Pub Date : 2023-06-16DOI: 10.47852/bonviewjcce3202847
M. Saeed, I. U. Din, Imtiaz Tariq, Harish Garg
This article discusses the results of an investigation into refined fuzzy soft sets, a novel variant of traditional fuzzy sets. Refined fuzzy soft sets provide a versatile method of data analysis, inspired by the need to deal with uncertainty and ambiguity in real-world data. This research expands on prior work in fuzzy set theory by investigating the nature and characteristics of refined fuzzy soft sets. They are useful in decision-making, pattern recognition, image processing, and control theory because of their capacity to deal with uncertainty, ambiguity, and the inclusion of expert information. This study analyzes these fuzzy set models and compares them to others in the field to reveal their advantages and disadvantages. The practical uses of enhanced fuzzy soft sets are also examined, along with possible future research strategies on this exciting new topic.
{"title":"Refined Fuzzy Soft Sets: Properties, Set-Theoretic Operations and Axiomatic Results","authors":"M. Saeed, I. U. Din, Imtiaz Tariq, Harish Garg","doi":"10.47852/bonviewjcce3202847","DOIUrl":"https://doi.org/10.47852/bonviewjcce3202847","url":null,"abstract":"This article discusses the results of an investigation into refined fuzzy soft sets, a novel variant of traditional fuzzy sets. Refined fuzzy soft sets provide a versatile method of data analysis, inspired by the need to deal with uncertainty and ambiguity in real-world data. This research expands on prior work in fuzzy set theory by investigating the nature and characteristics of refined fuzzy soft sets. They are useful in decision-making, pattern recognition, image processing, and control theory because of their capacity to deal with uncertainty, ambiguity, and the inclusion of expert information. This study analyzes these fuzzy set models and compares them to others in the field to reveal their advantages and disadvantages. The practical uses of enhanced fuzzy soft sets are also examined, along with possible future research strategies on this exciting new topic.","PeriodicalId":355809,"journal":{"name":"Journal of Computational and Cognitive Engineering","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130765369","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 : 2023-06-14DOI: 10.47852/bonviewjcce3202921
Francis J. Vasko
The Multiple Knapsack Assignment Problem (MKAP) is an interesting generalization of the Multiple Knapsack Problem which has logistical applications in transportation and shipping. In addition to trying to insert items into knapsacks in order to maximize the profit of the items in the knapsacks, the MKAP partitions the items into classes and only items from the same class can be inserted into a knapsack. In the literature, the Gurobi integer programming software has solved MKAPs with up to 1240 variables and 120 constraints in at most 20 minutes on a standard PC. In this article, using a standard PC and iteratively loosening the acceptable tolerance gap for 180 MKAPs with up to 20,100 variables and 1,120 constraints, we show that Gurobi can, on average, generate solutions that are guaranteed to be at most 0.17% from the optimums in 43 seconds. However, for very large MKAPs (over a million variables), Gurobi’s performance can be significantly improved when an initial feasible solution is provided. Specifically, using from the literature, a heuristic and 42 MKAP instances with over 6 million variables and nearly 90,000 constraints, Gurobi generated solutions guaranteed to be, on average, within 0.21% of the optimums in 10 minutes. This is a 99% reduction in the final solution bound (gap between the best Gurobi solution and the best upper bound) compared to the approach without initial solution inputs. Hence, a major objective of this article is to demonstrate for what size MKAP instances providing Gurobi with an initial heuristic solution significantly improves performance in terms of both execution time and solution quality.
{"title":"Efficiently Generating Bounded Solutions for Very Large Multiple Knapsack Assignment Problems","authors":"Francis J. Vasko","doi":"10.47852/bonviewjcce3202921","DOIUrl":"https://doi.org/10.47852/bonviewjcce3202921","url":null,"abstract":"The Multiple Knapsack Assignment Problem (MKAP) is an interesting generalization of the Multiple Knapsack Problem which has logistical applications in transportation and shipping. In addition to trying to insert items into knapsacks in order to maximize the profit of the items in the knapsacks, the MKAP partitions the items into classes and only items from the same class can be inserted into a knapsack. In the literature, the Gurobi integer programming software has solved MKAPs with up to 1240 variables and 120 constraints in at most 20 minutes on a standard PC. In this article, using a standard PC and iteratively loosening the acceptable tolerance gap for 180 MKAPs with up to 20,100 variables and 1,120 constraints, we show that Gurobi can, on average, generate solutions that are guaranteed to be at most 0.17% from the optimums in 43 seconds. However, for very large MKAPs (over a million variables), Gurobi’s performance can be significantly improved when an initial feasible solution is provided. Specifically, using from the literature, a heuristic and 42 MKAP instances with over 6 million variables and nearly 90,000 constraints, Gurobi generated solutions guaranteed to be, on average, within 0.21% of the optimums in 10 minutes. This is a 99% reduction in the final solution bound (gap between the best Gurobi solution and the best upper bound) compared to the approach without initial solution inputs. Hence, a major objective of this article is to demonstrate for what size MKAP instances providing Gurobi with an initial heuristic solution significantly improves performance in terms of both execution time and solution quality.","PeriodicalId":355809,"journal":{"name":"Journal of Computational and Cognitive Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121470224","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 : 2023-06-09DOI: 10.47852/bonviewjcce3202768
Pamraat Parmar, Timothy Sands
This study was conducted to understand the response of hearing aids to different inputs and propose a novel technique to significantly improve performance of hearing aid implants. The motivation behind this study is to represent behavior of hearing aid system with simple input-output relation rather than complicated models. This representation offered a better understanding of the system and inspired an innovation to improve the hearing aid implants. A model of a hearing aid system called cochlear transplants is generated and used to simulate the system response. Using multiple methods, simplified input-output relations are derived. Results from these methods are compared and conclusions are drawn regarding which method is best for this application. One of the methods used resulted in 69.7 % error measure reduction compared to the benchmark method. This method was later used to produce a simplified model, which was then used as the basis for analysis of different configurations. A qualitative comparison of model was made, and significant improvement of cochlear transplants was achieved.
{"title":"Hearing Aid System Response Improvement","authors":"Pamraat Parmar, Timothy Sands","doi":"10.47852/bonviewjcce3202768","DOIUrl":"https://doi.org/10.47852/bonviewjcce3202768","url":null,"abstract":"This study was conducted to understand the response of hearing aids to different inputs and propose a novel technique to significantly improve performance of hearing aid implants. The motivation behind this study is to represent behavior of hearing aid system with simple input-output relation rather than complicated models. This representation offered a better understanding of the system and inspired an innovation to improve the hearing aid implants. A model of a hearing aid system called cochlear transplants is generated and used to simulate the system response. Using multiple methods, simplified input-output relations are derived. Results from these methods are compared and conclusions are drawn regarding which method is best for this application. One of the methods used resulted in 69.7 % error measure reduction compared to the benchmark method. This method was later used to produce a simplified model, which was then used as the basis for analysis of different configurations. A qualitative comparison of model was made, and significant improvement of cochlear transplants was achieved.","PeriodicalId":355809,"journal":{"name":"Journal of Computational and Cognitive Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129383094","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 : 1900-01-01DOI: 10.47852/bonviewjcce32021141
H. Di̇nçer, Serkan Eti, S. Yuksel, Yasar Gokalp, Büşra Çelebi
Effective risk management plays an important role to improve renewable energy technology investments. Because of this issue, necessary actions must be implemented to effectively manage these risks. However, having high costs is the biggest disadvantage of the implementation of these actions. Therefore, it is not financially possible to implement different strategies together. In other words, it is necessary to identify the most important of these strategies. Accordingly, the purpose of this study is to make a priority evaluation for the risk strategies related to renewable energy technologies in hospitals. For this purpose, a new model is generated with SF TOP-DEMATEL technique. In this process, significant indicators are defined based on literature evaluations.In the next process, the weights of these indicators are calculated. The main contribution of this study is that a new technique is proposed by the name of TOP-DEMATEL. In this scope, the finals steps of TOPSIS are integrated to the analysis process of DEMATEL to overcome criticisms for classical DEMATEL technique. Moreover, a priority evaluation is carried out to understand the most critical risk management strategies in renewable energy technology investments. With the help of this analysis, it can be much easier to take risk management actions without having financial difficulties. It is determined that the weighting results of the criteria are quite similar for different t values. This situation identifies that the proposed model provides coherent and reliable results. It is concluded that government support is the most important strategy in this context. Additionally, technological improvements also play a crucial role for this situation. It is strongly recommended that governments should establish appropriate legal and regulatory frameworks to promote renewable energy projects. These frameworks can facilitate the financing and licensing of projects and offer economic incentives such as tax incentives and subsidies. Additionally, governments should also provide financial support such as incentives, grant programs, and low-interest loans.
{"title":"Strategy Generation for Risk Minimization of Renewable Energy Technology Investments in Hospitals with SF TOP-DEMATEL Methodology","authors":"H. Di̇nçer, Serkan Eti, S. Yuksel, Yasar Gokalp, Büşra Çelebi","doi":"10.47852/bonviewjcce32021141","DOIUrl":"https://doi.org/10.47852/bonviewjcce32021141","url":null,"abstract":"Effective risk management plays an important role to improve renewable energy technology investments. Because of this issue, necessary actions must be implemented to effectively manage these risks. However, having high costs is the biggest disadvantage of the implementation of these actions. Therefore, it is not financially possible to implement different strategies together. In other words, it is necessary to identify the most important of these strategies. Accordingly, the purpose of this study is to make a priority evaluation for the risk strategies related to renewable energy technologies in hospitals. For this purpose, a new model is generated with SF TOP-DEMATEL technique. In this process, significant indicators are defined based on literature evaluations.In the next process, the weights of these indicators are calculated. The main contribution of this study is that a new technique is proposed by the name of TOP-DEMATEL. In this scope, the finals steps of TOPSIS are integrated to the analysis process of DEMATEL to overcome criticisms for classical DEMATEL technique. Moreover, a priority evaluation is carried out to understand the most critical risk management strategies in renewable energy technology investments. With the help of this analysis, it can be much easier to take risk management actions without having financial difficulties. It is determined that the weighting results of the criteria are quite similar for different t values. This situation identifies that the proposed model provides coherent and reliable results. It is concluded that government support is the most important strategy in this context. Additionally, technological improvements also play a crucial role for this situation. It is strongly recommended that governments should establish appropriate legal and regulatory frameworks to promote renewable energy projects. These frameworks can facilitate the financing and licensing of projects and offer economic incentives such as tax incentives and subsidies. Additionally, governments should also provide financial support such as incentives, grant programs, and low-interest loans.","PeriodicalId":355809,"journal":{"name":"Journal of Computational and Cognitive Engineering","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122487574","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 : 1900-01-01DOI: 10.47852/bonviewjcce32021049
Rahul Paul, K. Das
Machine learning (ML) is rapidly evolving, leading to numerous theoretical advancements and widespread applications across multiple fields. The goal of ML is to enable machines to carry out cognitive tasks by acquiring knowledge from past encounters and resolving intricate issues despite varying circumstances that deviate from previous instances. Supervised Learning (SL) being one of the most popular type of ML has become an area of significant strategic importance due to its practical applications, data collection, and computing power's exponential growth. On the other hand, optimization is a crucial component of ML that has garnered significant attention from researchers. Numerous proposals have been made one after another for solving optimization problems or enhancing optimization techniques in the field of ML. A comprehensive review and application of optimization methods from the perspective of ML is crucial to guide the development of both optimization and ML research. This article presents information specifically on the area of SL and a wide range of optimization methods, applied in conjunction to address various scientific issues. Additionally, this article explores some of the challenges and open problems in optimizing SL models.
{"title":"Trends of Optimization Algorithms from Supervised Learning Perspective","authors":"Rahul Paul, K. Das","doi":"10.47852/bonviewjcce32021049","DOIUrl":"https://doi.org/10.47852/bonviewjcce32021049","url":null,"abstract":"Machine learning (ML) is rapidly evolving, leading to numerous theoretical advancements and widespread applications across multiple fields. The goal of ML is to enable machines to carry out cognitive tasks by acquiring knowledge from past encounters and resolving intricate issues despite varying circumstances that deviate from previous instances. Supervised Learning (SL) being one of the most popular type of ML has become an area of significant strategic importance due to its practical applications, data collection, and computing power's exponential growth. On the other hand, optimization is a crucial component of ML that has garnered significant attention from researchers. Numerous proposals have been made one after another for solving optimization problems or enhancing optimization techniques in the field of ML. A comprehensive review and application of optimization methods from the perspective of ML is crucial to guide the development of both optimization and ML research. This article presents information specifically on the area of SL and a wide range of optimization methods, applied in conjunction to address various scientific issues. Additionally, this article explores some of the challenges and open problems in optimizing SL models.","PeriodicalId":355809,"journal":{"name":"Journal of Computational and Cognitive Engineering","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121740648","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 : 1900-01-01DOI: 10.47852/bonviewjcce32021062
S. Haghshenas, V. Astarita, G. Guido, Mohammad Hassan Mobini Seraji, Paola Andrea Aldana Gonzalez, Ahmad Haghdadi, Sina Shaffiee Haghshenas
Traffic flow analysis is an interesting study topic in transportation studies. A better understanding of traffic flow is essential for more effective traffic reduction methods. Because managing traffic flow in cities is getting more complicated, we need more methodical ways to deal with these problems. Machine learning techniques have been suggested as a possible solution because they can process great amounts of data and give insights that can be used to help make decisions about how to manage traffic. The main objective of this research is to conduct a comprehensive examination of the quantitative and qualitative aspects of utilizing machine learning techniques in the management of traffic flow. Using the Web of Science (WOS) platform, documents from January 2007 to April 2023 were assessed. The study found that traffic flow management has been using machine learning techniques more and more over the past few years. This study shows the different approaches and methods that were used, as well as the results and limits of these methods. The results recommend that machine learning can be a useful tool for managing traffic flow in cities, but further investigation is warranted to gain a complete comprehension of both the advantages and disadvantages of the subject under scrutiny.
交通流分析是交通研究中一个有趣的研究课题。更好地了解交通流量对于采取更有效的减少交通的方法至关重要。由于管理城市交通流量变得越来越复杂，我们需要更有条理的方法来处理这些问题。机器学习技术被认为是一种可能的解决方案，因为它们可以处理大量数据，并提供可用于帮助制定如何管理流量的决策的见解。本研究的主要目的是对在交通流量管理中利用机器学习技术的定量和定性方面进行全面检查。利用Web of Science (WOS)平台，对2007年1月至2023年4月的文献进行了评估。研究发现，在过去的几年里，交通流量管理越来越多地使用机器学习技术。本研究展示了所使用的不同途径和方法，以及这些方法的结果和局限性。研究结果表明，机器学习可以成为管理城市交通流量的有用工具，但需要进一步的研究来全面了解该主题的优点和缺点。
{"title":"Assessment of Machine Learning Techniques and Traffic Flow: A Qualitative and Quantitative Analysis","authors":"S. Haghshenas, V. Astarita, G. Guido, Mohammad Hassan Mobini Seraji, Paola Andrea Aldana Gonzalez, Ahmad Haghdadi, Sina Shaffiee Haghshenas","doi":"10.47852/bonviewjcce32021062","DOIUrl":"https://doi.org/10.47852/bonviewjcce32021062","url":null,"abstract":"Traffic flow analysis is an interesting study topic in transportation studies. A better understanding of traffic flow is essential for more effective traffic reduction methods. Because managing traffic flow in cities is getting more complicated, we need more methodical ways to deal with these problems. Machine learning techniques have been suggested as a possible solution because they can process great amounts of data and give insights that can be used to help make decisions about how to manage traffic. The main objective of this research is to conduct a comprehensive examination of the quantitative and qualitative aspects of utilizing machine learning techniques in the management of traffic flow. Using the Web of Science (WOS) platform, documents from January 2007 to April 2023 were assessed. The study found that traffic flow management has been using machine learning techniques more and more over the past few years. This study shows the different approaches and methods that were used, as well as the results and limits of these methods. The results recommend that machine learning can be a useful tool for managing traffic flow in cities, but further investigation is warranted to gain a complete comprehension of both the advantages and disadvantages of the subject under scrutiny.","PeriodicalId":355809,"journal":{"name":"Journal of Computational and Cognitive Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129155169","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 : 1900-01-01DOI: 10.47852/bonviewjcce32021070
Farhana Islam, Tawhida Akand, Sohag Kabir
The rapid development of Internet of Things (IoT) enabled systems in public and private spaces offers consumers numerous conveniences. Among different internet-connected systems, the use of e-health systems is growing rapidly. The utilization of IoT devices and cloud-fog network technologies have made e-healthcare provision more convenient. While providing valuable services to the healthcare sector, like any other IoT-enable systems it is putting pressure on energy, an essential element of life. Therefore, it is imperative to know the energy consumption model of e-health systems. Considering the importance of energy consumption in IoT-based systems, this article develops a cloud-fog-based e-health system and makes it energy efficient by understanding energy consumption at different layers of communication. Moreover, how fog integration with the cloud reduces energy consumption and delays at different stages of communication is discussed.
{"title":"Energy Efficient Real-Time E-Healthcare System Based on Fog Computing","authors":"Farhana Islam, Tawhida Akand, Sohag Kabir","doi":"10.47852/bonviewjcce32021070","DOIUrl":"https://doi.org/10.47852/bonviewjcce32021070","url":null,"abstract":"The rapid development of Internet of Things (IoT) enabled systems in public and private spaces offers consumers numerous conveniences. Among different internet-connected systems, the use of e-health systems is growing rapidly. The utilization of IoT devices and cloud-fog network technologies have made e-healthcare provision more convenient. While providing valuable services to the healthcare sector, like any other IoT-enable systems it is putting pressure on energy, an essential element of life. Therefore, it is imperative to know the energy consumption model of e-health systems. Considering the importance of energy consumption in IoT-based systems, this article develops a cloud-fog-based e-health system and makes it energy efficient by understanding energy consumption at different layers of communication. Moreover, how fog integration with the cloud reduces energy consumption and delays at different stages of communication is discussed.","PeriodicalId":355809,"journal":{"name":"Journal of Computational and Cognitive Engineering","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127484894","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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