International Journal of Intelligent Systems最新文献

{"title":"A Preference Analysis Method Considering the Asymmetric Impact and Competitors Driven by Group Wisdom and Influence Mining From Online Reviews","authors":"Ru-Xin Nie,&nbsp;Meng-Meng Tu,&nbsp;Zhang-Peng Tian","doi":"10.1155/int/2901174","DOIUrl":"https://doi.org/10.1155/int/2901174","url":null,"abstract":"<div>\u0000 <p>Consumers increasingly post online reviews concerning products or services on the social media platforms. Online reviews have become a reliable data source for extracting consumer preferences. Importance-performance analysis (IPA) is widely used in preference analysis, but it normally ignores the effects of the performance of competitors as well as the asymmetric between requirements and satisfaction. Therefore, this study extends the IPA model and proposes a preference analysis method that considers asymmetric impact and competitors based on group wisdom and influence mining from online reviews. To do so, after identifying service attributes from online reviews, preferences hidden in massive online reviews are quantified using linguistic distribution assessments. Then, the influence of reviewers is measured by introducing both the relationship influence and the information influence from different types of reviewers as group wisdom to determine the performance of service attributes. The concept of competitive dominance degree is defined as the degree of competitive advantage relative to that of competitors under realistic contexts. The preference analysis method of this study reflects group wisdom and competitive environments more realistically. Its applicability and effectiveness have been testified in the hospitality industry.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2901174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SCA-Net: Seasonal Cycle-Aware Model Emphasizing Global and Local Features for Time Series Forecasting","authors":"Min Wang,&nbsp;Hua Wang,&nbsp;Zhen Hua,&nbsp;Fan Zhang","doi":"10.1155/int/4567807","DOIUrl":"https://doi.org/10.1155/int/4567807","url":null,"abstract":"<div>\u0000 <p>Recent advances in transformer architectures have significantly improved performance in time-series forecasting. Despite the excellent performance of attention mechanisms in global modeling, they often overlook local correlations between seasonal cycles. Drawing on the idea of trend-seasonality decomposition, we design a seasonal cycle-aware time-series forecasting model (SCA-Net). This model uses a dual-branch extraction architecture to decompose time series into seasonal and trend components, modeling them based on their intrinsic features, thereby improving prediction accuracy and model interpretability. We propose a method combining global modeling and local feature extraction within seasonal cycles to capture the global view and explore latent features. Specifically, we introduce a frequency-domain attention mechanism for global modeling and use multiscale dilated convolution to capture local correlations within each cycle, ensuring more comprehensive and accurate feature extraction. For simpler trend components, we apply a regression method and merge the output with the seasonal components via residual connections. To improve seasonal cycle identification, we design an adaptive decomposition method that extracts trend components layer by layer, enabling better decomposition and more useful information extraction. Extensive experiments on eight classic datasets show that SCA-Net achieves a performance improvement of 12.1% in multivariate forecasting and 15.6% in univariate forecasting compared to the baseline.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4567807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Unsupervised Learning Model for Intelligent Machine-Failure Prediction With Heterogeneous Sensors","authors":"Jonghee Park,&nbsp;Jinyoung Kim,&nbsp;Dong-Won Lee,&nbsp;Hyoungmin Kim,&nbsp;Dae-Geun Hong","doi":"10.1155/int/3346341","DOIUrl":"https://doi.org/10.1155/int/3346341","url":null,"abstract":"<div>\u0000 <p>This study proposes a system that uses unsupervised learning to autonomously identify sensor data which suggest that a machine may soon fail. The system predicts three failure modes in the servo motor of an injection machine by learning multivariate data from heterogeneous sensors. The unsupervised learning model predicted failures with an average F1 score of 0.9958. A case study in an actual shop verified the system’s practical applicability. This shop is a factory that runs 27 injection machines of various tonnages. Results confirmed the ease of retraining the unsupervised learning model and demonstrated its portability. The use of an unsupervised learning model eliminates the difficulties and dependencies associated with data acquisition for supervised learning models. The case study indicated that the use of the proposed failure-prediction program can reduce maintenance costs by up to $US 140,000/y. It can be applied to various machines across different industries.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3346341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced Approach for Predicting Breast Cancer Using Different Deep Learning Algorithms and Explainable AI Techniques in an IoT Environment","authors":"Belgacem Bouallegue,&nbsp;Yasser M. Abd El-Latif,&nbsp;Hosam El-Sofany,&nbsp;Islam A. T. F. Taj-Eddin","doi":"10.1155/int/8884481","DOIUrl":"https://doi.org/10.1155/int/8884481","url":null,"abstract":"<div>\u0000 <p>Breast cancer is the primary cause of death for women around the world, necessitating the development of highly accurate, interpreted, and technologically advanced predictive approaches to support early diagnosis and treatment. In this research, we introduce a deep learning (DL) model for predicting breast cancer using both public and private datasets. The model uses the internet of things (IoT) to improve data collection and real-time monitoring, and it also uses the SMOTE method to resolve issues of class imbalance. The proposed model combines an explainable AI approach with SHAP values to ensure model interpretability. To identify the best DL algorithm for this method, we assess and compare six different DL algorithms: temporal convolutional networks (TCNs), neural factorization machines (NFMs), long short–term memory (LSTM) networks, recurrent neural networks (RNNs), gated recurrent units (GRUs), and deep kernel learning (DKL). IoT devices allow for the continuous acquisition of patient data, which, when integrated with our predictive models, improve the capacity for early detection. Reliable cancer detection relies on our method’s enhanced predictive accuracy and sensitivity. Furthermore, we offer crucial transparency in clinical settings by using SHAP to give detailed explanations of model decisions. By employing thorough statistical analysis and cross-validation, we guarantee that our model is resilient and can be applied to various patient populations. The results show that our proposed IoT integrated method has the potential to improve prediction performance and boost confidence in AI-powered medical diagnostics by making them more accessible and easier to use. From a performance perspective, the proposed approach, which uses the TCN algorithm and SMOTE, achieved the best accuracy for BC prediction. With the public dataset, the experimental results were 99.44%, 100.0%, 99.01%, 98.75%, 99.37%, and 99.89% for accuracy, sensitivity, specificity, precision, F1-score, and AUC, respectively. The experimental results for accuracy, sensitivity, specificity, precision, F1-score, and AUC using the private dataset were 97.33%, 93.33%, 100%, 100%, 96.55%, and 99.48%, respectively. On the other hand, with the combined datasets, the TCN algorithm achieved 100% for all performance metrics.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8884481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wildfire Smoke Detection System: Model Architecture, Training Mechanism, and Dataset","authors":"Chong Wang,&nbsp;Chen Xu,&nbsp;Adeel Akram,&nbsp;Zhong Wang,&nbsp;Zhilin Shan,&nbsp;Qixing Zhang","doi":"10.1155/int/1610145","DOIUrl":"https://doi.org/10.1155/int/1610145","url":null,"abstract":"<div>\u0000 <p>Vanilla Transformers focus on semantic relevance between mid- to high-level features and are not good at extracting smoke features, as they overlook subtle changes in low-level features like color, transparency, and texture, which are essential for smoke recognition. To address this, we propose the cross contrast patch embedding (CCPE) module based on the Swin Transformer. This module leverages multiscale spatial contrast information in both vertical and horizontal directions to enhance the network’s discrimination of underlying details. By combining cross contrast with the transformer, we exploit the advantages of the transformer in the global receptive field and context modeling while compensating for its inability to capture very low-level details, resulting in a more powerful backbone network tailored for smoke recognition tasks. In addition, we introduce the separable negative sampling mechanism (SNSM) to address supervision signal confusion during training and release the SKLFS-WildFire test dataset, the largest real-world wildfire test set to date, for systematic evaluation. Extensive testing and evaluation on the benchmark dataset FIgLib and the SKLFS-WildFire test dataset show significant performance improvements of the proposed method over baseline detection models.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1610145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the Repertory Grid: New Approaches to Constructivist Knowledge Acquisition Tool Development","authors":"Jeffrey M. Bradshaw, Kenneth M. Ford, Jack R. Adams‐Webber, John H. Boose","doi":"10.1002/j.1098-111x.1993.tb00007.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00007.x","url":null,"abstract":"<jats:italic>Personal construct theory</jats:italic> provides both a plausible theoretical foundation for knowledge acquisition and a practical approach to modeling. Yet, only a fraction of the ideas latent in this theory have been tapped. Recently, several researchers have been taking another look at the theory, to discover new ways that it can shed light on the foundations and practice of knowledge acquisition. These efforts have led to the development of a new generation of constructivist knowledge acquisition systems: DDUCKS, ICONKAT, and KSSn/KRS. These tools extend repertory grid techniques in various ways and integrate them with ideas springing from complementary perspectives. New understandings of relationships between personal construct theory, assimilation theory, logic, semantic networks, and decision analysis have formed the underpinnings of these systems. Theoretical progress has fostered practical development in system architecture, graphical forms of knowledge representation, analysis and induction techniques, and group use of knowledge acquisition tools.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"39 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closing the Gap Between Modeling to Make Sense and Modeling to Implement Systems","authors":"Marc Linster","doi":"10.1002/j.1098-111x.1993.tb00004.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00004.x","url":null,"abstract":"We view knowledge acquisition for knowledge‐based systems as a constructive model‐building process. From this view we derive several requirements for knowledge modeling environments. We concentrate on those requirements that arise if one wants to support both <jats:italic>modeling to make sense</jats:italic> and <jats:italic>modeling to implement systems</jats:italic> with a single language. For example, among other things, such languages should support multifaceted, bottom‐up construing of observed behavior and they should have operational semantics. We introduce the operational modeling language OMOS, an experimental study that—in a KADS‐like fashion—allows multifaceted model building from a method and a domain point of view, but, unlike KADS conceptual models, results in directly operational systems. Finally, we compare OMOS to other recent developments to highlight differences in the approaches.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Simple Causal Structures1","authors":"Dan Geiger, Azaria Paz, Judea Pearl","doi":"10.1002/j.1098-111x.1993.tb00005.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00005.x","url":null,"abstract":"Humans use knowledge of causation to derive dependencies among events of interest. The converse task, that of inferring causal relationships from patterns of dependencies, is far less understood. This article established conditions under which the directionality of some dependencies is uniquely dictated by probabilistic information—an essential prerequisite for attributing a causal interpretation to these dependencies. An efficient algorithm is developed that, given data generated by an undisclosed simple causal schema, recovers the structure of that schema, as well as the directionality of all links that are uniquely orientable. A simple schema is represented by a directed acyclic graph (dag) where every pair of nodes with a common direct child have no common ancestor nor is one an ancestor of the other. Trees, singly connected dags, and directed bi‐partite graphs are examples of simple dags. Conditions ensuring the correctness of this recovery algorithm are provided.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operator Assistant Systems: An Experimental Approach Using a Telerobotics Application*","authors":"Guy A. Boy, Nathalie Mathé","doi":"10.1002/j.1098-111x.1993.tb00006.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00006.x","url":null,"abstract":"This article presents a knowledge‐based system methodology for developing Operator Assistant (OA) systems in dynamic and interactive environments. Recent results in human error studies show that automation appears to be the most critical factor in human‐machine interaction problems. This is a problem both of training and design, which is the subject of this article. Design includes both design of the system to be controlled and design of procedures for operating this system. A specific knowledge representation is proposed for representing the corresponding system and operational knowledge. This representation is based on the situation recognition and analytical reasoning paradigm (SRAR). It tries to make explicit common factors involved in both human and machine intelligence, including perception and reasoning. An OA system based on this representation has been developed for space telerobotics. Simulations have been carried out with astronauts and the resulting protocols have been analyzed. Results show the relevance of the approach and have been used for improving the knowledge representation and the OA architecture. © 1993 John Wiley &amp; Sons, Inc.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Formalization of Knowledge‐Level Models for Knowledge Acquisition","authors":"Hans Akkermans, Frank van Harmelen, Guus Schreiber, Bob Wielinga","doi":"10.1002/j.1098-111x.1993.tb00003.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00003.x","url":null,"abstract":"This article defines second‐generation knowledge acquisition as a modeling activity that is knowledge‐level oriented. Knowledge‐level models of expert reasoning represent an important output of the knowledge‐acquisition process, since they describe, in a conceptual and implementation‐independent fashion, the different roles and types of knowledge required for a problem‐solving task. We argue that a formalization of such models enhances knowledge acquisition, and in particular the conceptualization phase, by rendering currently informal concepts and intuitions more precise, thus also contributing to a more solid basis for KBS design, validation, and maintenance. A framework is constructed for the formal specification of knowledge‐level models. The proposed formalism, called <jats:sc>ml<jats:sup>2</jats:sup></jats:sc>, has been inspired by the <jats:sc>kads</jats:sc> methodology for KBS development, and aims at expressing different roles and types of knowledge components through employing an order‐sorted logic, a modular structuring of theories, and a meta‐level organization of knowledge, comprising “enlarged” reflection rules and a “meaningful” naming relation. An application of the formal specification method to heuristic classification is given. Issues relating to the epistemological adequacy and the computational tractability of formalized knowledge‐level models are discussed.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}