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Int. J. Fuzzy Log. Intell. Syst.最新文献

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Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation 车辆边缘计算中工作负载编排的数据流分类算法:比较评价
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2021-06-30 DOI: 10.5391/IJFIS.2021.21.2.101
Mu'ath Al-Tarawneh
{"title":"Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation","authors":"Mu'ath Al-Tarawneh","doi":"10.5391/IJFIS.2021.21.2.101","DOIUrl":"https://doi.org/10.5391/IJFIS.2021.21.2.101","url":null,"abstract":"This paper reports on the use of online data stream classification algorithms to support workload orchestration in vehicular edge computing environments. These algorithms can be used to predict the ability of available computational nodes to successfully handle computational tasks generated from vehicular applications. Several online data stream classification algorithms have been evaluated based on synthetic datasets generated from simulated vehicular edge computing environments. In addition, a multi-criteria decision analysis technique was utilized to rank the different algorithms based on their performance metrics. The evaluation results demonstrate that the considered algorithms can handle online classification operations with various trade-offs and dominance relations with respect to their obtained performance. In addition, the utilized multi-criteria decision analysis technique can efficiently rank various algorithms and identify the most appropriate algorithms to augment workload orchestration. Furthermore, the evaluation results show that the leveraging bagging algorithm, with an extremely fast decision tree base estimator, is able to maintain marked online classification performance and persistent competitive ranking among its counterparts for all datasets. Hence, it can be considered a promising choice to reinforce workload orchestration in vehicular edge computing environments.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116295262","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: 2
Hypergroupoids as Tools for Studying Blood Group Genetics 超类群作为研究血型遗传学的工具
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2021-06-30 DOI: 10.5391/IJFIS.2021.21.2.135
M. Munir, N. Kausar, Salahuddin, R. Anjum, Qingbing Xu, Waqas Ahmad
{"title":"Hypergroupoids as Tools for Studying Blood Group Genetics","authors":"M. Munir, N. Kausar, Salahuddin, R. Anjum, Qingbing Xu, Waqas Ahmad","doi":"10.5391/IJFIS.2021.21.2.135","DOIUrl":"https://doi.org/10.5391/IJFIS.2021.21.2.135","url":null,"abstract":"We initially introduce the concepts of an m -right ( m -left) hyperideal and an m -hyperideal in a hypergroupoid. The ideas behind an m -factor and a generalized m -factor are then introduced. Next, we demonstrate the existence and important properties of these sub-hyperstructures through theorems and examples. We then define the m -right ( m -left) consistent, m -consistent, m -intra-consistent, and m -simple hypergroupoids. Finally, we demonstrate that practical problems in biology, such as ABO blood group genetics, can be studied by defining these hypergroupoid substructures.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130013858","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
Enhancing Zero-Based Budgeting Under Fuzzy Environment 模糊环境下提高零基预算
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2021-06-30 DOI: 10.5391/IJFIS.2021.21.2.152
H. A. Khalifa, S. Alodhaibi
{"title":"Enhancing Zero-Based Budgeting Under Fuzzy Environment","authors":"H. A. Khalifa, S. Alodhaibi","doi":"10.5391/IJFIS.2021.21.2.152","DOIUrl":"https://doi.org/10.5391/IJFIS.2021.21.2.152","url":null,"abstract":"","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"23 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114129188","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
Distributed Medium Access Control Method through Inductive Reasoning 基于归纳推理的分布式媒体访问控制方法
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2021-06-30 DOI: 10.5391/IJFIS.2021.21.2.145
Jaesung Park, Changyong Yoon
{"title":"Distributed Medium Access Control Method through Inductive Reasoning","authors":"Jaesung Park, Changyong Yoon","doi":"10.5391/IJFIS.2021.21.2.145","DOIUrl":"https://doi.org/10.5391/IJFIS.2021.21.2.145","url":null,"abstract":"Wireless local area network (WLAN) uses a medium access control method based on the carrier sense multiple access with collision avoidance (CSMA/CA). In CSMA/CA, each station maintains a contention window by adjusting its size according to the perceived contention level. By making a station autonomously choose a waiting time randomly, using its current contention window size, CSMA/CA resolves the channel contention problem among a set of stations in a distributed manner. However, because the contention window size is limited, the packet collision probability increases sharply as the number of stations, with data to send, increases. To resolve this problem, we propose a novel medium access control method using a minority game. In the proposed method, each station learns the current contention level in a distributed manner and decides whether to send a packet using the acquired knowledge to decrease its packet collision probability. Through simulation studies, we show that compared with CSMA/CA and random selection methods, the proposed method decreases both the packet collision probability and the time interval between successful packet transmissions.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"451 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116179870","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
Application of Adaptive Neuro-Fuzzy Inference System for Evaluating Compressive Strength of Concrete 自适应神经模糊推理系统在混凝土抗压强度评价中的应用
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2021-06-30 DOI: 10.5391/IJFIS.2021.21.2.176
D. K. Sinha, R. Satavalekar, Senthil Kasilingam
{"title":"Application of Adaptive Neuro-Fuzzy Inference System for Evaluating Compressive Strength of Concrete","authors":"D. K. Sinha, R. Satavalekar, Senthil Kasilingam","doi":"10.5391/IJFIS.2021.21.2.176","DOIUrl":"https://doi.org/10.5391/IJFIS.2021.21.2.176","url":null,"abstract":"The objectives of this study are to develop a model for predicting the compressive strength of concrete using an adaptive neuro-fuzzy inference system (ANFIS) and validate the mix proportion using artificial neural networks (ANNs) and by experimentation. A model was developed, and the compressive strength was predicted using the ANFIS (with the subtractive clustering method of the fuzzy inference system) by MATLAB programming. In the present study, two ANFIS models were considered: ANFIS models-1 and -2. ANFIS model-1 was developed to predict the 3-day compressive strength, whereas ANFIS model-2 predicts the 28-day compressive strength by considering the 3-day compressive strength data obtained using ANFIS model-1. It was observed that the errors in the 3and 28-day compressive strengths were 6.33%, and 17.07%, respectively. Furthermore, experiments were performed for selective mixes—M40, M50, and M60—to verify the compressive strength obtained using the ANFIS model. The model results were verified against the experimental ones based on the mixes selected from the model, and the results were found to agree with the predicted ones, with a maximum deviation of 18%. Furthermore, an ANN model was developed to predict the compressive strength to verify the accuracy of the ANFIS model. The results predicted by the ANFIS and the ANN were compared with the original results available in the literature. A significant deviation was found between the ANN model results and the original results, however, the ANN model results presented the same trend as the original results. It was concluded that the ANFIS model results were highly consistent with the original results.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115650854","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: 2
A New Generalization of Hesitant and Interval-Valued Fuzzy Ideals of Ternary Semigroups 三元半群的犹豫理想和区间值模糊理想的新推广
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2021-06-30 DOI: 10.5391/IJFIS.2021.21.2.169
P. Julatha, Aiyared Iampan
{"title":"A New Generalization of Hesitant and Interval-Valued Fuzzy Ideals of Ternary Semigroups","authors":"P. Julatha, Aiyared Iampan","doi":"10.5391/IJFIS.2021.21.2.169","DOIUrl":"https://doi.org/10.5391/IJFIS.2021.21.2.169","url":null,"abstract":"The main aim of this article is to introduce the concept of a sup -hesitant fuzzy ideal, which is a generalization of a hesitant fuzzy ideal and an interval-valued fuzzy ideal, in a ternary semigroup. Some characterizations of a sup -hesitant fuzzy ideal are examined in terms of a fuzzy set, a hesitant fuzzy set, and an interval valued fuzzy set. Further, we discuss the relation between an ideal and a generalization of a characteristic hesitant fuzzy set and a characteristic interval-valued fuzzy set.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124418634","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: 3
New Soft Rough Set Approximations 新的软粗糙集近似
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2021-06-30 DOI: 10.5391/IJFIS.2021.21.2.123
Shawkat Alkhazaleh, E. Marei
{"title":"New Soft Rough Set Approximations","authors":"Shawkat Alkhazaleh, E. Marei","doi":"10.5391/IJFIS.2021.21.2.123","DOIUrl":"https://doi.org/10.5391/IJFIS.2021.21.2.123","url":null,"abstract":"The soft rough set model was introduced by Fing in 2011 and can be considered as a generalized rough set model, in which an interesting connection was established between two mathematical approaches to vagueness: rough sets and soft sets. It was also shown that Pawlak’s rough set model can be viewed as a special case of soft rough sets. There are two problems with this model in using this concept in real-life applications. The first problem is that some soft rough sets are not contained in their upper approximations, which contradicts Pawlak’s thoughts. The second problem is that the boundary region of any considered set, in the soft rough set model, must be decreased to make it possible to take a true decision of any application problem. In this study, the soft rough set model is modified to solve these problems. The basic properties of the modified approximations are introduced and supported with propositions and illustrative examples. Modified concepts can be viewed as a general mathematical model for qualitative and quantitative real-life problems. A comparison between the suggested approach to soft rough sets and the traditional soft rough set model is provided.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130626184","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
New Method of Internal Type-2 Fuzzy-Based CNN for Image Classification 基于内部2型模糊的CNN图像分类新方法
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.336
P. Murugeswari, S. Vijayalakshmi
{"title":"New Method of Internal Type-2 Fuzzy-Based CNN for Image Classification","authors":"P. Murugeswari, S. Vijayalakshmi","doi":"10.5391/ijfis.2020.20.4.336","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.336","url":null,"abstract":"Last two decades, neural network and fuzzy logic have been successfully implemented in intelligent systems. The fuzzy neural network system means the combination of fuzzy logic and neural network concepts, which includes the advantages of fuzzy logic and neural network. This FNN is applied in many scientific and engineering areas. Wherever there is an uncertainty associated with data fuzzy logic place a vital rule. The fuzzy set can represent and handles uncertainty information effectively. The main objective o f the FNN system is to achieve a high level of accuracy by including the fuzzy logic in either neural network structure, activation function or learning algorithms. In computer vision and intelligent system, Convlutional Neural Network has more popular architectures and their performance is excellent in many applications. In this paper fuzzy based CNN image classification methods are analysed, also interval type-2 fuzzy based CNN is proposed. From the experiment it is identified that the proposed method performance is well.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115475601","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: 3
Interval-Valued Fuzzy Graphs 区间值模糊图
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.316
Tarasankar Pramanik, Sovan Samanta, M. Pal
{"title":"Interval-Valued Fuzzy Graphs","authors":"Tarasankar Pramanik, Sovan Samanta, M. Pal","doi":"10.5391/ijfis.2020.20.4.316","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.316","url":null,"abstract":"Interval-valued fuzzy graphs (IVFGs) are a generalization of fuzzy graphs. In this article, the sum distance between vertices in an IVFG is introduced. This definition satisfies the metric properties. In addition, some important aspects related to eccentricity, radius, and diameter are proved. The necessary and sufficient conditions for a vertex to be eccentric are established. The relationship between eccentricities and the sum distance between two vertices is derived. An algorithm is presented to determine the sum distance between two vertices in interval-valued fuzzy graphs. Furthermore, some related theorems for the complete IVFG are deduced.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122594774","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 Decision Support System for Diagnosis of Some Liver Diseases in Educational Medical Institutions 教育医疗机构部分肝病诊断的模糊决策支持系统
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.358
A. A. Kamel, F. El-Mougi
{"title":"A Fuzzy Decision Support System for Diagnosis of Some Liver Diseases in Educational Medical Institutions","authors":"A. A. Kamel, F. El-Mougi","doi":"10.5391/ijfis.2020.20.4.358","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.358","url":null,"abstract":"Decision support systems improve medical diagnosis and minimize diagnostic errors. Existing diagnostic systems are often complex and exhibit limited performance on liver diseases, particularly the liver cancer. This paper presents a fuzzy decision support system for helping students diagnose some human liver diseases in educational medical institutions. The proposed system aims to improve real medical diagnosis processes. The approach has three basic steps: 1) symptoms-based diagnosis, 2) liver function-based diagnosis, and 3) image processingbased diagnosis. The proposed system employs two artificial intelligence techniques: fuzzy logic and image processing. The first is used for diagnosing liver diseases based on the liver function tests, while the second is used for diagnosing liver diseases such as the liver cancer, hepatitis, liver cirrhosis, liver fibrosis, and fatty liver. The proposed system combines two methods: the Mamdani inference and simulation method used in the MATLAB17 fuzzy logic toolbox, and the gray level co-occurrence matrix, for extracting the features of the secondorder statistical texture of images acquired using computed tomography, magnetic resonance imaging, or ultrasound, for various liver diseases. Our results reveal a very good agreement between expert-made and system-made diagnoses, suggesting high accuracy.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124524773","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: 3
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