International Journal of Intelligent Systems最新文献

{"title":"Interval-Valued Probabilistic Dual Hesitant Fuzzy Muirhead Mean Aggregation Operators and Their Applications in Regenerative Energy Source Selection","authors":"Muhammad Qiyas,&nbsp;Muhammad Naeem,&nbsp;Zahid Khan,&nbsp;Samuel Okyer","doi":"10.1155/int/8892299","DOIUrl":"https://doi.org/10.1155/int/8892299","url":null,"abstract":"<div>\u0000 <p>As an effective addition to the hesitant fuzzy set (HFS), a probabilistic dual hesitant fuzzy set (PDHFS) has been designed in this paper. PDHFS would be an improved version of the dual hesitant fuzzy set (DHFS) where both membership and nonmembership hesitant quality is considered for all its probability of existence. Additional information on the degree of acceptance or rejection contains such allocated probabilities. More conveniently, we create a comprehensive type of PDHFS called interval-valued PDHFS (IVPDHFS) to interpret the probability data that exist in the hesitancy. This study describes several basic operating laws by stressing the advantages and enriching the utility of IVPDHFS in MAGDM. To aggregate IVPDHF information in MAGDM problems and extend its applications, we present the Muirhead mean (MM) operator of IVPDHFSs and study some attractive properties of the suggested operator. Besides that, in order to compute attribute weights, a new organizational framework is designed by using partial knowledge of the decision makers (DMs). Subsequently, a standardized technique with the suggested operator for MAGDM is introduced, and the realistic usage of the operator is illustrated by the use of a problem of regenerative energy source selection. We discuss the influence of the parameter vector on the ranking results. Finally, to address the benefits and limitations of the recommended MAGDM approach, the findings of the proposal are contrasted with other approaches.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8892299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519630","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 Hybrid TLBO–XGBoost Model With Novel Labeling for Bitcoin Price Prediction","authors":"Elnaz Radmand,&nbsp;Jamshid Pirgazi,&nbsp;Ali Ghanbari Sorkhi","doi":"10.1155/int/6674437","DOIUrl":"https://doi.org/10.1155/int/6674437","url":null,"abstract":"<div>\u0000 <p>In the digital currency market, including Bitcoin, price prediction using artificial intelligence (AI) and machine learning (ML) is critical but challenging. Conventional methods such as technical analysis (based on historical market data) and fundamental analysis (based on economic variables) suffer from data noise, processing delays, and insufficient data. To make predictions more accurate, faster, and able to handle more data, the suggested method combines several steps: extracting important information, labeling it, choosing the best features, merging different models, and fine-tuning the model settings. Based on the price data, this approach initially generates 5 labels with a new labeling method based on the percentage of average price changes in several days and generates signals (hold, buy, sell, strong sell, and strong buy). Thereafter, it extracts 768 features from technical studies using the TA-Lib library and from an authoritative site. The TLBOA algorithm, which does not get stuck in the local optimum with two updates, was used to select and reduce features to 15 to avoid overfitting. A variety of ML models, including support vector machine and Naive Bayes, use these selected features for training. By using the evolutionary DE algorithm to optimize the XGBoost meta-parameters, we increased the accuracy by 1%–4%. The proposed strategy has performed better than other models, such as XGBoost with 85.66% and gradient boosting with 84.15%, and has achieved an accuracy of 91%–92%.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6674437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519631","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":"Framework for Quantum-Based Deepfake Video Detection (Without Audio)","authors":"Atul Pandey,&nbsp;Bhawana Rudra,&nbsp;Rajesh Kumar Krishnan","doi":"10.1155/int/3990069","DOIUrl":"https://doi.org/10.1155/int/3990069","url":null,"abstract":"<div>\u0000 <p>Artificial intelligence (AI) has made human tasks easier compared to earlier days. It has revolutionized various domains, from paper drafting to video editing. However, some individuals exploit AI to create deceptive content, such as fake videos, audios, and images, to mislead others. To address this, researchers and large corporations have proposed solutions for detecting fake content using classical deep learning models. However, these models often suffer from a large number of trainable parameters, which leads to large model sizes and, consequently, computational intensive. To overcome these limitations, we propose various hybrid classical–quantum models that use a classical pre-trained model as a front-end feature extractor, followed by a quantum-based LSTM network, that is, QLSTM. These pre-trained models are based on the ResNet architecture, such as ResNet34, 50, and 101. We have compared the performance of the proposed models with their classical counterparts. These proposed models combine the strengths of classical and quantum systems for the detection of deepfake video (without audio). Our results indicate that the proposed models significantly reduce the number of trainable parameters, as well as quantum long short-term memory (QLSTM) parameters, which leads to a smaller model size than the classical models. Despite the reduced parameter, the performance of the proposed models is either superior to or comparable with that of their classical equivalent. The proposed hybrid quantum models, that is, ResNet34-QLSTM, ResNet50-QLSTM, and ResNet101-QLSTM, achieve a reduction of approximately 1.50%, 4.59%, and 5.24% in total trainable parameters compared to their equivalent classical models, respectively. Additionally, QLSTM linked with the proposed models reduces its trainable parameters by 99.02%, 99.16%, and 99.55%, respectively, compared to equivalent classical LSTM. This significant reduction highlights the efficiency of the quantum-based network in terms of resource usage. The trained model sizes of the proposed models are 81.35, 88.06, and 162.79, and their equivalent classical models are 82.59, 92.28, and 171.76 in MB, respectively.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3990069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492724","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 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}