International Journal of Intelligent Systems最新文献

{"title":"A Multiobjective Optimization Method for Collecting and Releasing Processes of Winch System Considering Wave Disturbance and Control Laws","authors":"Xiaochuan Duan,&nbsp;Shaoping Wang,&nbsp;Jian Shi,&nbsp;Di Liu,&nbsp;Yaoxing Shang","doi":"10.1155/int/2004983","DOIUrl":"https://doi.org/10.1155/int/2004983","url":null,"abstract":"<p>The winch’s performance under complex sea conditions is significantly influenced by its collecting and releasing processes. To enhance its performance and reliability, an optimization approach considering wave disturbances and control laws is proposed to balance time efficiency and tension stability. Within a multiobjective optimization framework, the method designs constant tension control and robust adaptive speed control and introduces sinusoidal acceleration trajectories to minimize tension surges and reduce system impacts caused by rapid starts/stops. The constant tension controller reduces wave disturbances, while the speed controller manages the working process. These controllers are designed with unknown reference signals determined during the optimization process. Additionally, the objective functions in the optimization phase aim to reduce working time and tension fluctuations, with constraints ensuring system safety and mission requirements. Furthermore, an experimental platform constructed on a ship validates the accuracy of the winch model. The optimized process not only shortens operational time, as collecting same length only consumption 127.44 s compared 143.14 s without optimization, but also reduces tension and acceleration. More importantly, transitions between states become more gradual. This indicates that the proposed method is both time-efficient and effective in dampening tension fluctuations and mitigating the effects of abrupt changes during the working process.</p>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2004983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814765","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":"Scalable Comprehensive Automatic Inspection, Cleaning, and Evaluation Mechanism for Large-Diameter Pipes","authors":"Imran Shafi,&nbsp;Imad Khan,&nbsp;Jose Brenosa,&nbsp;Miguel Angel Lopez Flores,&nbsp;Julio Cesar Martinez Espinosa,&nbsp;Jin-Ghoo Choi,&nbsp;Imran Ashraf","doi":"10.1155/int/2441962","DOIUrl":"https://doi.org/10.1155/int/2441962","url":null,"abstract":"<p>Cleaning and inspection of pipelines and gun barrels are crucial for ensuring safety and integrity to extend their lifespan. Existing automatic inspection approaches lack high robustness, as well as portability, and have movement restrictions and complexity. This study presents the design and development of a scalable, comprehensive automated inspection, cleaning, and evaluation mechanism (CAICEM) for large-sized pipelines and barrels with diameters in the range of 105 mm–210 mm. The proposed system is divided into electrical and mechanical assemblies that are independently designed, tested, fabricated, integrated, and controlled with industrial grid controllers and processors. These actuators are suitably programmed to provide the desired actions through toggle switches on a simple housing subassembly. The stress analysis and material specifications are obtained using ANSYS to ensure robustness and practicability. Later, on-ground testing and optimization are performed before industrial prototyping. The inspection system of the proposed mechanism includes barrel-mounted and brush-mounted cameras with sensors utilized to keep track of the pipeline deposits and monitor user activity. The experimental results demonstrate that the proposed mechanism is cost-effective and achieves the desired objectives with minimum human efforts in the least possible time for both smooth and rifled large-diameter pipes and barrels.</p>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2441962","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832798","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 Improved Best-Worst Method Integrated With Combined Compromise Solution for Evaluating Large Language Models","authors":"O. S. Albahri,&nbsp;M. A. Alsalem,&nbsp;A. S. Albahri,&nbsp;Moamin A. Mahmoud,&nbsp;Laith Alzubaidi,&nbsp;A. H. Alamoodi,&nbsp;Iman Mohamad Sharaf","doi":"10.1155/int/2376097","DOIUrl":"https://doi.org/10.1155/int/2376097","url":null,"abstract":"<div>\u0000 <p>The emergence of large language models (LLMs) has substantially changed the artificial intelligence field, enabling its wide use over different domains. As various LLM alternatives have been developed, the current study proposes a novel decision-support framework for evaluating and benchmarking LLMs based on multicriteria decision-making (MCDM) techniques. In the proposed framework, an improved version of the best-worst method (BWM) is proposed to effectively reduce the computational complexity of assigning a critical weight for the evaluation criteria of LLMs. Then, the improved BWM is integrated with the combined compromise solution (CoCoSo) method for ranking LLM alternatives. Findings show that the improved BWM successfully computes the criteria weights with low computational complexity compared to the original BWM. According to the enhanced BWM, the ‘factual errors’ criterion received the highest significant weight (0.2681), while the ‘logical inconsistencies’ criteria obtained the lowest (0.0827). The rest of the criteria were distributed in between that range. Subsequently, CoCoSo ranked the involved LLM alternatives in two different runs based on the extracted weights. Sensitivity analysis was employed to evaluate the effect of the assessment criteria on LLMs’ evaluation.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2376097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811328","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":"Improving End-of-Life Care for COPD Patients: Design and Development of an Intelligent Clinical Decision Support System to Predict One-Year Mortality After Acute Exacerbations","authors":"Manuel Casal-Guisande,&nbsp;Cristina Represas-Represas,&nbsp;Rafael Golpe,&nbsp;Alberto Comesaña-Campos,&nbsp;Alberto Fernández-García,&nbsp;María Torres-Durán,&nbsp;Alberto Fernández-Villar","doi":"10.1155/int/5556476","DOIUrl":"https://doi.org/10.1155/int/5556476","url":null,"abstract":"<div>\u0000 <p>Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease, which presents a significant challenge in identifying patients at high risk of short- and medium-term mortality. Such complexity poses challenges to clinical decision-making and the effective planning of end-of-life care in these patients. This study proposes the development of a novel intelligent clinical decision support system, designed to predict 1-year mortality in COPD patients following an acute exacerbation. The system is constructed upon a database of over 500 patients, comprising demographic, clinical, and social variables. First, a feature selection process is conducted to identify the variables that possess the greatest predictive power. Based on these, the data for each patient are encapsulated in a pseudosymbol construct that represents and consolidates them. The construction of the pseudosymbol comprises two distinct steps: (1) transforming the variables into a sound composition and (2) generating the corresponding spectrogram, which constitutes a visual representation (i.e., an image). The system employs a convolutional neural network, SqueezeNet, as the inference engine to calculate the 1-year mortality risk based on the images. Ten percent of the data was reserved for testing the system, achieving an area under the ROC curve (AUC) close to 0.85, indicating a high predictive power. Despite this promising initial result, further clinical validations in real-world settings will be necessary to confirm the system’s applicability and usefulness.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5556476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751573","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 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":"Continual Learning Inspired by Brain Functionality: A Comprehensive Survey","authors":"Muhammad Azeem Aslam,&nbsp;Muhammad Hamza,&nbsp;Zhu Shuangtong,&nbsp;Hu Hongfei,&nbsp;Xu Wei,&nbsp;Muhammad Irfan,&nbsp;Zheng Jiangbin,&nbsp;Saba Aslam","doi":"10.1155/int/3145236","DOIUrl":"https://doi.org/10.1155/int/3145236","url":null,"abstract":"<div>\u0000 <p>Neural network–based models have shown tremendous achievements in various fields. However, standard AI-based systems suffer from catastrophic forgetting when undertaking sequential learning of multiple tasks in dynamic environments. Continual learning has emerged as a promising approach to address catastrophic forgetting. It enables AI systems to learn, transfer, augment, fine-tune, and reuse knowledge for future tasks. The techniques used to achieve continual learning are inspired by the learning processes of the human brain. In this study, we present a comprehensive review of research and recent developments in continual learning, highlighting key contributions and challenges. We discuss essential functions of the biological brain that are pivotal for achieving continual learning and map these functions to the recent machine-learning methods to aid understanding. Additionally, we offer a critical review of five recent types of continual learning methods inspired by the biological brain. We also provide empirical results, analysis, challenges, and future directions. We hope that this study will benefit both general readers and the research community by offering a complete picture of the latest developments in this field.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3145236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705445","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":"Automatic Identification and Counting of South African Animal Species in Camera Traps Using Deep Learning","authors":"Siyabonga Mamapule,&nbsp;Michael Esiefarienrhe,&nbsp;Ibidun Christiana Obagbuwa","doi":"10.1155/int/1561380","DOIUrl":"https://doi.org/10.1155/int/1561380","url":null,"abstract":"<div>\u0000 <p>In the area of ecology, counting animals to estimate population size and types of species is important for the wildlife conservation. This includes analysing massive volumes of image, video or audio/acoustic data and traditional counting techniques. Automating the process of identifying, classifying and counting animals would be helpful to researchers as it will phase out the tedious human–labour tasks of manual counting and labelling. The intention of this work is to address manual identification and counting methods of images by implementing an automated solution using computer vision and deep learning. This study applies a classification model to classify species and trains an object detection model using deep convolutional neural networks to automatically identify and determine the count of four mammal species in 3304 images extracted from camera traps. The image classification model reports a classification accuracy of 98%, and the YOLOv8 object detection model automatically detects buffalo, elephant, rhino and zebra school mean average precision of 50 of 89% and mean average precision of 50–95 of 72.2% and provides an accurate count over all animal classes. Furthermore, it performs well across various image scenarios such as blurriness, day, night and images displaying multiple species compared to the RT-DETR model. The results of the study display that the application of computer vision and deep learning methods on data-scarce and data-enriched scenarios, respectively, can conserve biologists and ecologists an enormous amount of time used on time-consuming human tasks methods of analysis and counting. The high-performing deep learning models developed capable of accurately classifying and localising multiple species can be integrated into the existing conservation workflows to process large volumes of camera trap images in real time. This integration can significantly reduce the manual labour required for labelling and counting, improve the consistency and speed of wildlife surveys and enable timely decision-making in habitat protection, population assessment and antipoaching initiatives. Additionally, these automated identification techniques can contribute towards enhancing wildlife conservation and future studies.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1561380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705473","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":"A Basic Probability Assignment Generation Method Based on Normal Cloud Similarity and Its Application in Evidence Combination","authors":"Nuo Cheng,&nbsp;Xin Wang","doi":"10.1155/int/8839165","DOIUrl":"https://doi.org/10.1155/int/8839165","url":null,"abstract":"<div>\u0000 <p>The effective utilization of Dempster–Shafer (D-S) evidence theory depends on the accurate establishment of the basic probability assignment (BPA). How to generate more effective BPA for different situations is always an open and hot topic. In this study, we present an approach for obtaining BPA based on the normal cloud model called combined fuzzy similarity measure (CFSM). The method first constructs the normal cloud model of each class of sample in each attribute by an interval number and uses the mean standard deviation to obtain the interval number for the test sample, thereby obtaining the normal cloud model. Then, the similarity between the test samples and the training samples is quantified based on the area relationship, thereby obtaining the BPA of the test samples. Finally, the evidence combination method based on the intuitionistic fuzzy earth mover’s distance (IFEMD) is used for experimental analysis. The experimental results verify the effectiveness of the method and its applicability in the case of small sample data.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8839165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681551","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}