International Journal of Intelligent Systems最新文献

{"title":"A Maturity Model for Practical Explainability in Artificial Intelligence-Based Applications: Integrating Analysis and Evaluation (MM4XAI-AE) Models","authors":"Julián Muñoz-Ordóñez,&nbsp;Carlos Cobos,&nbsp;Juan C. Vidal-Rojas,&nbsp;Francisco Herrera","doi":"10.1155/int/4934696","DOIUrl":"https://doi.org/10.1155/int/4934696","url":null,"abstract":"<div>\u0000 <p>The increasing adoption of artificial intelligence (AI) in critical domains such as healthcare, law, and defense demands robust mechanisms to ensure transparency and explainability in decision-making processes. While machine learning and deep learning algorithms have advanced significantly, their growing complexity presents persistent interpretability challenges. Existing maturity frameworks, such as Capability Maturity Model Integration, fall short in addressing the distinct requirements of explainability in AI systems, particularly where ethical compliance and public trust are paramount. To address this gap, we propose the Maturity Model for eXplainable Artificial Intelligence: Analysis and Evaluation (MM4XAI-AE), a domain-agnostic maturity model tailored to assess and guide the practical deployment of explainability in AI-based applications. The model integrates two complementary components: an analysis model and an evaluation model, structured across four maturity levels—operational, justified, formalized, and managed. It evaluates explainability across three critical dimensions: technical foundations, structured design, and human-centered explainability. MM4XAI-AE is grounded in the PAG-XAI framework, emphasizing the interrelated dimensions of practicality, auditability, and governance, thereby aligning with current reflections on responsible and trustworthy AI. The MM4XAI-AE model is empirically validated through a structured evaluation of thirteen published AI applications from diverse sectors, analyzing their design and deployment practices. The results show a wide distribution across maturity levels, underscoring the model’s capacity to identify strengths, gaps, and actionable pathways for improving explainability. This work offers a structured and scalable framework to standardize explainability practices and supports researchers, developers, and policymakers in fostering more transparent, ethical, and trustworthy AI systems.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4934696","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367476","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":"A Privacy-Preserving Federated Learning Framework for Ambient Temperature Estimation With Crowdsensing and Exponential Mechanism","authors":"Saeid Zareie,&nbsp;Rasool Esmaeilyfard,&nbsp;Pirooz Shamsinejadbabaki","doi":"10.1155/int/5531568","DOIUrl":"https://doi.org/10.1155/int/5531568","url":null,"abstract":"<div>\u0000 <p>Ambient temperature estimation plays a vital role in various domains, including environmental monitoring, smart cities, and energy-efficient systems. Traditional sensor-based methods suffer from high deployment costs and limited scalability, while centralized machine learning approaches raise significant privacy concerns. Recent crowdsensing-based systems leverage smartphone sensor data but face two major challenges: user privacy protection and unreliable participant contributions. To address these issues, this study proposes a privacy-preserving federated learning framework that integrates differential privacy with the exponential mechanism to ensure user anonymity during decentralized training. Furthermore, a novel utility-based filtering mechanism is employed to detect and exclude low-quality or adversarial data, enhancing model reliability. Advanced deep learning models, including long short–term memory (LSTM) and ensemble learning, are integrated to improve prediction accuracy in temporal and noisy environments. The dataset consists of mobile sensor data, including battery temperature, CPU usage, and environmental temperature measurements, collected from participants in real-world settings. The framework achieved high accuracy, with the LSTM model outperforming others (federated MAE: 1.292, MAPE: 0.0511) and performing comparably to centralized models (MAE: 1.179, MAPE: 0.0462) while ensuring privacy. The proposed framework showed comparable performance to centralized models while ensuring strong privacy guarantees. The integration of privacy-preserving mechanisms and robust data filtering enables a scalable and reliable solution suitable for practical deployment in large-scale ambient temperature estimation tasks.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5531568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367218","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":"Acquiring Tactical and Strategic Knowledge with a Generalized Method for Chunking of Game Pieces","authors":"Steven Walczak, Douglas Dankei","doi":"10.1002/j.1098-111x.1993.tb00001.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00001.x","url":null,"abstract":"The physical configuration of playing pieces on a game board contains a plethora of information which can be used by the game player. Current computer game programs deal well with some positional and tactical information that is built into the program, but are incapable of acquiring and using strategic information. We present a technique for capturing strategic and tactical chunks or patterns of pieces in game domains. The chunking technique models the cognitive method employed by expert level human game players and acquires knowledge that is mostly domain independent. Induction is performed on the collection of chunks captured for a particular adversary to identify the playing style of that adversary.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"229 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304464","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":"Accurate Classification of Pathological Whole-Slide Images for Out-of-Distribution Generalization","authors":"Kai Sun,&nbsp;Kai Huang,&nbsp;Jiaqi Huang,&nbsp;Maoxu Zhou,&nbsp;Gang Yu","doi":"10.1155/int/9988577","DOIUrl":"https://doi.org/10.1155/int/9988577","url":null,"abstract":"<div>\u0000 <p>WSI-based classification often suffers from out-of-distribution (OOD) generalization due to the distribution mismatch between training on mixed patches from multiple WSIs and testing on individual WSIs with varying tissue compositions. This prior shift impairs model generalization and degrades performance. To address this issue, we propose two distribution alignment strategies: intra-WSI rearrange and inter-WSI rearrange, which, respectively, regulate patch distribution within individual WSIs and across different WSIs. These strategies are embedded into a transformer-based multi-instance learning (MIL) framework enabling more accurate and robust classification. Our method achieves excellent AUC scores of 0.959 and 0.963 on the CAMELYON16 and TCGA-NSCLC datasets, respectively. Moreover, it reaches an average AUC of 0.974 in 5-fold cross-validation on a private CRC dataset, matching the performance of patch-based approaches. Ablation studies further validate the effectiveness of our proposed strategies in mitigating the OOD challenge in WSI classification. Overall, these strategies enhance the robustness and accuracy of WSI-based models in handling OOD challenges.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9988577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299737","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 Overview of Robot Embodied Intelligence Based on Multimodal Models: Tasks, Models, and System Schemes","authors":"Yao Cong,&nbsp;Hongwei Mo","doi":"10.1155/int/5124400","DOIUrl":"https://doi.org/10.1155/int/5124400","url":null,"abstract":"<div>\u0000 <p>The exploration of embodied intelligence has garnered widespread consensus in the field of artificial intelligence (AI), aiming to achieve artificial general intelligence (AGI). Classical AI models, which rely on labeled data for learning, struggle to adapt to dynamic, unstructured environments due to their offline learning paradigms. Conversely, embodied intelligence emphasizes interactive learning, acquiring richer information through environmental interactions for training, thereby enabling autonomous learning and action. Early embodied tasks primarily centered on navigation. With the surge in popularity of large language models (LLMs), the focus shifted to integrating LLMs/multimodal large models (MLM) with robots, empowering them to tackle more intricate tasks through reasoning and planning, leveraging the prior knowledge imparted by LLM/MLM. This work reviews initial embodied tasks and corresponding research, categorizes various current embodied intelligence schemes deployed in robotics within the context of LLM/MLM, summarizes the perception–planning–action (PPA) paradigm, evaluates the performance of MLM across different schemes, and offers insights for future development directions in this domain.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5124400","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281404","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":"Artificial Intelligence in Melanoma Detection: A Review of Current Technologies and Future Directions","authors":"Fakhre Alam,&nbsp;Asad Ullah,&nbsp;Dilawar Shah,&nbsp;Shujaat Ali,&nbsp;Muhammad Tahir","doi":"10.1155/int/3164952","DOIUrl":"https://doi.org/10.1155/int/3164952","url":null,"abstract":"<div>\u0000 <p>Early and accurate identification of malignant melanoma continues to be a major challenge for clinicians in the field. Traditional diagnostic approaches, including physical examination, histology, imaging, and nodal assessments, are frequently costly, require significant expertise, and can display large variations among clinicians. These factors may result in missed or misdiagnosis, which often significantly affects a patient’s prognosis. We examine in detail how the application of AI methods such as machine learning and deep learning can be used to advance early detection and identification of melanoma. We review various AI algorithms, including standard classifiers, ensemble techniques, and complex deep learning models. Hybrid models that combine convolutional neural networks (CNNs) and support vector machines (SVMs) are emphasized in this review, as they show enhanced performance and improved resistance to variations in the diagnostician’s input. Better utility of transfer learning and data augmentation approaches is discussed to overcome the challenges posed by small and unbalanced medical datasets. The authors consider the combination of various types of medical information for more effective cancer diagnosis. However, significant obstacles, including model explainability, privacy safeguarding, and clinical evaluation, still need to be addressed. Extensive efforts are needed to overcome these barriers if AI systems are to be effectively adopted within healthcare environments. We suggest that AI offers the opportunity to revolutionize melanoma care by enabling rapid decision support and individualized treatment plans. Realizing this opportunity will depend on effective partnerships between researchers, clinicians, and industry to bring together advances in technology and their effective implementation in the healthcare system.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3164952","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273354","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":"Abnormal Traffic Detection Method Based on DCNN-GRU Architecture in SDN","authors":"Xueyuan Duan,&nbsp;Kun Wang,&nbsp;Yu Fu,&nbsp;Taotao Liu,&nbsp;Yihan Yu,&nbsp;Jianqiao Xu,&nbsp;Lu Wang","doi":"10.1155/int/2846238","DOIUrl":"https://doi.org/10.1155/int/2846238","url":null,"abstract":"<div>\u0000 <p>In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of service quality of SDN network, this paper proposes a large-scale abnormal traffic detection method of SDN network based on Distributed Convolutional Neural Networks and Gate Recurrent Unit (DCNN-GRU) architecture. This method utilizes lightweight detection agents based on CNN deployed on each controller to extract traffic features preliminarily. Then it inputs the feature data into the GRU-based deep detection model hosted in the cloud for collaborative training and completes the final abnormal detection task. Since the feature extraction tasks are distributed across multiple controllers, the cloud server only needs to relearn and classify the extracted feature data, which is less costly than directly extracting feature information from the original traffic data and occupies less bandwidth resources than transmitting complete data packets. The experiment shows that the method achieves an abnormal detection accuracy of 0.9939, a recall rate of 0.9831, and a false alarm rate of only 0.0244, obtaining a higher precision and lower false alarm rate than traditional detection methods.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2846238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256102","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":"MCOA: A Multistrategy Collaborative Enhanced Crayfish Optimization Algorithm for Engineering Design and UAV Path Planning","authors":"Yaning Xiao,&nbsp;Hao Cui,&nbsp;Ruba Abu Khurma,&nbsp;Abdelazim G. Hussien,&nbsp;Pedro A. Castillo","doi":"10.1155/int/5054424","DOIUrl":"https://doi.org/10.1155/int/5054424","url":null,"abstract":"<div>\u0000 <p>The crayfish optimization algorithm (COA) is a recent bionic optimization technique that mimics the summer sheltering, foraging, and competitive behaviors of crayfish. Although COA has outperformed some classical metaheuristic (MH) algorithms in preliminary studies, it still manifests the shortcomings of falling into local optimal stagnation, slow convergence speed, and exploration–exploitation imbalance in addressing intractable optimization problems. To alleviate these limitations, this study introduces a novel modified crayfish optimization algorithm with multiple search strategies, abbreviated as MCOA. First, specular reflection learning is implemented in the initial iterations to enrich population diversity and broaden the search scope. Then, the location update equation in the exploration procedure of COA is supplanted by the expanded exploration strategy adopted from Aquila optimizer (AO), endowing the proposed algorithm with a more efficient exploration power. Subsequently, the motion characteristics inherent to Lévy flight are embedded into local exploitation to aid the search agent in converging more efficiently toward the global optimum. Finally, a vertical crossover operator is meticulously designed to prevent trapping in local optima and to balance exploration and exploitation more robustly. The proposed MCOA is compared against twelve advanced optimization algorithms and nine similar improved variants on the IEEE CEC2005, CEC2019, and CEC2022 test sets. The experimental results demonstrate the reliable optimization capability of MCOA, which separately achieves the minimum Friedman average ranking values of 1.1304, 1.7000, and 1.3333 on the three test benchmarks. In most test cases, MCOA can outperform other comparison methods regarding solution accuracy, convergence speed, and stability. The practicality of MCOA has been further corroborated through its application to seven engineering design issues and unmanned aerial vehicle (UAV) path planning tasks in complex three-dimensional environments. Our findings underscore the competitive edge and potential of MCOA for real-world engineering applications. The source code for MCOA can be accessed at https://doi.org/10.24433/CO.5400731.v1.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5054424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244711","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":"Intelligent Management Frameworks for Global Cooperation","authors":"Ana M. Gonzalez de Miguel,&nbsp;Antonio Sarasa-Cabezuelo","doi":"10.1155/int/1706422","DOIUrl":"https://doi.org/10.1155/int/1706422","url":null,"abstract":"<div>\u0000 <p>This paper presents the definition, use, and evaluation of intelligent management frameworks for global cooperation. The research work brings new concepts and ideas to design new management models and artificial intelligence solutions in sustainable environments. An intelligent management framework is a flexible and efficient vertical association of models, architectures, and processes. It is a mixed (architectural and methodological) association of services and procedures across IT departments of global organizations. The paper presents a general top-down approach to design these frameworks for global, cooperative models of intelligence. The approach includes five levels of abstraction and three refinement techniques. These elements are used to design an evaluation case study with global services and process-oriented cooperation for current sustainable targets in education. In our future work, we will implement these management solutions for government organizations currently involved with digital transformations.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1706422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244575","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":"Space Ground Collaborative SFC Flow Scheduling Strategy in Satellite–Terrestrial Integrated Network–Enabled Internet of Vehicles Rescuing Based on Computation–Space–Time Graph","authors":"Yingjie Deng,&nbsp;Yu Liu,&nbsp;Yumei Wang,&nbsp;Konglin Zhu,&nbsp;Peng Wu,&nbsp;Lu Cao,&nbsp;Wen Sun,&nbsp;Jingwen Xu","doi":"10.1155/int/9914571","DOIUrl":"https://doi.org/10.1155/int/9914571","url":null,"abstract":"<div>\u0000 <p>The extensive coverage of satellite constellations has rendered the satellite–terrestrial integrated network (STIN) a pivotal solution for communication and computation services in internet of vehicles (IoVs) rescuing in remote or disaster areas with limited terrestrial networks. To optimise network resource utilisation and service quality, the integration of the service function chain (SFC) into STIN-enabled IoV rescuing systems has become essential. However, traditional SFC-based STIN systems encounter challenges in flow scheduling flexibility, stemming from the sequential execution of subtasks on satellites equipped with virtual network functions (VNFs). This leads to a trade-off between data volume reduction and the additional communication and computation energy costs incurred in the orbit. To address this issue, this paper introduces a space ground collaborative SFC (SGC-SFC) flow scheduling strategy. This strategy enables the execution of subtasks on either VNF-equipped satellites or the ground vehicle formation, contingent on network conditions. Firstly, we carry out a computation–space–time graph (CSTG) model specifically for the STIN-enabled IoV rescuing system with SFC. This model integrates the computational layer into the space–time graph (STG), accurately capturing the data volume reduction characteristics and sequential execution constraints of SFC in the STIN-enabled IoV rescuing system. Secondly, a SGC-SFC flow scheduling algorithm is designed to identify a set of feasible paths with minimal energy cost and maximum processable data volume. Simulation results validate the effectiveness and robustness of our proposed SGC-SFC under diverse conditions.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9914571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220277","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}