Article
Automation & Control Systems
Jiao Zhu, Sugen Chen, Yufei Liu, Cong Hu
Summary: This study proposes a novel energy-based structural least squares twin support vector clustering algorithm (ESLSTWSVC), which improves clustering performance and efficiency by introducing within-class covariance matrix and solving system of linear equations.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Computer Science, Artificial Intelligence
Barenya Bikash Hazarika, Deepak Gupta
Summary: This paper proposed two novel methods for class imbalance learning, which improve the model training efficiency through weighting and least squares principles, and carried out simulations on imbalanced datasets to compare model performance.
NEURAL PROCESSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
B. Richhariya, M. Tanveer
Summary: Universum based twin support vector machines use prior information about data distribution, leading to better generalization performance. However, in practice, data points may have varying importance, requiring the use of fuzzy membership functions. The proposed fuzzy universum least squares twin support vector machine (FULSTSVM) addresses this issue by providing weights based on membership values for both data samples and universum data, resulting in improved performance compared to existing algorithms.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Umesh Gupta, Deepak Gupta
Summary: This paper presents two efficient variant models to handle noise and outliers, obtaining solutions by solving a system of linear equations and minimizing the impact of noise. The proposed models demonstrate exceptional generalization performance.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xijiong Xie, Feixiang Sun, Jiangbo Qian, Lijun Guo, Rong Zhang, Xulun Ye, Zhijin Wang
Summary: In this paper, a novel semi-supervised learning method called Lap-LpLSTSVM is proposed, which utilizes Lp norm least squares twin support vector machine to handle classification problems. The method has the advantages of adjustable performance, efficient utilization of geometric information, and effective optimization, enabling the use of unlabeled data and handling of noisy datasets.
PATTERN RECOGNITION
(2023)
Article
Multidisciplinary Sciences
Ahmed Youssef Ali Amer
Summary: This study introduces GLocal-LS-SVM, a novel machine learning algorithm that combines localised and global learning to address challenges associated with decentralised data sources, large datasets, and input-space-related issues. The algorithm employs multiple local LS-SVM models to extract informative support vectors from each local region in the input space, which are then merged to train the global model. Experimental results demonstrate that GLocal-LS-SVM achieves comparable or superior classification performance compared to standard LS-SVM and state-of-the-art models, while also outperforming standard LS-SVM in terms of computational efficiency.
Article
Computer Science, Artificial Intelligence
Chengjiang Zhou, Hao Li, Jintao Yang, Qihua Yang, Limiao Yang, Shanyou He, Xuyi Yuan
Summary: In this paper, a recognition method based on fuzzy regular LSTSVM (FRLSTSVM) is proposed. L2 norm regular term is introduced to improve generalization performance, and support vector domain description (SVDD) is used to detect outliers. A membership degree S3 is constructed to reduce the impact of outliers on results. The method is also extended to a multi-classification model and combined with an improved multiscale fluctuating Rényi dispersion entropy (IMFRDE) for fault diagnosis.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Shuaiyong Li, Zhengxu Dai, Mingyang Liu, Mengqian Cai
Summary: This article proposes a water supply pipeline leak identification method based on the combination of compressed sensing (CS) theory and least squares twin support vector machine (LSTSVM), called CS-LSTSVM. The method reduces redundant information and volume of data through compressed sensing, extracts feature information, and identifies leaks through the LSTSVM model, achieving efficient and accurate leak identification in water supply pipelines.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Yanshan Xiao, Jinneng Liu, Kairun Wen, Bo Liu, Liang Zhao, Xiangjun Kong
Summary: This paper proposes a novel uncertain-data-based least squares twin support vector machine method (ULSTSVM) that can efficiently handle data with uncertain information. By introducing noise vectors to model the uncertain information and utilizing a two-step heuristic framework, ULSTSVM achieves better classification accuracy and higher training efficiency.
APPLIED INTELLIGENCE
(2023)
Article
Spectroscopy
Masoumeh Valaee, Mahmoud Reza Sohrabi, Fereshteh Motiee
Summary: In this study, two chemometrics methods, PLS and LS-SVM, were used to determine the content of zidovudine (ZDV) and lamivudine (LMV) in synthetic mixtures and anti-HIV pharmaceutical formulation. The results showed that both methods achieved accurate determination of the two components with good recovery rates. The comparison with HPLC as a reference technique demonstrated the reliability of the chemometrics approaches for routine analysis and quality control of the drug.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2023)
Article
Environmental Sciences
Barenya Bikash Hazarika, Deepak Gupta, Narayanan Natarajan
Summary: This study proposed wavelet kernel-based LSTSVR models for accurate wind speed prediction. The models were evaluated using data from four different stations in Tamil Nadu, India, and were found to outperform other models.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Hossein Moosaei, Milan Hladik
Summary: In this paper, a feature selection method based on least-squares twin multi-class support vector machine and cardinality-constrained optimization problem, called l(p)-norm least-squares twin multi-class support vector machine, is proposed. This method performs classification and feature selection simultaneously in multi-class classification. Experimental results show that the proposed method outperforms other methods in terms of classification accuracy and number of features in most datasets.
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Da Zhao, Yan-Ru Guo, Xiang-Yu Hua
Summary: Feature selection is crucial for solving high-dimensional regression problems by extracting relevant features containing useful information to improve learning performance. The sparse LSSVR based on L-p-norm offers an effective method for feature selection, avoiding singularity issues and ensuring convergence. Experimental results demonstrate the effectiveness of SLSSVR in both feature selection ability and regression performance.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Zheming Gao, Shu-Cherng Fang, Xuerui Gao, Jian Luo, Negash Medhin
Summary: This paper proposes a kernel-free least squares twin support vector machine model for multi-class classification, which utilizes a special fourth order polynomial surface and one-versus-all classification strategy, with l(2) regularization to accommodate various levels of nonlinearity in datasets. Theoretical analysis and computational results demonstrate the superior performance of the proposed model, particularly for imbalanced datasets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.