Journal of Intelligent & Fuzzy Systems最新文献

On the explanation of COVID-19 blood test variables using fuzzy models 利用模糊模型解释 COVID-19 血液测试变量

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-23 DOI: 10.3233/jifs-219372

Arturo Téllez-Velázquez, Pierre A. Delice, Rafael Salgado-Leyva, Raúl Cruz-Barbosa

{"title":"On the explanation of COVID-19 blood test variables using fuzzy models","authors":"Arturo Téllez-Velázquez, Pierre A. Delice, Rafael Salgado-Leyva, Raúl Cruz-Barbosa","doi":"10.3233/jifs-219372","DOIUrl":"https://doi.org/10.3233/jifs-219372","url":null,"abstract":"This paper performs an analysis comparing two evolutionary explainable fuzzy models that make inferences in a pipeline with a blood test data set for COVID-19 classification. Firstly, data is preprocessed by the following stages: cleaning, imputation and ranking feature selection. Later, we perform a comparative analysis between several clustering methods used in an Evolutionary Clustering-Structured Fuzzy Classifier (ECSFC) to solve this classification problem using the Differential Evolution (DE) algorithm. Complementarily, we find that the Fuzzy Decision Tree model produces similar performance when is tuned with the DE algorithm (EFDT). The obtained results show that, simpler models are easier to explain qualitatively, i.e., increasing the number of clusters in ECSFC model or the maximum depth of the tree in EFDT model, does not necessarily help to obtain simplified and accurate models. In addition, although the EFDT model is by itself an intuitively explainable model, the ECSFC, with the help of the proposed Weighted Stacked Features Plot, generates more intuitive models that allow not only highlighting the features and the linguistic terms that defines a patient with COVID-19, but also allows users to visualize in a single graph and in specific colors the analyzed classes.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140211152","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 image registration for analysis of multisource eye fundus images 用于分析多源眼底图像的稳健图像配准技术

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-23 DOI: 10.3233/jifs-219374

Edgar López-Jasso, E. Felipe-Riverón, José E. Valdez-Rodríguez

{"title":"Robust image registration for analysis of multisource eye fundus images","authors":"Edgar López-Jasso, E. Felipe-Riverón, José E. Valdez-Rodríguez","doi":"10.3233/jifs-219374","DOIUrl":"https://doi.org/10.3233/jifs-219374","url":null,"abstract":" This study underscores the crucial role of image preprocessing in enhancing the outcomes of multimodal image registration tasks using scale-invariant feature selection. The primary focus is on registering two types of retinal images, assessing a methodology’s performance on a set of retinal image pairs, including those with and without microaneurysms. Each pair comprises a color optical image and a gray-level fluorescein image, presenting distinct characteristics and captured under varying conditions. The SIFT methodology, encompassing five stages, with preprocessing as the initial and pivotal stage, is employed for image registration. Out of 35 test retina image pairs, 33 (94.28%) were successfully registered, with the inability to extract features hindering automatic registration in the remaining pairs. Among the registered pairs, 42.42% were retinal images without microaneurysms, and 57.57% had microaneurysms. Instead of simultaneous registration of all channels, independent registration of preprocessed images in each channel proved more effective. The study concludes with an analysis of the fifth registration’s resulting image to detect abnormalities or pathologies, highlighting the challenges encountered in registering blue channel images due to high intrinsic noise.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140210637","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

Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches 使用深度递归神经网络和环境学习方法在边缘云系统中进行动态任务调度

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-23 DOI: 10.3233/jifs-236838

S.K. Ammavasai

{"title":"Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches","authors":"S.K. Ammavasai","doi":"10.3233/jifs-236838","DOIUrl":"https://doi.org/10.3233/jifs-236838","url":null,"abstract":"The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140210874","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 self-disclosure ESG rating method based on the fuzzy set and reward mechanism of disclosure 基于模糊集和披露奖励机制的自我披露 ESG 评级方法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-23 DOI: 10.3233/jifs-230777

Songyi Yin, Yu Wang, Yelin Fu

{"title":"A self-disclosure ESG rating method based on the fuzzy set and reward mechanism of disclosure","authors":"Songyi Yin, Yu Wang, Yelin Fu","doi":"10.3233/jifs-230777","DOIUrl":"https://doi.org/10.3233/jifs-230777","url":null,"abstract":"The environmental, social, and governance (ESG) rating method is a powerful tool that can help investors to judge the investment value of companies based on the information disclosure. However, mainstream ESG rating methods ignore the distinction between companies with incomplete information disclosure and companies without information disclosure, which decreases the initiative and enthusiasm of companies to disclose information. In this study, a self-disclosure ESG (SDESG) rating method is proposed to evaluate companies’ ESG performance capabilities. First, based on the fuzzy set, fuzzy data is defined and applied to the SDESG rating method. Second, analogous to the academic reward system of a university, a reward mechanism of disclosure is used in the SDESG rating method. Finally, the effectiveness and reliability of the SDESG rating method are demonstrated through Refinitiv’s case. The results show that the SDESG rating method can distinguish companies with incomplete information disclosure from companies without information disclosure and allow companies that proactively disclose information to obtain better ESG scores under each industry. The implications of the study would increase companies’ enthusiasm to disclose information and maintain transparency within a company.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140211031","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

Data-driven control of a five-bar parallel robot with compliant joints 带顺应性关节的五杆并联机器人的数据驱动控制

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-23 DOI: 10.3233/jifs-219364

Angel Ramírez-Martínez, J. E. Chong-Quero, Héctor Cervantes-Culebro, C. Cruz-Villar

引用次数: 0

An efficient two-heuristic algorithm for the student-project allocation with preferences over projects 学生项目分配的高效双启发式算法（对项目有偏好

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-23 DOI: 10.3233/jifs-236300

Hoang Huu Viet, Nguyen Thi Uyen, Son Thanh Cao, Long Giang Nguyen

{"title":"An efficient two-heuristic algorithm for the student-project allocation with preferences over projects","authors":"Hoang Huu Viet, Nguyen Thi Uyen, Son Thanh Cao, Long Giang Nguyen","doi":"10.3233/jifs-236300","DOIUrl":"https://doi.org/10.3233/jifs-236300","url":null,"abstract":"The Student-Project Allocation with preferences over Projects problem is a many-to-one stable matching problem that aims to assign students to projects in project-based courses so that students and lecturers meet their preference and capacity constraints. In this paper, we propose an efficient two-heuristic algorithm to solve this problem. Our algorithm starts from an empty matching and iteratively constructs a maximum stable matching of students to projects. At each iteration, our algorithm finds an unassigned student and assigns her/his most preferred project to her/him to form a student-project pair in the matching. If the project or the lecturer who offered the project is over-subscribed, our algorithm uses two heuristic functions, one for the over-subscribed project and the other for the over-subscribed lecturer, to remove a student-project pair in the matching. To reach a stable matching of a maximum size, our two heuristics are designed such that the removed student has the most opportunities to be assigned to some project in the next iterations. Experimental results show that our algorithm is efficient in execution time and solution quality for solving the problem.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140210784","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

CycleGAN generated pneumonia chest x-ray images: Evaluation with vision transformer CycleGAN 生成肺炎胸部 X 光图像：使用视觉转换器进行评估

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-23 DOI: 10.3233/jifs-219373

Gerardo Lugo-Torres, José E. Valdez-Rodríguez, D. Peralta-Rodríguez

{"title":"CycleGAN generated pneumonia chest x-ray images: Evaluation with vision transformer","authors":"Gerardo Lugo-Torres, José E. Valdez-Rodríguez, D. Peralta-Rodríguez","doi":"10.3233/jifs-219373","DOIUrl":"https://doi.org/10.3233/jifs-219373","url":null,"abstract":"The use of generative models in image synthesis has become increasingly prevalent. Synthetic medical imaging data is of paramount importance, primarily because medical imaging data is scarce, costly, and encumbered by legal considerations pertaining to patient confidentiality. Synthetic medical images offer a potential answer to these issues. The predominant approaches primarily assess the quality of images and the degree of resemblance between these images and the original ones employed for their generation.The central idea of the work can be summarized in the question: Do the performance metrics of Frechet Inception Distance(FID) and Inception Score(IS) in the Cycle-consistent Generative Adversarial Networks (CycleGAN) model are adequate to determine how real a generated chest x-ray pneumonia image is? In this study, a CycleGAN model was employed to produce artificial images depicting 3 classes of chest x-ray pneumonia images: general(any type), bacterial, and viral pneumonia. The quality of the images were evaluated assessing and contrasting 3 criteria: performance metric of CycleGAN model, clinical assessment of respiratory experts and the results of classification of a visual transformer(ViT). The overall results showed that the evaluation metrics of the CycleGAN are insufficient to establish realism in generated medical images.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140210493","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

Application of fuzzy prediction control model based on neural network in teaching resource recommendation and matching 基于神经网络的模糊预测控制模型在教学资源推荐与匹配中的应用

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-23 DOI: 10.3233/jifs-233265

Shuai Shao, Dongwei Li

{"title":"Application of fuzzy prediction control model based on neural network in teaching resource recommendation and matching","authors":"Shuai Shao, Dongwei Li","doi":"10.3233/jifs-233265","DOIUrl":"https://doi.org/10.3233/jifs-233265","url":null,"abstract":"As technology evolves, the allocation and use of educational resources becomes increasingly complex. Due to the many factors involved in recommending and matching English education resources, traditional predictive control models are no longer adequate. Therefore, fuzzy predictive control models based on neural networks have emerged. To increase the effectiveness and efficiency of using English educational resources (EER), this research aims to create a neural network-based fuzzy predictive control model (T-S-BPNN) for resource suggestion and matching. The results of the study show that the T-S-BPNN model α proposed in the study starts from 0 and increases sequentially by 0.1 up to 1, observing the change in MAE values. The experiment’s findings demonstrate that the value of MAE is lowest at values around 0.5. The T-S-BPNN model, on the other hand, gradually plateaued in its adaptation rate up to 7 runs, reaching about 9.8%. The accuracy rate peaked at 0.843 when the number of recommendations reached 7. The recall rate also peaked at 0.647 when the number of recommended English courses reached 7. The R-value for each set hovered around 0.97, which is a good fit. And the R-value of the training set is 0.97024, which can indicate that the T-S-BPNN model model proposed in the study fits well. It indicates that the algorithm proposed in the study is highly practical.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140211321","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 hybrid LSTM-Graph Attention Network for cross-subject analysis on thinking and speaking state using EEG signals 利用脑电信号对思维和说话状态进行跨主体分析的新型 LSTM-Graph 注意力混合网络

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-22 DOI: 10.3233/jifs-233143

N. Ramkumar, D. Karthika Renuka

{"title":"A novel hybrid LSTM-Graph Attention Network for cross-subject analysis on thinking and speaking state using EEG signals","authors":"N. Ramkumar, D. Karthika Renuka","doi":"10.3233/jifs-233143","DOIUrl":"https://doi.org/10.3233/jifs-233143","url":null,"abstract":"In recent times, the rapid advancement of deep learning has led to increased interest in utilizing Electroencephalogram (EEG) signals for automatic speech recognition. However, due to the significant variation observed in EEG signals from different individuals, the field of EEG-based speech recognition faces challenges related to individual differences across subjects, which ultimately impact recognition performance. In this investigation, a novel approach is proposed for EEG-based speech recognition that combines the capabilities of Long Short Term Memory (LSTM) and Graph Attention Network (GAT). The LSTM component of the model is designed to process sequential patterns within the data, enabling it to capture temporal dependencies and extract pertinent features. On the other hand, the GAT component exploits the interconnections among data points, which may represent channels, nodes, or features, in the form of a graph. This innovative model not only delves deeper into the connection between connectivity features and thinking as well as speaking states, but also addresses the challenge of individual disparities across subjects. The experimental results showcase the effectiveness of the proposed approach. When considering the thinking state, the average accuracy for single subjects and cross-subject are 65.7% and 67.3% respectively. Similarly, for the speaking state, the average accuracies were 65.4% for single subjects and 67.4% for cross-subject conditions, all based on the KaraOne dataset. These outcomes highlight the model’s positive impact on the task of cross-subject EEG speech recognition. The motivations for conducting cross subject are real world applicability, Generalization, Adaptation and personalization and performance evaluation.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140211209","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

Comprehensive evaluation measures of nonlinear estimation algorithm performance 非线性估计算法性能的综合评估措施

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-22 DOI: 10.3233/jifs-231376

Weishi Peng, Yangwang Fang, Yongzhong Ma

引用次数: 0

Journal of Intelligent &amp; Fuzzy Systems最新文献

Journal of Intelligent & Fuzzy Systems最新文献