The numerical examples and discussions illustrate the effectiveness and potential of the proposed approach in enhancing the design and operation of satellite communication systems","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134057632","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}

引用次数: 0

A fuzzy sustainable model for drug supply chain networks during a pandemic 大流行期间药品供应链网络的模糊可持续模型

International Journal of Enterprise Modelling Pub Date : 2023-01-01 DOI: 10.35335/emod.v17i1.68

Nosatzki Stein Rivest, Hanguir Leiserson Truong

{"title":"A fuzzy sustainable model for drug supply chain networks during a pandemic","authors":"Nosatzki Stein Rivest, Hanguir Leiserson Truong","doi":"10.35335/emod.v17i1.68","DOIUrl":"https://doi.org/10.35335/emod.v17i1.68","url":null,"abstract":"This research focuses on developing a fuzzy sustainable model for drug supply chain networks during a pandemic. The outbreak of a pandemic introduces unprecedented uncertainties and complexities to the drug supply chain, necessitating the integration of sustainability considerations and fuzzy logic techniques into decision-making processes. The proposed model aims to optimize decision variables, such as inventory levels, production capacities, transportation routes, and allocation strategies, while balancing conflicting objectives and addressing sustainability criteria. The model incorporates fuzzy logic to handle imprecise and uncertain inputs, allowing decision-makers to capture qualitative information and expert knowledge. The research emphasizes the importance of sustainability in drug supply chains, encompassing environmental impact, social welfare, and economic viability. Through the use of an optimization framework and a decision support system, stakeholders can make informed decisions considering sustainability criteria and dynamic pandemic conditions. The research contributes to enhancing the resilience, efficiency, and sustainability of drug supply chains during pandemics, facilitating better patient care and community well-being.","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"384 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133319266","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}

引用次数: 0

Stochastic modeling and performance analysis of multi-altitude LEO satellite networks using cox point processes 基于cox点过程的多高度LEO卫星网络随机建模与性能分析

International Journal of Enterprise Modelling Pub Date : 2023-01-01 DOI: 10.35335/emod.v17i1.72

F. Riandari, Salomo Sijabat, Firta Sari Panjaitan

{"title":"Stochastic modeling and performance analysis of multi-altitude LEO satellite networks using cox point processes","authors":"F. Riandari, Salomo Sijabat, Firta Sari Panjaitan","doi":"10.35335/emod.v17i1.72","DOIUrl":"https://doi.org/10.35335/emod.v17i1.72","url":null,"abstract":"The research focuses on the stochastic modeling and performance analysis of multi-altitude Low Earth Orbit (LEO) satellite networks using Cox point processes. LEO satellite networks have emerged as a promising solution for global connectivity, offering high data rates and low latency. To optimize their performance and resource allocation, accurate modeling and analysis techniques are crucial. This research employs Cox point processes to model the spatial distribution and behavior of satellites at different altitudes within the network. The intensity functions capture the expected number of satellites per unit area at each altitude. Realizations of the Cox point process are generated using Monte Carlo simulations, enabling performance analysis in terms of network connectivity, coverage probability, signal quality, and interference levels. The results provide insights into network behavior and inform network design decisions, including the optimal number of satellites, their altitudes, and their spatial distribution. The research contributes to the advancement of multi-altitude LEO satellite networks, enabling efficient global connectivity and addressing communication needs in various industries and applications \u0000 ","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126024292","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}

引用次数: 0

A novel stochastic fuzzy decision model for optimizing decision-making in the manufacturing industry 制造行业优化决策的随机模糊决策模型

International Journal of Enterprise Modelling Pub Date : 2023-01-01 DOI: 10.35335/emod.v17i1.69

Xie Shone Seen, Darvishi Mondragon Ortiz-Barrios, Osei Scott Kant

{"title":"A novel stochastic fuzzy decision model for optimizing decision-making in the manufacturing industry","authors":"Xie Shone Seen, Darvishi Mondragon Ortiz-Barrios, Osei Scott Kant","doi":"10.35335/emod.v17i1.69","DOIUrl":"https://doi.org/10.35335/emod.v17i1.69","url":null,"abstract":"In unpredictable and imprecise production environments, this research introduces a stochastic fuzzy decision model for the manufacturing industry. Decision-makers can use the stochastic and fuzzy logic model to capture uncertainties, variability, and language representations of industrial factors. The choice problem, fuzzy input variables, and crisp outcome variables are identified to start the research. Linguistic terms related with fuzzy input variables are represented by fuzzy sets and membership functions. Fuzzy rules link fuzzy input variables to crisp output variables based on expert knowledge or historical data. Objective function, restrictions, and fuzzy rules are incorporated into the stochastic fuzzy decision model's mathematical formulation. Decision-makers can maximize outcomes by considering stochastic factors and fuzzy logic with the model. The model uses an optimization technique to find the optimal choice variable values. A numerical example of manufacturing production planning illustrates the model's use. The results show that the stochastic fuzzy decision model may minimize production costs by calculating optimal production quantities depending on demand. The research concludes that the proposed approach helps manufacturing companies make decisions. Decision-makers can use the model to make educated judgments despite uncertainties and inaccurate information. Future study will explore additional aspects and integrate the model into decision support systems or industrial software. In dynamic and uncertain manufacturing contexts, the stochastic fuzzy decision model empowers manufacturing decision-makers to make optimal decisions","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114591760","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}

引用次数: 0