IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第4页

Adaptive Strategies and its Application in the Mittag-Leffler Synchronization of Delayed Fractional-Order Complex-Valued Reaction-Diffusion Neural Networks 延迟分阶复值反应扩散神经网络的自适应策略及其在 Mittag-Leffler 同步中的应用

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-01 DOI: 10.1109/TETCI.2024.3375450

G. Narayanan;M. Syed Ali;Rajagopal Karthikeyan;Grienggrai Rajchakit;Sumaya Sanober;Pankaj Kumar

{"title":"Adaptive Strategies and its Application in the Mittag-Leffler Synchronization of Delayed Fractional-Order Complex-Valued Reaction-Diffusion Neural Networks","authors":"G. Narayanan;M. Syed Ali;Rajagopal Karthikeyan;Grienggrai Rajchakit;Sumaya Sanober;Pankaj Kumar","doi":"10.1109/TETCI.2024.3375450","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3375450","url":null,"abstract":"This paper addresses the Mittag-Leffler synchronization problem of fractional-order reaction-diffusion complex-valued neural networks (FRDCVNNs) with delays. New Mittag-Leffler synchronization (MLS) criteria in the form of the \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000-norm for an error model derived from the drive-response model are constructed. In the design of the adaptive feedback controller, the Lyapunov approach is considered in the framework of the \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000-norm technique, and less conservative algebraic conditions that guarantee MLS for the considered model are given. Moreover, the MLS of the considered model without reaction diffusion effect is investigated using adaptive control. Finally, an example is used to validate the proposed control scheme. To demonstrate the advantages and superiority of the proposed technique over existing methods, an image encryption method based on MLS of FRDCVNNs is considered and solved using the proposed method.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3294-3307"},"PeriodicalIF":5.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach 广义推荐系统：一种高效的非线性协作过滤方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-01 DOI: 10.1109/TETCI.2024.3378599

Ling Huang;Can-Rong Guan;Zhen-Wei Huang;Yuefang Gao;Chang-Dong Wang;C. L. P. Chen

{"title":"Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach","authors":"Ling Huang;Can-Rong Guan;Zhen-Wei Huang;Yuefang Gao;Chang-Dong Wang;C. L. P. Chen","doi":"10.1109/TETCI.2024.3378599","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3378599","url":null,"abstract":"Recently, Deep Neural Networks (DNNs) have been largely utilized in Collaborative Filtering (CF) to produce more accurate recommendation results due to their ability of extracting the nonlinear relationships in the user-item pairs. However, the DNNs-based models usually encounter high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we develop a novel broad recommender system named Broad Collaborative Filtering (BroadCF), which is an efficient nonlinear collaborative filtering approach. Instead of DNNs, Broad Learning System (BLS) is used as a mapping function to learn the nonlinear matching relationships in the user-item pairs, which can avoid the above issues while achieving very satisfactory rating prediction performance. Contrary to DNNs, BLS is a shallow network that captures nonlinear relationships between input features simply and efficiently. However, directly feeding the original rating data into BLS is not suitable due to the very large dimensionality of the original rating vector. To this end, a new preprocessing procedure is designed to generate user-item rating collaborative vector, which is a low-dimensional user-item input vector that can leverage quality judgments of the most similar users/items. Convincing experimental results on seven datasets have demonstrated the effectiveness of the BroadCF algorithm.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2843-2857"},"PeriodicalIF":5.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

State-Observer-Based Adaptive Fuzzy Event-Triggered Formation Control for Nonlinear Multiagent System 基于状态观测器的非线性多代理系统自适应模糊事件触发编队控制

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-01 DOI: 10.1109/TETCI.2024.3377254

Shuai Sui;Dongyu Shen;Shaocheng Tong;C. L. Philip Chen

引用次数: 0

Hybrid Architecture-Based Evolutionary Robust Neural Architecture Search 基于混合架构的进化鲁棒神经架构搜索

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-29 DOI: 10.1109/TETCI.2024.3400867

Shangshang Yang;Xiangkun Sun;Ke Xu;Yuanchao Liu;Ye Tian;Xingyi Zhang

{"title":"Hybrid Architecture-Based Evolutionary Robust Neural Architecture Search","authors":"Shangshang Yang;Xiangkun Sun;Ke Xu;Yuanchao Liu;Ye Tian;Xingyi Zhang","doi":"10.1109/TETCI.2024.3400867","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3400867","url":null,"abstract":"The robustness of neural networks in image classification is important to resist adversarial attacks. Although many researchers proposed to enhance the network robustness by inventing network training paradigms or designing network architectures, existing approaches are mainly based on a single type of networks, e.g., convolution neural networks (CNNs) or vision Transformer (ViT). Considering a recently revealed fact that CNNs and ViT can effectively defend against adversarial attacks transferred from each other, this paper aims to enhance network robustness by designing robust hybrid architecture networks containing different types of networks. To this end, we propose a hybrid architecture-based evolutionary neural architecture search approach for robust architecture design, termed HA-ENAS. Specifically, to combine or aggregate different types of networks in the same network framework, a multi-stage block-wise hybrid architecture network is first devised as the supernet, where three types of blocks (called convolution blocks, Transformer blocks, multi-layer perception blocks) are further designed as each block's candidate, and thus a hybrid architecture-based search space is established for HA-ENAS; then, the robust hybrid architecture search is formulated as an optimization problem maximizing both clean and adversarial accuracy of architectures, and an efficient multi-objective evolutionary algorithm is employed to solve the problem, where a supernet-based retraining evaluation and a surrogate model are used to mitigate coupled weight influence and reduce the whole search cost. Experimental results show that the hybrid architectures found by the proposed HA-ENAS outperform state-of-the-art single-type architectures in terms of clean accuracy and adversarial accuracy under a variety of common attacks.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2919-2934"},"PeriodicalIF":5.3,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmented Intelligence Based COVID-19 Diagnostics and Deep Feature Categorization Based on Federated Learning 基于增强智能的 COVID-19 诊断和基于联合学习的深度特征分类

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-29 DOI: 10.1109/TETCI.2024.3375455

Syed Thouheed Ahmed;Vinoth Kumar Venkatesan;Mahesh T R;Roopashree S;Muthukumaran Venkatesan

{"title":"Augmented Intelligence Based COVID-19 Diagnostics and Deep Feature Categorization Based on Federated Learning","authors":"Syed Thouheed Ahmed;Vinoth Kumar Venkatesan;Mahesh T R;Roopashree S;Muthukumaran Venkatesan","doi":"10.1109/TETCI.2024.3375455","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3375455","url":null,"abstract":"The global pandemic of COVID-19 has had profound and devastating effects on human life since its emergence in 2019. This viral infection predominantly impacts the respiratory system, causing a range of severity in alveolar overlapping that results in breathlessness and fatality. A novel methodology was assessed using the primary COVID-19 dataset from Kaggle, employing a federated learning ecosystem with multi-user datasets. This technique involves extracting data logs from various user repositories and datasets within the federated learning framework. Subsequently, a validation process is conducted, followed by computation utilizing a deep feature set categorization technique augmented by artificial intelligence. This augmented intelligence is showcased in a multi-layer image classification system designed for feature recognition and extraction. The training dataset, comprising 1056 data samples, is split into 647 for training and 409 for testing. Experimental outcomes highlighted a more comprehensive mapping and prioritization of features relative to attribute values. Remarkably, the proposed classification technique surpasses existing methods in accurately labeling COVID-19 detection as opposed to pneumonia and normal lung conditions in MRI/CT images.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3308-3315"},"PeriodicalIF":5.3,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Single-Step Adversarial Training With Energy-Based Models 利用基于能量的模型进行有效的单步对抗训练

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-29 DOI: 10.1109/TETCI.2024.3378652

Keke Tang;Tianrui Lou;Weilong Peng;Nenglun Chen;Yawen Shi;Wenping Wang

引用次数: 0

A Novel Projection Neural Network for Sparse Optimization With ${L_mathrm{{1}}}$-Minimization Problem 用于稀疏优化的新型投影神经网络与 ${L_mathrm{1}}$ 最小化问题

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-28 DOI: 10.1109/TETCI.2024.3377265

Hongsong Wen;Xing He;Tingwen Huang

{"title":"A Novel Projection Neural Network for Sparse Optimization With ${L_mathrm{{1}}}$-Minimization Problem","authors":"Hongsong Wen;Xing He;Tingwen Huang","doi":"10.1109/TETCI.2024.3377265","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377265","url":null,"abstract":"In this paper, a novel projection neural network (PNN) for solving the \u0000<inline-formula><tex-math>$L_{1}$</tex-math></inline-formula>\u0000-minimization problem is proposed, which can be applied to sparse signal reconstruction and image reconstruction. First, a one-layer PNN is designed with the projection matrix and the projection operator, which is shown to be stable in the Lyapunov sense and converges globally to the optimal solution of the \u0000<inline-formula><tex-math>$L_{1}$</tex-math></inline-formula>\u0000-minimization problem. Then, the finite-time convergence of the proposed PNN is further investigated, with the upper bound on the convergence time given and the convergence rate analyzed. Finally, we make comparisons of our proposed PNN with the existing neural networks. Experimental results based on random Gaussian sparse signals demonstrate the effectiveness and performance of our proposed PNN. Moreover, the experiments on grayscale image reconstruction and color image reconstruction are further implemented, which sufficiently demonstrate the superiority of our proposed PNN.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3339-3351"},"PeriodicalIF":5.3,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flow-Shop Scheduling Problem With Batch Processing Machines via Deep Reinforcement Learning for Industrial Internet of Things 通过深度强化学习解决工业物联网批量处理机的流水线调度问题

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-28 DOI: 10.1109/TETCI.2024.3402685

Zihui Luo;Chengling Jiang;Liang Liu;Xiaolong Zheng;Huadong Ma

{"title":"Flow-Shop Scheduling Problem With Batch Processing Machines via Deep Reinforcement Learning for Industrial Internet of Things","authors":"Zihui Luo;Chengling Jiang;Liang Liu;Xiaolong Zheng;Huadong Ma","doi":"10.1109/TETCI.2024.3402685","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3402685","url":null,"abstract":"The rapidly evolving Industrial Internet of Things (IIoT) is driving the transition from conventional manufacturing to intelligent manufacturing. Intelligent shop scheduling, as one of the essential components of intelligent manufacturing in IIoT, is desired to allocate jobs on different machines to achieve specific production targets. The flow-shop scheduling problem with batch processing machines (FSSP-BPM), which widely exists in real-world manufacturing, requires two distinct but interdependent decisions: batch formation and job scheduling. Existing approaches rely on fixed search paradigms that utilize expert knowledge to find satisfactory solutions. However, these methods struggle to ensure solution quality under real-time constraints due to the varying data distribution and the complexity of large-scale practical problems. To address this challenge, we propose a deep reinforcement learning (DRL) based method. First, we formulate the FSSP-BPM decision process as a Markov Decision Process (MDP) and design the corresponding state, action, and reward. Second, we propose a basic scheduling framework based on an encoder-decoder model with the attention mechanism. Finally, we design a batch formation module and a scheduling module trained on unlabeled multi-dimensional data. Extensive experiments on public benchmark datasets and actual production data demonstrate that the proposed method outperforms baseline algorithms and improves makespan performance by an average of 8.33%.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3518-3533"},"PeriodicalIF":5.3,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach 混合 IRS 辅助安全卫星下行链路通信：快速深度强化学习方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-28 DOI: 10.1109/TETCI.2024.3378605

Quynh Tu Ngo;Khoa Tran Phan;Abdun Mahmood;Wei Xiang

{"title":"Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach","authors":"Quynh Tu Ngo;Khoa Tran Phan;Abdun Mahmood;Wei Xiang","doi":"10.1109/TETCI.2024.3378605","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3378605","url":null,"abstract":"This paper considers a secure satellite downlink communication system with a hybrid intelligent reflecting surface (IRS). A robust design problem for the satellite and IRS joint beamforming is formulated to maximize the system's worst-case secrecy rate, considering practical models of the outdated channel state information and IRS power consumption. We leverage deep reinforcement learning (DRL) to solve the problem by proposing a fast DRL algorithm, namely the deep post-decision state–deterministic policy gradient (DPDS-DPG) algorithm. In DPDS-DPG, the prior known system dynamics are exploited by integrating the PDS concept into the traditional deep DPG (DDPG) algorithm, resulting in faster learning convergence. Simulation results show a faster learning convergence of 50% for DPDS-DPG compared to DDPG, with a comparable achievable system secrecy rate. Additionally, the results demonstrate system secrecy rate gains of 52% and 35% when employing active IRS and hybrid IRS, respectively, over conventional passive IRS, thereby supporting secure communications.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2858-2869"},"PeriodicalIF":5.3,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discovering Interpretable Latent Space Directions for 3D-Aware Image Generation 为三维感知图像生成发现可解释的潜在空间方向

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-27 DOI: 10.1109/TETCI.2024.3369319

Zhiyuan Yang;Qingfu Zhang

{"title":"Discovering Interpretable Latent Space Directions for 3D-Aware Image Generation","authors":"Zhiyuan Yang;Qingfu Zhang","doi":"10.1109/TETCI.2024.3369319","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369319","url":null,"abstract":"2D GANs have yielded impressive results especially in image synthesis. However, they often encounter challenges with multi-view inconsistency due to the absence of 3D perception in their generation process. To overcome this shortcoming, 3D-aware GANs have been proposed to take advantage of both 3D representation methods, GANs, but it is very difficult to edit semantic attributes. To explore the semantic disentanglement in the 3D-aware latent space, this paper proposes a general framework, presents two representative approaches for the 3D manipulation task in both supervised, unsupervised manners. Our key idea is to utilize existing latent discovery methods, bring direct compatibility to 3D control. Specifically, we propose a novel module to extract the semantic latent space of the existing 3D-aware models, then develop two approaches to find a normal editing direction in the latent space. Leveraging the meaningful semantic latent directions, we can easily edit the shape, appearance attributes while preserving the 3D consistency. Quantitative, qualitative experiments show that our method is effective, efficient for the 3D-aware generation with steerability on both synthetic, real-world datasets.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2570-2580"},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141094885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0