Article
Computer Science, Artificial Intelligence
Xinsen Zhou, Wenyong Gui, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen
Summary: Continuous ant colony optimization algorithm incorporates a random following strategy to enhance global optimization performance and effectively handle high-dimensional feature selection problems. The algorithm performs competitively with other state-of-the-art algorithms in benchmark tests and outperforms well-known classification methods on high-dimensional datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
A. Zhiwei Ye, B. Ruihan Li, C. Wen Zhou, D. Mingwei Wang, E. Mengqing Mei, F. Zhe Shu, G. Jun Shen
Summary: This paper proposes two innovative feature selection methods that integrate ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO) to address the issue of redundant or irrelevant features in high-dimensional data analysis.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ziqian Wang, Shangce Gao, Yong Zhang, Lijun Guo
Summary: Feature selection is a crucial data-mining technique, and swarm intelligence has been successfully applied in this field. This study proposes a novel ant colony optimization method that significantly improves classification performance by constructing a probabilistic sequence-based graphical representation.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zana Azeez Kakarash, Farhad Mardukhia, Parham Moradi
Summary: This paper proposes a method of filtering and multi-label feature selection to address the issue of reduced machine learning performance in high-dimensional data. The method utilizes a graph-based density peaks clustering to group similar features and uses ant colony optimization search process to rank features based on their relevancy and redundancy. Experimental results show the superiority of the proposed method over baseline and state-of-the-art methods.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Yusheng Chen, Weiping Ding, Jiucheng Xu, Yuanyuan Ma
Summary: This article proposes a novel adaptive fuzzy neighborhood-based multilabel feature subset selection approach with ant colony optimization (ACO) for multilabel classification. It addresses the issue of ignoring correlations among labels and the manual setting of neighborhood radius in existing feature selection models. The approach combines feature cosine similarity and label Jaccard similarity to effectively reflect overall similarity between samples, and utilizes dynamic adjustment coefficients to control label similarity importance. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving excellent feature subset for multilabel classification.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Ziqian Wang, Shangce Gao, Mengchu Zhou, Syuhei Sato, Jiujun Cheng, Jiahai Wang
Summary: Feature selection is a multiobjective optimization problem that aims to reduce the number of selected features and improve classification performance. This article proposes an Information-theory-based Nondominated Sorting ACO (INSA) method to tackle the problematic characteristics in feature selection. Experimental results demonstrate that INSA is capable of obtaining feature subsets with better performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Mohsen Paniri, Mohammad Bagher Dowlatshahi, Hossein Nezamabadi-pour
Summary: This paper proposes a new multi-label feature selection method based on Ant Colony Optimization, using a heuristic learning approach to enhance performance. Experimental results demonstrate that the proposed method significantly outperforms competing methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Green & Sustainable Science & Technology
Mohammad Karimzadeh Kolamroudi, Mustafa Ilkan, Fuat Egelioglu, Babak Safaei
Summary: This study ranked the factors influencing the output power of a low concentrator photovoltaic system with 4 mirrors. The results showed that the system with 4 mirrors had roughly the same average power output as the reference panel, but it was 2.8 times higher in summer and winter. Additionally, the average power output improvement in the PV panel was 184.01% in summer and 179.42% in winter. The parameters affecting the output power were determined using feature selection and ant colony optimization methods.
Article
Computer Science, Artificial Intelligence
Boyang Xu, Ali Asghar Heidari, Zhennao Cai, Huiling Chen
Summary: This study proposes a variant of the colony predation algorithm (CPA) called Covariance Gaussian cuckoo Colony Predation Algorithm (CGCPA), which employs a designed gaussian cuckoo variable dimensional strategy to enhance population diversity and global search ability, and a covariance matrix adaptation evolution strategy to enhance convergence speed and capture the global optimal solution. Experimental results show that CGCPA outperforms state-of-the-art algorithms in terms of convergence speed and accuracy.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Amin Hashemi, Mehdi Joodaki, Nazanin Zahra Joodaki, Mohammad Bagher Dowlatshahi
Summary: Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic algorithm inspired by real ants' behavior. It uses pheromone trails to find optimal solutions to complex combinatorial optimization problems. This paper proposes an ACO algorithm based on the ensemble of heuristics using a Multi-Criteria Decision-Making (MCDM) procedure. The algorithm selects multiple heuristics to improve the performance and stability of ACO.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematics
Ibrahim Al-Shourbaji, Na Helian, Yi Sun, Samah Alshathri, Mohamed Abd Elaziz
Summary: This paper discusses the importance of feature selection in the telecommunications industry for machine learning models. It introduces a new approach that combines ant colony optimization and reptile search algorithm, and evaluates its performance in customer churn prediction.
Article
Computer Science, Artificial Intelligence
Hong Wang, Ben Niu, Lijing Tan
Summary: This paper investigates a new bacterial colony-based feature selection algorithm, which enhances the performance of feature subsets by weighting features, recording feature appearance frequency, and minimizing classification error with an acceptable number of features. Experimental results demonstrate that the proposed algorithm outperforms other seven feature selection methods by achieving higher classification accuracy rate with smaller feature sets.
Article
Computer Science, Artificial Intelligence
Emrah Hancer
Summary: Feature selection aims to select a feature subset that contributes the most to the performance of a further process. This paper introduces an improved cost-sensitive subset selection method that minimizes both the classification error rate and the feature cost. The proposed method outperforms other multi-objective optimization algorithms according to benchmark tests.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Biology
Xixi He, Huajun Ye, Rui Zhao, Mengmeng Lu, Qiwen Chen, Lishimeng Bao, Tianmin Lv, Qiang Li, Fang Wu
Summary: Changes in human lifestyles have led to a dramatic increase in the incidence of Crohn's disease worldwide. Predicting the activity and remission of Crohn's disease has become an urgent research problem. In this paper, a wrapper feature selection classification model called bIACOR-KELM-FS was proposed, which combines the improved ant colony optimization algorithm and the kernel extreme learning machine. The model showed a high prediction accuracy of 98.98% for predicting the activity and remission of Crohn's disease, and the analysis of important attributes improved the interpretability of the model and provided a reference for the diagnosis of Crohn's disease.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Noura Metawa, Irina V. Pustokhina, Denis A. Pustokhin, K. Shankar, Mohamed Elhoseny
Summary: Financial decisions are currently based on classifier techniques for predicting financial crises. Selecting relevant features improves classification results. A new FS method combining EHO and MWWO algorithms shows significant performance enhancements in financial crisis prediction.
Article
Computer Science, Artificial Intelligence
Ronghua Shang, Jiarui Kong, Lujuan Wang, Weitong Zhang, Chao Wang, Yangyang Li, Licheng Jiao
Summary: This paper proposes an unsupervised feature selection method, FSDSC, which integrates discrete spectral clustering and feature weights. The method combines regression models and spectral clustering in a unified framework and introduces a feature weight matrix to improve feature selection performance.
Article
Automation & Control Systems
Vincent Havyarimana, Zhu Xiao, Thabo Semong, Jing Bai, Hongyang Chen, Licheng Jiao
Summary: This article proposes a reliable fusion technique, called non-Gaussian Redheffer weighted least squares (nGRWLSs), for intervehicle positioning estimation in various GNSS outage environments. The method combines the Gaussian dynamical matrix principle and the Redheffer distribution function to accurately estimate their positions.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Ronghua Shang, Weitong Zhang, Jingwen Zhang, Licheng Jiao, Yangyang Li, Rustam Stolkin
Summary: This article proposes a new local community detection algorithm that utilizes alternating strong fusion and weak fusion strategies to fuse nodes, improving the solution in each stage. A new membership function is proposed in the strong fusion phase, considering both node information and connection information, leading to higher quality fused nodes while preserving community structure. In the weak fusion phase, a parameter-based similarity measure is proposed to detect influential nodes in local communities. Additionally, a local community evaluation metric is proposed that does not require true division to determine the optimal local community under different parameters.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Review
Computer Science, Artificial Intelligence
Licheng Jiao, Dan Wang, Yidong Bai, Puhua Chen, Fang Liu
Summary: In this article, we provide a comprehensive review of the development of deep learning in visual tracking, including deep feature representations, network architecture, and key issues. We also analyze the performance of DL-based approaches and propose future directions and tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Environmental Sciences
Yanqiao Chen, Yangyang Li, Heting Mao, Guangyuan Liu, Xinghua Chai, Licheng Jiao
Summary: Remote sensing image scene classification (RSISC) has attracted significant attention in recent years. Deep learning methods have shown promising performance in classifying remote sensing images (RSI), but they usually require a large amount of labeled data. Acquiring sufficient labeled data is costly, making few-shot RSISC highly meaningful. In this study, we propose a discriminative enhanced attention-based deep nearest neighbor neural network (DEADN4) to address the few-shot RSISC task. Our approach introduces center loss, deep local-global descriptors (DLGD), and modifies the Softmax loss with cosine margin. Experimental results on diverse RSI datasets demonstrate the efficacy of our approach compared to state-of-the-art methods.
Article
Environmental Sciences
Tianyi Zhang, Chenhao Qin, Weibin Li, Xin Mao, Liyun Zhao, Biao Hou, Licheng Jiao
Summary: In this study, we proposed a new method (MF-SegFormer) for automatically extracting water bodies from remote sensing images in complex environments. The method, which combines multiscale fusion and feature enhancement techniques, performs well in extracting small water bodies and water body edge information, and its superiority is demonstrated through comparisons with other methods.
Article
Automation & Control Systems
Qigong Sun, Xiufang Li, Licheng Jiao, Yan Ren, Fanhua Shang, Fang Liu
Summary: This article proposes a novel sequential single-path search (SSPS) method for mixed-precision model quantization, which introduces given constraints to guide the searching process and improves search efficiency and convergence speed. The experiments demonstrate that the method significantly outperforms uniform-precision models for different architectures and datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Geochemistry & Geophysics
Jing Chen, Biao Hou, Bo Ren, Qian Wu, Licheng Jiao
Summary: This paper proposes a PolSAR classification algorithm based on the Wishart locally constrained expansion (WLCE) algorithm, combined with convolutional neural network and Markov random field for training and post-processing. Experimental results show that the algorithm outperforms state-of-the-art algorithms on several benchmark datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Jie Feng, Zizhuo Gao, Ronghua Shang, Xiangrong Zhang, Licheng Jiao
Summary: Generative adversarial network (GAN) and its variants provide a powerful training mechanism for hyperspectral image (HSI) classification. However, the single-generation pattern of GANs tends to collapse for HSI sample generation, and the performance of the generator is limited. To address these issues, a multi-complementary GANs with contrastive learning (CMC-GAN) is proposed, which generates diverse multiscale samples by introducing multiple groups of GANs and a contrastive learning constraint, leading to superior classification performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Jie Chen, Licheng Jiao, Xu Liu, Fang Liu, Lingling Li, Shuyuan Yang
Summary: This article proposes a novel approach for modeling contextual relationships in images using a multiresolution interpretable contourlet graph network (MICGNet), which balances graph representation learning with the geometric features of images. Experimental analysis shows that MICGNet is significantly more effective and efficient than other recent algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geochemistry & Geophysics
Zhonghua Li, Biao Hou, Zitong Wu, Bo Ren, Zhongle Ren, Licheng Jiao
Summary: This article proposes a Gaussian OBB algorithm for object detection in aerial image scenes, which eliminates border shift and improves the performance and accuracy of the detector through synthesis and decoding methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaotong Li, Licheng Jiao, Hao Zhu, Zhongjian Huang, Fang Liu, Lingling Li, Puhua Chen, Shuyuan Yang
Summary: This study proposes a novel dynamic polar spatio-temporal encoding method to improve the tracking performance of visual Transformer models in video scenes. By utilizing spiral functions in polar space and a dynamic relative encoding mode for continuous frames, the method captures the spatio-temporal motion characteristics among video frames more effectively.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jing Bai, Junjie Ren, Zhu Xiao, Zheng Chen, Chengxi Gao, Talal Ahmed Ali Ali, Licheng Jiao
Summary: In recent years, there has been increasing attention on object localization and detection methods in remote sensing images (RSIs) due to their broad applications. Weakly supervised object localization (WSOL) is a cost-effective alternative to fully supervised methods as it only requires image-level labels instead of time-consuming and labor-intensive instance-level annotations. In this article, a self-directed weakly supervised strategy (SD-WSS) is proposed to perform WSOL in RSIs by enhancing the spatial feature extraction capability of the RSIs' classification model and utilizing GradCAM++ to address the discriminative region problem. A novel self-directed loss is also designed to eliminate interference from complex backgrounds. Additionally, new WSOL benchmarks in RSIs, named C45V2 and PN2, are created to evaluate the proposed method alongside six mainstream WSOL methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geochemistry & Geophysics
Zhonghua Li, Biao Hou, Zitong Wu, Zhengxi Guo, Bo Ren, Xianpeng Guo, Licheng Jiao
Summary: Traditional 2-D Gaussian distribution loses angular information when dealing with square-like objects, leading to inaccurate localization. To address this issue, this study modifies the 2-D Gaussian function using the Lame curve to create a super-Gaussian distribution. This distribution maintains angular information at arbitrary aspect ratios, and the distance between two super-Gaussian distributions is measured using KL divergence, converted into localization loss. Experimental results on multiple datasets confirm the effectiveness of the proposed algorithm, achieving state-of-the-art performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Yunpeng Li, Xiangrong Zhang, Xina Cheng, Xu Tang, Licheng Jiao
Summary: Tremendous progresses have been made in remote sensing image captioning (RSIC) task in recent years. This work focuses on injecting high-level visual-semantic interaction into RSIC model. The experiments on three benchmark data sets show the superiority of our approach compared with the reference methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)
