International Journal of Intelligent Systems最新文献

{"title":"Optimizing IIoT Performance: Intelligent Selection of SDN Controllers through AHP Analysis","authors":"Claudio Urrea,&nbsp;David Benítez","doi":"10.1155/2024/7908506","DOIUrl":"https://doi.org/10.1155/2024/7908506","url":null,"abstract":"<div>\u0000 <p>This article deals with the use of software-defined networking (SDN) in the Industrial Internet of Things (IIoT). The use of SDN in IIoT can solve the limitations presented by traditional networks in order to guarantee quality of service (QoS) for new applications. The approach of this work centers on the selection of an SDN controller that satisfies the requirements for the networks of IIoT. Selection is based on the characteristics of the SDN controllers and employs the analytic hierarchy process (AHP). From the review conducted, and as a result of the work, the group of the current best SDN controllers for IIoT is identified, which is a part of the subsequent selection process. Another contribution of this study is that it defines the criteria for comparing these controllers and selecting the most suitable one for this type of application. The established criteria and the employed quantification method via AHP enrich the decision-making process, providing a replicable model for future selections. The objectives and criteria established can be useful for other SDN selection processes to be used in scenarios where delay, jitter, and packet loss are key parameters to consider. This nuanced approach, accommodating both theoretical frameworks and empirical observations, offers an advancement in the strategic deployment of SDN within IIoT environments.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7908506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488916","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 Grey Prediction-Based Reproduction Strategy for Many-Objective Evolutionary Algorithm","authors":"Li-Sen Wei,&nbsp;Er-Chao Li","doi":"10.1155/2024/8994938","DOIUrl":"https://doi.org/10.1155/2024/8994938","url":null,"abstract":"<div>\u0000 <p>Many-objective evolutionary algorithms (MaOEAs) consisted of environmental selection and reproduction operator. However, few studies focus on how to design reproduction operators to improve the performance of MaOEAs. In this paper, a reproduction operator based on grey prediction is proposed for MaOEAs, named GPRS. Specifically, the grey prediction assisted by reference vector is first used to get the target location. Then, a fine regulation is designed to generate potential solutions by handling the different information further. Finally, a gene sharing strategy is executed to accelerate the convergence by information exchange. The effectiveness of the proposed reproduction strategy is validated by comparing it with five widely used reproduction operators by embedding into a classical framework NSGAIII. At the same time, an improved NSGAIIIGPRS is developed by embedding the proposed GPRS and compared with seven excellent algorithms on a number of benchmark problems and one practical application. The final experimental results show that the proposed GPRS has significant advantages over similar reproduction strategies, and the improved NSGAIIGRPS is more effective compared with other excellent algorithms in handling many-objective optimization problem.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8994938","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453600","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":"Learning Cognitive Features as Complementary for Facial Expression Recognition","authors":"Huihui Li,&nbsp;Xiangling Xiao,&nbsp;Xiaoyong Liu,&nbsp;Guihua Wen,&nbsp;Lianqi Liu","doi":"10.1155/2024/7321175","DOIUrl":"https://doi.org/10.1155/2024/7321175","url":null,"abstract":"<div>\u0000 <p>Facial expression recognition (FER) has a wide range of applications, including interactive gaming, healthcare, security, and human-computer interaction systems. Despite the impressive performance of FER based on deep learning methods, it remains challenging in real-world scenarios due to uncontrolled factors such as varying lighting conditions, face occlusion, and pose variations. In contrast, humans are able to categorize objects based on both their inherent characteristics and the surrounding environment from a cognitive standpoint, utilizing concepts such as cognitive relativity. Modeling the cognitive relativity laws to learn cognitive features as feature augmentation may improve the performance of deep learning models for FER. Therefore, we propose a cognitive feature learning framework to learn cognitive features as complementary for FER, which consists of Relative Transformation module (AFRT) and Graph Convolutional Network module (AFGCN). AFRT explicitly creates cognitive relative features that reflect the position relationships between the samples based on human cognitive relativity, and AFGCN implicitly learns the interaction features between expressions as feature augmentation to improve the classification performance of FER. Extensive experimental results on three public datasets show the universality and effectiveness of the proposed method.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7321175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430311","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":"Positivity and Stability of Caputo Fractional Order Gene Regulatory Networks: The System Comparison Method","authors":"Cong Wu","doi":"10.1155/2024/4790696","DOIUrl":"https://doi.org/10.1155/2024/4790696","url":null,"abstract":"<div>\u0000 <p>As well known, the positivity is an essential topic when studying gene regulatory networks since the variables involved, e.g., the concentrations of mRNA and proteins, can never be negative. However, the positivity of Caputo fractional order models has been a longstanding problem due to the nonlocality of Caputo fractional derivatives (CFD). In this paper, we present the system comparison method to prove the positivity of Caputo fractional order gene regulatory networks (CFOGRNs) only under positive initial conditions. Moreover, it is found that the positivity results can make it feasible to give proper comparison systems for CFOGRNs, in which the upper and lower estimations can be used to guarantee the stability of the objective CFOGRNs. Thus, the system comparison method for the stability of CFOGRNs is also provided here. Compared to the existing Lyapunov direct method, the proposed system comparison method affords an alternative method for stability analysis and different insights in stability conditions. Finally, these theoretical derivations are illustrated and validated by an example with numerical simulations.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4790696","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425011","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 System of Predicting Lymph Node Metastasis in Colorectal Cancer Using 3D CT Scans","authors":"Min Xie,&nbsp;Yi Zhang,&nbsp;Xinyang Li,&nbsp;Jiayue Li,&nbsp;Xingyu Zou,&nbsp;Yiji Mao,&nbsp;Haixian Zhang","doi":"10.1155/2024/7629441","DOIUrl":"https://doi.org/10.1155/2024/7629441","url":null,"abstract":"<div>\u0000 <p>In colorectal cancer (CRC), accurately predicting lymph node metastasis (LNM) contributes to developing appropriate treatment plans and serves as the key to long-term survival of patients. In the clinical settings, preoperative LNM diagnosis in CRC predominantly depends on computed tomography (CT). Nevertheless, lymph nodes are small in size and difficult to identify on 3D CT scans, and CT-based diagnosis of metastatic lymph nodes is prone to a significant misdiagnosis rate and lacks consistency across clinicians. Currently, there is no automatic system available for LNM prediction in CRC via 3D CT scans. In addition, existing deep learning- (DL-) based lymph node detection models present low detection accuracy and high false-positive rates, and most existing DL-based lymph node metastasis prediction models mainly use tumor area characteristics but fail to adequately utilize lymph node information, thus not yielding satisfactory results. To tackle these issues, we propose an intelligent diagnosis system for this challenging task, mainly including a lymph node detection (LND) model and a lymph node metastasis prediction (LNMP) model. In detail, the LND model utilizes an encoder-decoder network to detect lymph nodes, and the LNMP model employs an innovative attention-based multiple instance learning (MIL) network. An instance-level self-attention feature enhancement module is designed to extract and augment lymph node features as a bag of instances. Furthermore, a bag-level MIL prediction module is employed to extract instance features and create a bag representation for the ultimate LNM prediction. As far as we know, the proposed intelligent system represents the pioneering method for addressing this complex clinical challenge. In experiments, our proposed intelligent system achieves the AUC of 75.4% and the accuracy of 73.9%, showcasing a significant enhancement compared to physicians specialising in CRC and highlighting its strong clinical applicability. The accessible code can be found at https://github.com/SCU-MI/IS-LNM.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7629441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425012","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":"ARDST: An Adversarial-Resilient Deep Symbolic Tree for Adversarial Learning","authors":"Sheng Da Zhuo,&nbsp;Di Wu,&nbsp;Xin Hu,&nbsp;Yu Wang","doi":"10.1155/2024/2767008","DOIUrl":"https://doi.org/10.1155/2024/2767008","url":null,"abstract":"<div>\u0000 <p>The advancement of intelligent systems, particularly in domains such as natural language processing and autonomous driving, has been primarily driven by deep neural networks (DNNs). However, these systems exhibit vulnerability to adversarial attacks that can be both subtle and imperceptible to humans, resulting in arbitrary and erroneous decisions. This susceptibility arises from the hierarchical layer-by-layer learning structure of DNNs, where small distortions can be exponentially amplified. While several defense methods have been proposed, they often necessitate prior knowledge of adversarial attacks to design specific defense strategies. This requirement is often unfeasible in real-world attack scenarios. In this paper, we introduce a novel learning model, termed “immune” learning, known as adversarial-resilient deep symbolic tree (ARDST), from a neurosymbolic perspective. The ARDST model is semiparametric and takes the form of a tree, with logic operators serving as nodes and learned parameters as weights of edges. This model provides a transparent reasoning path for decision-making, offering fine granularity, and has the capacity to withstand various types of adversarial attacks, all while maintaining a significantly smaller parameter space compared to DNNs. Our extensive experiments, conducted on three benchmark datasets, reveal that ARDST exhibits a representation learning capability similar to DNNs in perceptual tasks and demonstrates resilience against state-of-the-art adversarial attacks.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2767008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141298630","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":"Hyperparameter Optimization of the Machine Learning Model for Distillation Processes","authors":"Kwang Cheol Oh,&nbsp;Hyukwon Kwon,&nbsp;Sun Yong Park,&nbsp;Seok Jun Kim,&nbsp;Junghwan Kim,&nbsp;DaeHyun Kim","doi":"10.1155/2024/5564380","DOIUrl":"https://doi.org/10.1155/2024/5564380","url":null,"abstract":"<div>\u0000 <p>This study was conducted to enhance the efficiency of chemical process systems and address the limitations of conventional methods through hyperparameter optimization. Chemical processes are inherently continuous and nonlinear, making stable operation challenging. The efficiency of processes often varies significantly with the operator’s level of expertise, as most tasks rely on experience. To move beyond the constraints of traditional simulation approaches, a new machine learning-based simulation model was developed. This model utilizes a recurrent neural network (RNN) algorithm, which is ideal for analyzing time-series data from chemical process systems, presenting new possibilities for applications in systems with special chemical reactions or those that are continuous and complex. Hyperparameters were optimized using a grid search method, and optimal results were confirmed when the model was applied to an actual distillation process system. By proposing a methodology that utilizes machine learning for the optimization of chemical process systems, this research contributes to solving new problems that were previously unaddressed. Based on these results, the study demonstrates that a machine learning simulation model can be effectively applied to continuous chemical process systems. This application enables the derivation of unique hyperparameters tailored to the specificities of a limited control volume system.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5564380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141298373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Decision-Making System of Air Defense Resource Allocation via Hierarchical Reinforcement Learning","authors":"Minrui Zhao,&nbsp;Gang Wang,&nbsp;Qiang Fu,&nbsp;Wen Quan,&nbsp;Quan Wen,&nbsp;Xiaoqiang Wang,&nbsp;Tengda Li,&nbsp;Yu Chen,&nbsp;Shan Xue,&nbsp;Jiaozhi Han","doi":"10.1155/2024/7777050","DOIUrl":"https://doi.org/10.1155/2024/7777050","url":null,"abstract":"<div>\u0000 <p>Intelligent decision-making in air defense operations has attracted wide attention from researchers. Facing complex battlefield environments, existing decision-making algorithms fail to make targeted decisions according to the hierarchical decision-making characteristics of air defense operational command and control. What’s worse, in the process of problem-solving, these algorithms are beset by defects such as dimensional disaster and poor real-time performance. To address these problems, a new hierarchical reinforcement learning algorithm named Hierarchy Asynchronous Advantage Actor-Critic (H-A3C) is developed. This algorithm is designed to have a hierarchical decision-making framework considering the characteristics of air defense operations and employs the hierarchical reinforcement learning method for problem-solving. With a hierarchical decision-making capability similar to that of human commanders in decision-making, the developed algorithm produces many new policies during the learning process. The features of air situation information are extracted using the bidirectional-gated recurrent unit (Bi-GRU) network, and then the agent is trained using the H-A3C algorithm. In the training process, the multihead attention mechanism and the event-based reward mechanism are introduced to facilitate the training. In the end, the proposed H-A3C algorithm is verified in a digital battlefield environment, and the results prove its advantages over existing algorithms.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7777050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264634","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":"Vibration Suppression and Trajectory Tracking Control of Flexible Joint Manipulator Based on PSO Algorithm and Fixed-Time Control","authors":"Yan Guan,&nbsp;Yang Wang,&nbsp;Rui Yin,&nbsp;Mingshu Chen,&nbsp;Yaqi Xu","doi":"10.1155/2024/5510259","DOIUrl":"https://doi.org/10.1155/2024/5510259","url":null,"abstract":"<div>\u0000 <p>In this paper, the vibration suppression and trajectory tracking control of a flexible joint manipulator (FJM) based on particle swarm optimization (PSO) and fixed-time nonsingular terminal sliding mode control (NTSMC) are studied. Firstly, in order to suppress the residual vibration of the FJM, an optimal trajectory planning method based on higher-order trajectory planning (HOTP) and the PSO algorithm is proposed. Then, to ensure that the FJM can track the optimized trajectory without being affected by the initial value of the trajectory, a novel fixed-time NTSMC scheme is proposed. Compared with the cubic spline trajectory, the proposed HOTP is smoother and can more accurately suppress the residual vibration of the FJM. By combining the HOTP with the PSO algorithm, the vibration amplitude of FJM can be suppressed to around 0.002 mm. Unlike finite-time NTSMC, the rate of convergence of the proposed fixed-time NTSMC does not depend on the initial value of FJM’s joint trajectory. Especially when the initial value of the trajectory is large, the FJM can still quickly track the optimal trajectory within 0 to 1 s. Finally, the effectiveness of this method is verified through simulation and comparison.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5510259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141245431","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 Efficient Anomaly Detection Method for Industrial Control Systems: Deep Convolutional Autoencoding Transformer Network","authors":"Wenli Shang,&nbsp;Jiawei Qiu,&nbsp;Haotian Shi,&nbsp;Shuang Wang,&nbsp;Lei Ding,&nbsp;Yanjun Xiao","doi":"10.1155/2024/5459452","DOIUrl":"https://doi.org/10.1155/2024/5459452","url":null,"abstract":"<div>\u0000 <p>Industrial control systems (ICSs), as critical national infrastructures, are increasingly susceptible to sophisticated security threats. To address this challenge, our study introduces the CAE-T, a deep convolutional autoencoding transformer network designed for efficient anomaly detection and real-time fault monitoring in ICS. The CAE-T utilizes unsupervised deep learning, employing a convolutional autoencoder for spatial feature extraction from multidimensional time-series data, and combines this with a transformer architecture to capture long-term temporal dependencies. The design of the model facilitates rapid training and inference, while its dual-component approach, utilizing an optimization function based on support vector data description (SVDD), enhances detection accuracy. This integration synergistically combines spatiotemporal feature extraction, significantly improving the robustness and precision of anomaly detection in ICS environments. The CAE-T model demonstrated notable performance enhancements across three industrial control system datasets. Notably, the CAE-T model achieved approximately a 70.8% increase in <i>F</i>1 score and a 9.2% rise in AUC on the WADI dataset. On the SWaT dataset, the model showed improvements of approximately 2.8% in <i>F</i>1 score and 5% in AUC. The power system dataset saw more modest gains, with an approximately 0.1% uptick in <i>F</i>1 score and a 1% increase in AUC. These improvements validate the CAE-T model’s efficacy and robustness in anomaly detection across various scenarios.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5459452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246126","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}