Computational Intelligence最新文献

{"title":"A Method for Constructing Open-Channel Velocity Field Prediction Model Based on Machine Learning and CFD","authors":"Bo Li,&nbsp;Cheng Jin,&nbsp;Ruixiang Lin,&nbsp;Xinzhi Zhou,&nbsp;Mingjiang Deng","doi":"10.1111/coin.70043","DOIUrl":"https://doi.org/10.1111/coin.70043","url":null,"abstract":"<div>\u0000 \u0000 <p>Rapid and accurate prediction of the sectional velocity field of the channel is of great significance to the design and maintenance of open channels and the improvement of irrigation efficiency. During the water delivery process of Renmin Canal of Dujiangyan irrigation system, the water level of the main canal changes rapidly and in a large range, which is the biggest difficulty in real-time prediction of its velocity field. Therefore, based on machine learning, this paper proposes a new method to construct a real-time velocity field prediction model, which can directly predict the velocity field of the channel according to the water level. According to this method, the computational fluid dynamics (CFD) technology is used to simulate the target open channel, and a machine learning model that can adaptively optimize the characteristics of the velocity field data is designed as the velocity field prediction model, which is experimented in the main canal of Renmin Canal of Dujiangyan irrigation system. The results suggest that the predictions are in line with the general features of flow velocity distribution in open channels and have high precision. Therefore, this method is of high value for engineering application and theoretical research.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Violence Detection in Video Using Statistical Features of the Optical Flow and 2D Convolutional Neural Network","authors":"Javad Mahmoodi,&nbsp;Hossein Nezamabadi-Pour","doi":"10.1111/coin.70034","DOIUrl":"https://doi.org/10.1111/coin.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid growth of video data has resulted in an increasing need for surveillance and violence detection systems. Although such events occur less frequently than normal activities, developing automated video surveillance systems for violence detection has become essential to minimize labor and time waste. Detecting violent activity in videos is a challenging task due to the variability and diversity of violent behavior, which can involve a wide range of actions, motions, and interactions between people and objects. Currently, researchers employ deep learning models to detect violent behaviors. In fact, a large number of deep learning approaches are based on extracting spatio-temporal information from a video by exploiting a 3D Convolutional Neural Network (CNN). Despite their success, these techniques require a lot more parameters than 2D CNNs and have high computational complexity. Therefore, we focus on exploiting a 2D CNN to encode spatio-temporal information. Actually, statistical features of the optical flow changes are used to give this ability to a 2D CNN. These features are designed to make attention to regions of a video clip with much more motion. Accordingly, the optical flow of an input video is calculated. To determine meaningful changes in the optical flow, the optical flow magnitude of a current frame is compared with its predecessor. After that, statistical features of these changes are extracted to summarize a video clip to a 2D template, which feeds a 2D CNN. Experimental results on four benchmark datasets observe that the suggested strategy outperforms baseline ones. In particular, we make a better estimation of the spatio-temporal features in a video by shortening a video clip into a 2D template.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Real-Time Solutions for Dynamic Complex Matrix Inversion and Chaotic Control Using ODE-Based Neural Computing Methods","authors":"Cheng Hua,&nbsp;Xinwei Cao,&nbsp;Bolin Liao","doi":"10.1111/coin.70042","DOIUrl":"https://doi.org/10.1111/coin.70042","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a robust dual-integral structure zeroing neural network (ZNN) design framework, effectively overcoming the limitations of existing single-integral enhanced ZNN models in completely suppressing linear noise. Based on this design framework, a complex-type dual-integral structure ZNN (DISZNN) model with inherent linear noise suppression capability is constructed for computing dynamic complex matrix inversion (DCMI) online. The stability, convergence, and robustness of the proposed DISZNN model are ensured via rigorous theoretical analyses. In three distinct experiments involving DCMI (including cases with only imaginary parts, both real and imaginary parts, and high-dimensional scenarios), the state trajectories of the DISZNN model are well and quickly fitted to the dynamic trajectories of the theoretical solutions with very low residual errors in various linear noise environments. More specifically, the residual errors of the DISZNN model for online computation of DCMI under linear noise environments are consistently below the order of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <msup>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ 1{0}^{-3} $$</annotation>\u0000 </semantics></math>, representing one-thousandth of the residual errors in existing noise-tolerant ZNN models. Finally, the DISZNN design framework is applied to construct a controlled chaotic system of a permanent magnet synchronous motor (PMSM) with uncertainties and external disturbances based on real-world modeling. Experimental results demonstrate that the three state errors of the controlled PMSM chaotic system converge to zero quickly and stably under various conditions (system parameters, external disturbances, and uncertainties), further highlighting the superiority and generalizability of the DISZNN design framework.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143581675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Improving Neural Machine Translation Through Code-Mixed Data Augmentation","authors":"Ramakrishna Appicharla,&nbsp;Kamal Kumar Gupta,&nbsp;Asif Ekbal,&nbsp;Pushpak Bhattacharyya","doi":"10.1111/coin.70033","DOIUrl":"https://doi.org/10.1111/coin.70033","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper studies neural machine translation (NMT) of code-mixed (CM) text. Specifically, we generate synthetic CM data and how it can be used to improve the translation performance of NMT through the data augmentation strategy. We conduct experiments on three data augmentation approaches viz. CM-Augmentation, CM-Concatenation, and Multi-Encoder approaches, and the latter two approaches are inspired by document-level NMT, where we use synthetic CM data as context to improve the performance of the NMT models. We conduct experiments on three language pairs, viz. Hindi–English, Telugu–English and Czech–English. Experimental results demonstrate that the proposed approaches significantly improve performance over the baseline model trained without data augmentation and over the existing data augmentation strategies. The CM-Concatenation model attains the best performance.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Optimized Residual Attention Based Generalized Adversarial Network for COVID-19 Classification Using Chest CT Images","authors":"A. V. P. Sarvari,&nbsp;K. Sridevi","doi":"10.1111/coin.70031","DOIUrl":"https://doi.org/10.1111/coin.70031","url":null,"abstract":"<div>\u0000 \u0000 <p>The early detection and classification of COVID-19 is crucial for disease diagnosis and control. To reduce the need for medical professionals, fast and accurate detection approaches for COVID-19 are required. Due to environmental concerns, the quality of the image gets degraded. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19. Thus, the performance of the deep learning (DL) techniques is diminished. Therefore, a CT image-based hybrid DL technology is presented in this article for the classification of COVID-19 disease as COVID or non-COVID or pneumonia. Initially, in the pre-processing stage, the hybrid nonlocal moment bilateral filtering (Hybrid NMBF) technique is introduced for image de-noising and re-sizing. After pre-processing, the image is fed into the feature extraction phase. Gray-level covariance matrices (GLCM) technique is used to extract the relevant features and reduce feature dimensionality issues. For the feature selection process, the enhanced Archimedes optimization algorithm (EAOA) is introduced to select optimal features. The residual channel attention-generative adversarial network (RCA-GAN) technique is introduced for image classification. Here, the hyperparameter of the network is tuned using the Sandpiper optimization (SPO) algorithm to optimize the loss function. The data set used in this research is COVID-CT-machine learning deep learning (MD), and the performance is analyzed using the MATLAB tool. In the experimental scenario, the proposed system achieves 98.3% accuracy, 98.7% specificity, 99.4% sensitivity, 97.4% <i>F</i>-score, and 96.1% kappa. The attained results prove that the proposed system works better than the traditional techniques.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"PCIR: Privacy-Preserving Convolutional Neural Network Inference With Rapid Responsiveness","authors":"Jinguo Li,&nbsp;Yan Yan,&nbsp;Kai Zhang,&nbsp;Chunlin Li,&nbsp;Peichun Yuan","doi":"10.1111/coin.70030","DOIUrl":"https://doi.org/10.1111/coin.70030","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;Several companies leverage trained convolutional neural networks (CNNs) to offer predictive services to users. These companies capitalize on CNNs' superior performance in image processing tasks, such as autonomous driving or face recognition. To safeguard data privacy and model parameters, various algorithms have been proposed. Most of them are predominantly designed using secure multi-party computation (MPC) or hardware-assisted solutions. However, certain limitations persist. First, MPC-based approaches (e.g., garbled circuits, homomorphic encryption) fail to meet rapid responsiveness requirements. Additionally, hardware-assisted solutions impose extra burdens to realize secure inference tasks. The primary reasons for these shortcomings can be summarized as follows: (1) high computation and communication delays are introduced by heavy cryptographic operations during the online phase. (2) Additional overhead for sharing triples. In this article, we propose PCIR, a secure protocol for privacy-preserving convolutional neural network inference (PCIR). PCIR aims to address the aforementioned issues based on a pre-shared secret sharing mechanism. It can achieve rapid responses to user requirements and preserve privacy of data and model for the following reasons: (1) it circumvents computationally expensive operations, such as an operation for permuting plaintext slots, which runs 56 times slower than a homomorphic addition operation, and 34 times slower than a homomorphic multiplication operation. (2) Computational operations, such as homomorphic additions or multiplications, are conducted during the pre-computation phase. It can significantly reduce the online computing costs. (3) PCIR conducts secure multiplication based on pre-shared secret shares. It results in much lower communication and computation costs compared with the use of multiplicative triples. Finally, we evaluate PCIR with benchmark neural networks trained on the MNIST and CIFAR-10 datasets. The results have shown that PCIR requires &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;7&lt;/mn&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 1.3times -3.7times $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; less time and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;12&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 1.1times -12.3times $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; less communication cost than pr","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Vision-Based UAV Detection and Tracking Using Deep Learning and Kalman Filter","authors":"Nancy Alshaer,&nbsp;Reham Abdelfatah,&nbsp;Tawfik Ismail,&nbsp;Haitham Mahmoud","doi":"10.1111/coin.70026","DOIUrl":"https://doi.org/10.1111/coin.70026","url":null,"abstract":"<p>The rapid increase in unmanned aerial vehicles (UAVs) usage across various sectors has heightened the need for robust detection and tracking systems due to safety and security concerns. Traditional methods like radar and acoustic sensors face limitations in noisy environments, underscoring the necessity for advanced solutions such as deep learning-based detection and tracking. Hence, this article proposes a two-stage platform designed to address these challenges by detecting, classifying, and tracking various consumer-grade UAVs. The tracking efficacy of the proposed system is assessed using a combination of deep learning and Kalman filter techniques. Specifically, we evaluate models such as YOLOv3, YOLOv4, YOLOv5, and YOLOx to identify the most efficient detector for the initial detection stage. Moreover, we employ both the Kalman filter and the Extended Kalman filter for the tracking stage, enhancing the system's robustness and enabling real-time tracking capabilities. To train our detector, we construct a dataset comprising approximately 10,000 records that capture the diverse environmental and behavioural conditions experienced by UAVs during their flight. We then present both visual and analytical results to assess and compare the performance of our detector and tracker. Our proposed system effectively mitigates cumulative detection errors across consecutive video frames and enhances the accuracy of the target's bounding boxes.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"TransPapCanCervix: An Enhanced Transfer Learning-Based Ensemble Model for Cervical Cancer Classification","authors":"Barkha Bhavsar,&nbsp;Bela Shrimali","doi":"10.1111/coin.70027","DOIUrl":"https://doi.org/10.1111/coin.70027","url":null,"abstract":"<div>\u0000 \u0000 <p>Cervical cancer, like many other cancers, is most treatable when detected at an early stage. Using classification methods helps find early signs of cancer and small tumors. This allows doctors to act quickly and offer treatments that might cure the cancer. This paper presents a comprehensive approach to the classification of squamous cell carcinoma (SCC) leveraging a dataset comprising 1140 single-cell images sourced from Herlev. In addition to that, in this work, a new ensemble model based on the transfer-learning (TL) technique is developed on various deep learning models, including DenseNet121, DenseNet169, InceptionResNet, XceptionNet, ResNet50, and ResNet101 to demonstrate their efficacy in classifying diverse cellular features. To evaluate our proposed approach's performance, the ensemble approach's results are compared with some transfer learning models such as DenseNet121, DenseNet169, InceptionResNet, XceptionNet, ResNet50, and ResNet101. The experimental results demonstrate that transfer learning-based deep neural networks combined with ensemble methods enhance the diagnostic accuracy of SCC classification systems, achieving 98% accuracy across various cell types. This further validates the effectiveness of the proposed approach. A comprehensive investigation yields a precise and efficient model for SCC classification, offering detailed insights into both normal and abnormal cell types.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An Implementation of Adaptive Multi-CNN Feature Fusion Model With Attention Mechanism With Improved Heuristic Algorithm for Kidney Stone Detection","authors":"Gunasekaran Kulandaivelu,&nbsp;M Suchitra,&nbsp;R Pugalenthi,&nbsp;Ruchika Lalit","doi":"10.1111/coin.70028","DOIUrl":"https://doi.org/10.1111/coin.70028","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, most people have been admitted to emergencies with severe pain caused by kidney stones worldwide. In this case, diverse imaging approaches are aided in the detection process of stones in the kidney. Moreover, the specialist acquires better diagnosis and interpretation of this image. Here, computer-aided techniques are considered the practical techniques, which it is used as the auxiliary tool for the process of diagnosis. Most urologists have failed to train the type of kidney stone identification effectively and it is operator-dependent. Concerning the surgical operation, there is a requirement for accurate as well as adequate detection of stone position in the kidney. Thus, it has made the detection process even more difficult. To overcome the challenging issues, an effective detection model for kidney stones using classifiers. Initially, the input images are collected from the standard resources. Further, the input images are subjected to the adaptive multi-convolutional neural network with attention mechanism (AMC-AM) feature fusion model, in which, the pertinent features are extracted from the three networks: Visual Geometry Group16 (VGG16), Residual Network (ResNet), and Inception net. Thus, the three distinct features are obtained for the feature fusion procedure. Finally, the resultant features are subjected as input to the final layer of CNN. In the proposed network, the model is integrated with the attention mechanism and also the parameter tuning is done by proposing the modified social distance of coronavirus mask protection algorithm (MSD-CMPA). Therefore, the performance is examined using different metrics and compared with other baseline models. Hence, the proposed model overwhelms the outstanding results in detecting the kidney stones that aid the individual in getting rid of kidney disorders.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Deep Reinforcement Learning Based Flow Aware-QoS Provisioning in SD-IoT for Precision Agriculture","authors":"Mohammed J. F. Alenazi,&nbsp;Mahmoud Ahmad Al-Khasawneh,&nbsp;Saeedur Rahman,&nbsp;Zaid Bin Faheem","doi":"10.1111/coin.70023","DOIUrl":"https://doi.org/10.1111/coin.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>To meet the demands of modern technologies such as 5G, big data, edge computing, precision, and sustainable agriculture, the combination of Internet-of-Things (IoT) with software-defined networking (SDN) known as SD-IoT is suggested to automate the network by leveraging the programmable and centralized SDN interfaces. The previous literature has suggested quality-of-service (QoS) aware flow processing using manual strategies or heuristic algorithms, however, these schemes proposed with white-box approaches do not provide effective results as the network scales or dynamic changes are happening. This article proposes a novel QoS provision strategy using deep reinforcement learning (DRL) to calculate the optimal routes autonomously for SD-IoT traffic. To satisfy the different demands of flows in the SD-IoT network the flows are divided into two types. Hence, based on their service demand the routes are generated for them as per service request. The scenario is explained with precision agriculture based on SD-IoT and results are compared with benchmark strategies. A real internet topology is used for the evaluation of results. The results indicated that the proposed method gives improvements for QoS such as delay, throughput, packet loss rate, and jitter compared with benchmark models.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}