Article
Computer Science, Artificial Intelligence
Bhagat Singh Raghuwanshi, Akansha Mangal, Sanyam Shukla
Summary: Imbalanced classification is a challenging problem in machine learning and data mining. This paper proposes a novel hybrid framework that combines Universum learning with WELM to achieve better performance in handling class imbalance.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Carlos Perales-Gonzalez, Francisco Fernandez-Navarro, Javier Perez-Rodriguez, Mariano Carbonero-Ruz
Summary: The paper introduces a novel ELM architecture, called NCHL-ELM, which improves the performance of ELM models by introducing parameters into nodes and optimizing them to reduce errors in the training set. The method promotes diversity among parameters to enhance the generalization results.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics, Applied
Naxian Ni, Suchuan Dong
Summary: This paper introduces a modified ELM method, called HLConcELM, which can produce highly accurate solutions to linear/nonlinear PDEs when the last hidden layer of the network is narrow and when it is wide. The method is based on a modified feedforward neural network (FNN), termed HLConcFNN, which incorporates a logical concatenation of the hidden layers in the network and exposes all the hidden nodes to the output-layer nodes. The HLConcFNNs have the interesting property that the representation capacity of the network is guaranteed to be not smaller than that of the original network architecture, even with additional hidden layers or nodes.
JOURNAL OF SCIENTIFIC COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yinan Guo, Botao Jiao, Ying Tan, Pei Zhang, Fengzhen Tang
Summary: This paper proposes a transfer weighted extreme learning machine (TWELM) classifier to address the issue of limited labeled instances and poor generalization. The TWELM classifier extracts knowledge from other domains and combines it with limited labeled target domain data to improve the classification performance. Experimental results show that TWELM outperforms existing algorithms in terms of classification accuracy and computation cost.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Bhagat Singh Raghuwanshi, Sanyam Shukla
Summary: This paper introduces a new variant of extreme learning machine, MCVCSELM, for effectively addressing binary class imbalance problems by utilizing minimum class variance and class-specific regularization. Experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art methods for imbalanced learning.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jiongming Qin, Cong Wang, Qinhong Zou, Yubin Sun, Bin Chen
Summary: Active learning combined with Extreme Learning Machine can reduce the cost of labeling instances and improve learning efficiency. The proposed AI-WSELM framework in this paper effectively handles multiclass imbalanced data and stream-based data, showing satisfactory performance compared to existing models.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Shahan Yamin Siddiqui, Muhammad Adnan Khan, Sagheer Abbas, Farrukh Khan
Summary: This paper focuses on predicting parking locations using deep extreme learning machine (DELM). The approach enhances familiarity and safety in traffic, reducing parking turbulence. Experimental results demonstrate the effectiveness and accuracy of the proposed DELM method.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xiaopeng Zhang, Liangxi Qin
Summary: In this paper, a new extreme learning machine algorithm called OWA-ELM is proposed to improve the classification performance of imbalanced data. Experimental results show that the OWA-ELM algorithm achieves better results in dealing with imbalanced data classification.
Article
Computer Science, Artificial Intelligence
Xinli Wang, Juan Gong, Yan Song, Jianhua Hu
Summary: In this paper, a new improved oversampling method called adaptively weighted three-way decision oversampling (AWTDO) is proposed for imbalanced learning. The method involves removing noise samples, clustering, categorizing clusters based on imbalance ratios, and generating synthetic samples accordingly. Experimental results show that AWTDO outperforms other methods.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Hardware & Architecture
Fan Wu, Si Hong, Wei Zhao, Xiaoyan Wang, Xun Shao, Xiujun Wang, Xiao Zheng
Summary: This study proposed a pseudo-double hidden layer feedforward neural network model for predicting actual bike-sharing demands. An improved extreme learning machine algorithm was designed to overcome limitations in traditional back-propagation learning process. The performance was verified by comparing with other learning algorithms on a dataset from Streeter Dr bike-sharing station in Chicago.
MOBILE NETWORKS & APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
Moming Duan, Duo Liu, Xianzhang Chen, Renping Liu, Yujuan Tan, Liang Liang
Summary: Federated Learning (FL) is a distributed deep learning method where multiple devices contribute to a neural network training while keeping their data private. Data imbalance in mobile systems can lead to accuracy degradation in FL applications, but the Astraea framework offers improvements through data augmentation and rescheduling. Compared to FedAvg, Astraea demonstrates higher accuracy and reduced communication traffic.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Meejoung Kim
Summary: This paper introduces the Generalized Extreme Learning Machine (GELM) which incorporates analyzed hyperparameters and a limiting approach for the Moore-Penrose generalized inverse (M-P GI) into the learning process. Experimental results show the advantages of GELM in prediction performance and learning speed.
Article
Computer Science, Information Systems
Md. Omaer Faruq Goni, Md. Nazrul Islam Mondal, S. M. Riazul Islam, Md. Nahiduzzaman, Md. Robiul Islam, Md. Shamim Anower, Kyung-Sup Kwak
Summary: Malaria, a worldwide life-threatening disease, requires a reliable and fast early prognosis infrastructure. This paper proposes an unorthodox method based on an extreme learning machine (ELM) algorithm to detect malaria. The proposed CNN-DELM model achieves high accuracy and robustness in detecting malaria, outperforming traditional methods and producing comparable or better results.
Article
Computer Science, Artificial Intelligence
Yanjiao Li, Jie Zhang, Sen Zhang, Wendong Xiao, Zhiqiang Zhang
Summary: This paper presents a multi-objective optimization-based adaptive class-specific cost extreme learning machine (MOAC-ELM) method for imbalanced classification problems. By considering the costs of different classes and enhancing the representation of the minority class using penalty factors, the class-specific costs are automatically determined. The proposed MOAC-ELM shows good robustness and generalization performance in imbalanced classification tasks, as demonstrated by comprehensive experiments.
Article
Automation & Control Systems
Guanjin Wang, Kok Wai Wong, Jie Lu
Summary: In this article, AUC-ELM and SAUC-ELM models were proposed to address imbalanced binary classification tasks by integrating AUC maximization into the ELM framework. These two models showed superior performance in classification and training speed compared to other methods in experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Optics
Dongshun Cui, Guanghao Zhang, Kai Hu, Wei Han, Guang-Bin Huang
OPTICS AND LASER TECHNOLOGY
(2019)
Article
Automation & Control Systems
Chenwei Deng, Shuigen Wang, Zhen Li, Guang-Bin Huang, Weisi Lin
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2019)
Editorial Material
Computer Science, Artificial Intelligence
Guang-Bin Huang, Jonathan Wu, Donald C. Wunsch
Article
Computer Science, Artificial Intelligence
Tianchi Liu, Chamara Kasun Liyanaarachchi Lekamalage, Guang-Bin Huang, Zhiping Lin
Article
Computer Science, Artificial Intelligence
Dongshun Cui, Guang-Bin Huang, Tianchi Liu
PATTERN RECOGNITION
(2018)
Article
Automation & Control Systems
Lei Zhang, Xuehan Wang, Guang-Bin Huang, Tao Liu, Xiaoheng Tan
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Editorial Material
Engineering, Electrical & Electronic
Osvaldo Simeone, Bipin Rajendran, Andre Gruning, Evangelos S. Eleftheriou, Mike Davies, Sophie Deneve, Guang-Bin Huang
IEEE SIGNAL PROCESSING MAGAZINE
(2019)
Article
Automation & Control Systems
Yue Li, Chamara Kasun Liyanaarachchi Lekamalage, Tianchi Liu, Pin-An Chen, Guang-Bin Huang
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2020)
Article
Computer Science, Artificial Intelligence
Lei Zhang, Shanshan Wang, Guang-Bin Huang, Wangmeng Zuo, Jian Yang, David Zhang
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Automation & Control Systems
Chenwei Deng, Shuigen Wang, Alan C. Bovik, Guang-Bin Huang, Baojun Zhao
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Automation & Control Systems
Yijie Zeng, Jichao Chen, Guang-Bin Huang
Summary: The article introduces a novel online CDL algorithm based on a local, slice-based representation that is efficient in handling large datasets and achieving superior performance. Theoretical analysis and experiments demonstrate that the algorithm has lower time complexity and better reconstruction objectives compared to existing methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Civil
Xinggan Peng, Rongzihan Song, Qi Cao, Yue Li, Dongshun Cui, Xiaofan Jia, Zhiping Lin, Guang-Bin Huang
Summary: This paper proposes a novel detection algorithm using in-vehicle cameras for illegal parking detection. A new dataset and a labeling method are also introduced. The experiments show that the proposed algorithm has strong illumination robustness in different operating environments.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Biomedical
Xiaofan Jia, Sadeed Bin Sayed, Nahian Ibn Hasan, Luis J. Gomez, Guang-Bin Huang, Abdulkadir C. Yucel
Summary: This paper proposes a deep learning-based emulator called DeeptDCS for rapidly evaluating the current density induced by transcranial direct current stimulation (tDCS). The emulator utilizes Attention U-net model to generate the three-dimensional current density distribution across the entire head based on the volume conductor models of head tissues. By fine-tuning the model, the generalization ability of DeeptDCS to non-trained electrode configurations can be greatly enhanced. DeeptDCS provides satisfactorily accurate results and is significantly faster than a physics-based open-source simulator.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Proceedings Paper
Automation & Control Systems
Xinggan Peng, Huiping Zhuang, Guang-Bin Huang, Haizhou Li, Zhiping Lin
16TH IEEE INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2020)
(2020)
Article
Engineering, Electrical & Electronic
Tinghui Ouyang, Chongwu Wang, Zhangjun Yu, Robert Stach, Boris Mizaikoff, Guang-Bin Huang, Qi-Jie Wang
Summary: This study suggests utilizing spectroscopic gas sensing methods and a deep learning network algorithm to measure NOx concentrations in sustainable developments, showing the effectiveness of the approach in emission monitoring.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(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.
