International Journal of Intelligent Systems最新文献

{"title":"An Intelligent Surveillance Platform With Deep Tampered Video Detection in Secure Edge-Cloud Services","authors":"Yuwen Shao,&nbsp;Qiuling Wang,&nbsp;Junsong Zhang,&nbsp;Haiying Tian,&nbsp;Yong Zhang","doi":"10.1155/int/3744881","DOIUrl":"https://doi.org/10.1155/int/3744881","url":null,"abstract":"<div>\u0000 <p>The increasing complexity of video tampering techniques poses a significant threat to the integrity and security of Internet of Multimedia Things (IoMT) ecosystems, particularly in resource-constrained edge-cloud infrastructures. This paper introduces Multiscale Gated Multihead Attention Depthwise Separable CNN (MGMA-DSCNN), an advanced deep learning framework specifically optimized for real-time tampered video detection in IoMT environments. By integrating lightweight convolutional neural networks (CNNs) with multihead attention mechanisms, MGMA-DSCNN significantly enhances feature extraction while maintaining computational efficiency. Unlike conventional methods, this approach employs a multiscale attention mechanism to refine feature representations, effectively identifying deepfake manipulations, frame insertions, splicing, and adversarial forgeries across diverse multimedia streams. Extensive experiments on multiple forensic video datasets—including the HTVD dataset—demonstrate that MGMA-DSCNN outperforms state-of-the-art architectures such as VGGNet-16, ResNet, and DenseNet, achieving an unprecedented detection accuracy of 98.1%. Furthermore, by leveraging edge-cloud synergy, our framework optimally distributes computational loads, effectively reducing latency and energy consumption, making it highly suitable for real-time security surveillance and forensic investigations. These advancements position MGMA-DSCNN as a scalable, high-performance solution for next-generation intelligent video authentication, offering robust, low-latency detection capabilities in dynamic and resource-constrained IoMT environments.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3744881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144717050","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 Innovative Coverage Path Planning Approach for UAVs to Boost Precision Agriculture and Rescue Operations","authors":"Nur Mohammad Fahad,&nbsp;Selvarajah Thuseethan,&nbsp;Sheikh Izzal Azid,&nbsp;Sami Azam","doi":"10.1155/int/4700518","DOIUrl":"https://doi.org/10.1155/int/4700518","url":null,"abstract":"<div>\u0000 <p>Unmanned aerial vehicles (UAVs) have been employed for a variety of inspection and monitoring tasks, including agricultural applications and search and rescue (SAR) in remote areas. However, traditional monitoring methods tend to focus on optimizing one aspect. This study aims to propose a complete framework by integrating advanced methods to provide a robust and accurate path coverage solution. The combination of edge detection and area decomposition with a pathfinding algorithm can improve the overall performance. An effective edge detection model is developed that simultaneously detects the boundary and segments the area of interest (AOI) from the aerial land images and provides precise area mapping of the area. An intuitive grid decomposition with grid-to-graph mapping improves the flexibility of the area decomposition and ensures maximal coverage and safe operation routes for the UAVs. Finally, a robust modified simulated annealing (MSA) algorithm is introduced to determine the shortest path coverage route. The performance of the proposed methodology is tested on aerial imagery. Area decomposition ensures that there are no gaps in the AOI during the coverage planning. The MSA algorithm obtains the minimum length cost, charge consumption cost, and minimum number of turns to cover the area. It is shown that the integration of these techniques enhances the performance of the coverage path planning (CPP). A comparison of the proposed approach with benchmark algorithms further demonstrates its effectiveness. This study contributes to creating a complete CPP application for UAVs, which may assist with precision agriculture as well as safe and secure rescue operations.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4700518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688244","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":"Variable Dimensional Multiobjective Lifetime Constrained Quantum PSO With Reinforcement Learning for High-Dimensional Patient Data Clustering","authors":"Chen Guo,&nbsp;Heng Tang,&nbsp;Huifen Zhong,&nbsp;Hua Xiao,&nbsp;Ben Niu","doi":"10.1155/int/5521043","DOIUrl":"https://doi.org/10.1155/int/5521043","url":null,"abstract":"<div>\u0000 <p>Ming potential patterns from patient data are usually treated as a high-dimensional data clustering problem. Evolutionary multiobjective clustering algorithms with feature selection (FS) are widely used to handle this problem. Among the existing algorithms, FS can be performed either before or during the clustering process. However, research on performing FS at both stages (hybrid FS), which can yield robust and credible clustering results, is still in its infancy. This paper introduces an improved high-dimensional patient data clustering algorithm with hybrid FS called variable dimensional multiobjective lifetime constrained quantum PSO with reinforcement learning (VLQPSOR). VLQPSOR consists of two main independent stages. In the first stage, a dimensionality reduction ensemble strategy is developed before clustering to reduce the patient dataset’s dimensionality, resulting in subdatasets of varying dimensions. In the second stage, an improved multiobjective QPSO clustering algorithm is proposed to simultaneously conduct dimensionality reduction and clustering. To accomplish this, several strategies are employed. Firstly, the variable dimensional lifetime constrained particle learning strategy, the continuous-to-binary encoding transformation strategy, and multiple external archives elite learning strategy are introduced to further reduce the dimensionality of the subdatasets and mitigate the risk of QPSO getting trapped in local optima. Secondly, an improved reinforcement learning–based clustering method selection strategy is proposed to adaptively select the optimal classical clustering algorithm. Experimental results demonstrate that VLQPSOR outperforms five representative comparative algorithms across four validity indexes and clustering partitions for most patient datasets. Ablation experiments confirm the effectiveness of the proposed strategies in enhancing the performance of QPSO.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5521043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144673118","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":"Ensuring Supply Chain Transparency by Deploying Blockchain-Enabled Technology: An Overview With Demonstration","authors":"Ahm Shamsuzzoha,&nbsp;Khuram Shahzad,&nbsp;Essi Nousiainen,&nbsp;Mikko Ranta,&nbsp;Petri Helo,&nbsp;Kannan Govindan","doi":"10.1155/int/7304193","DOIUrl":"https://doi.org/10.1155/int/7304193","url":null,"abstract":"<div>\u0000 <p>It is nowadays quite challenging to manage and control the supply chain concerning transparency, traceability, and zero-trust security. Digital technology such as blockchain has shown promising features to ease the global supply chain for tracking, tracing, and authenticity. This study critically examines the potential of blockchain technology and smart contracts to manage global supply chain sustainability. It also analyzes the inherent opportunities, benefits, and common barriers to deploying blockchain in the supply chain. Moreover, an overview of blockchain technology and its application in the various industries’ supply chain management is illustrated in this study. Furthermore, an application demo related to blockchain in the supply chain is provided within the scope of this study with the view to demonstrating how various transactions in the supply chain are executed with higher authenticity. The study is concluded with several future research propositions and directions that may provide insight into overcoming current challenges and the adoption of blockchain for the supply chain.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7304193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615200","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}