Review
Computer Science, Information Systems
Nancy Kumari, D. P. Acharjya
Summary: Digitization is important in improving people's physical health, and healthcare organizations are focusing on precise disease diagnosis and patient care to enhance living standards. However, the vast amount of data generated through digitization contains uncertainties and impreciseness, requiring the application of computational intelligence techniques for analysis and processing.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Zhihong Wang, Hongmei Chen, Xiaoling Yang, Jihong Wan, Tianrui Li, Chuan Luo
Summary: Dimensionality reduction is an important step in many learning methods to achieve optimal performance using discriminative features. This study proposes a fuzzy rough dimensionality reduction method that combines feature selection and feature extraction, and compares its performance with other algorithms, showing higher classification performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Jindong Feng, Zengtai Gong
Summary: In this study, a hybrid feature selection model combining neighborhood rough set with an improved particle swarm optimization is proposed. Experimental results demonstrate the model's stronger classification ability and its ability to remove more redundant features in most datasets.
Article
Computer Science, Software Engineering
Nancy Kumari, Debi Prasanna Acharjya
Summary: This paper introduces a decision support system that integrates rough set and artificial fish swarm optimization to handle uncertainties in healthcare data analysis. By applying the proposed model to hepatitis B disease, the results show that it achieves a high accuracy and outperforms other classical models.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Biology
Dongwan Lu, Yinggao Yue, Zhongyi Hu, Minghai Xu, Yinsheng Tong, Hanjie Ma
Summary: In this paper, a fuzzy k-nearest neighbor method based on the improved binary salp swarm algorithm (IBSSA-FKNN) is proposed for the early diagnosis of Alzheimer's disease (AD). The method is validated on multiple benchmark datasets and effectively distinguishes between patients with mild cognitive impairment (MCI), AD, and normal controls (NC).
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Mathematics, Interdisciplinary Applications
Bingsheng Chen, Huijie Chen, Mengshan Li
Summary: Swarm intelligence algorithm simulates animal behavior, while feature selection is an effective data processing method that has received extensive attention in various fields.
Article
Computer Science, Artificial Intelligence
Shuyin Xia, Cheng Wang, Guoyin Wang, Xinbo Gao, Weiping Ding, Jianhang Yu, Yujia Zhai, Zizhong Chen
Summary: This article introduces a granular-ball rough set (GBRS) model based on granular-ball computing, which can process continuous data and use equivalence classes for knowledge representation. Experimental results demonstrate that GBRS outperforms traditional rough set models in terms of learning accuracy and feature selection.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Ahmad Taher Azar, Mustafa Samy Elgendy, Mustafa Abdul Salam, Khaled M. Fouad
Summary: This paper presents a novel hybrid strategy based on particle swarm optimization and the Mayfly algorithm for dimensionality reduction of big data sets. Evaluation on six different datasets demonstrates the effectiveness and superiority of the proposed approach.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Water Resources
Chaode Yan, Ziwei Li, Muhammad Waseem Boota, Muhammad Zohaib, Xiao Liu, Chunlong Shi, Jikun Xu
Summary: This study focuses on the discrimination of river patterns in the Yellow River using Rough Set theory. A hierarchical structure integrating the boundary and the interior was proposed to describe the morphological feature of river patterns. The main feature factors were selected using Rough Set theory, and river pattern discriminant rules were generated based on the reduced feature subsets. The results demonstrate good performance in expressing the morphological features of different river patterns.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Chemistry, Analytical
Miodrag Zivkovic, Catalin Stoean, Amit Chhabra, Nebojsa Budimirovic, Aleksandar Petrovic, Nebojsa Bacanin
Summary: This study proposes a feature selection method based on swarm intelligence paradigm, which extracts the most important attributes from multiple datasets. By combining machine learning with metaheuristic approaches, feature selection is improved to enhance classification accuracy.
Article
Computer Science, Artificial Intelligence
Keyu Liu, Tianrui Li, Xibei Yang, Xin Yang, Dun Liu
Summary: This paper introduces a novel ensemble feature selection method, which selects features with local significance through cross-class sample granulation and ensemble feature selection strategies.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Jiao Hu, Wenyong Gui, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen, Zhifang Pan
Summary: The dispersed foraging slime mould algorithm (DFSMA) is proposed as an enhanced version of the slime mould algorithm (SMA) to address the limitations of SMA in solving multimodal and hybrid functions. Experimental results demonstrate that DFSMA outperforms other algorithms in terms of convergence speed and accuracy. Furthermore, the binary DFSMA (BDFSMA) is evaluated and found to have improved performance in classification accuracy and feature selection compared to other optimization algorithms.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ramesh Kumar Huda, Haider Banka
Summary: Feature selection is the process of selecting criterion functions and search strategies to find the best feature subset from a large number of subsets. Algorithms based on particle swarm optimization and fuzzy rough fitness function can effectively select optimal feature subsets from datasets with numerous features.
Article
Computer Science, Artificial Intelligence
Jihong Wan, Hongmei Chen, Tianrui Li, Binbin Sang, Zhong Yuan
Summary: This study proposes a method for feature selection in data with uncertainty, fuzziness, and noise. A robust fuzzy rough set model is constructed to enhance the robustness and antinoise ability. Uncertainty measures are defined to analyze the interactivity and redundancy of features. Experimental results demonstrate the significance of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shuyin Xia, Xinyu Bai, Guoyin Wang, Yunlong Cheng, Deyu Meng, Xinbo Gao, Yujia Zhai, Elisabeth Giem
Summary: This paper presents a strong data-mining method based on rough set, which achieves feature selection, classification, and knowledge representation simultaneously. It addresses the efficiency issue by discovering the stability of local redundancy and proposes a theorem to prove it. Furthermore, it solves the accuracy issue by introducing relative importance as a robust measurement for attribute selection. Experimental results demonstrate its superiority over seven state-of-the-art feature-selection methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Yumin Chen, Duoqian Miao
INFORMATION SCIENCES
(2020)
Article
Computer Science, Theory & Methods
Yumin Chen, Shunzhi Zhu, Wei Li, Nan Qin
Summary: The study proposes a fuzzy granular convolutional classifier, which extracts features and optimizes weights through fuzzy granulation and convolutional operations, ultimately achieving better classification performance.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yumin Chen, Zhiwen Cai, Lei Shi, Wei Li
Summary: Sparse learning is crucial in dealing with uncertain data, leading to the proposal of a Fuzzy Granular Sparse Learning (FGSL) model for identifying antigenic variants of influenza viruses. The FGSL model, utilizing fuzzy set theory and constraint norms, shows fast convergence and strong feature selection ability, successfully identifying antigenic variants with low RMSE.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Multidisciplinary
Wei Li, Xiaoyu Ma, Yumin Chen, Bin Dai, Runjing Chen, Chao Tang, Youmeng Luo, Kaiqiang Zhang
Summary: This study approaches the classification problem from the perspective of granular computing, transforming it into the fuzzy granular space. By developing an adaptive global random clustering algorithm and a parallel model for data granulation, the efficiency of handling nonnumerical data is greatly enhanced. The random fuzzy granular decision tree (RFGDT) algorithm is designed to classify fuzzy granules, selecting important features and avoiding overfitting, ultimately proving to be efficient and accurate in solving classification problems.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jianwei Dong, Yumin Chen, Bingyu Yao, Xiao Zhang, Nianfeng Zeng
Summary: The paper discusses the characteristics of boosting models and neural networks, and proposes a Neural Network Boosting (NNBoost) regression model that uses shallow neural networks as weak classifiers. The model approximates the target loss function using the Taylor expansion and applies a gradient descent algorithm for optimization. It addresses issues in deep learning such as gradient vanishing and overfitting, and achieves improved regression accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Junwen Lu, Hao Yongsheng, Kesou Wua, Yuming Chen, Qin Wang
Summary: Mobile cloud computing provides rich computational resources for mobile users, network operators, and cloud computing providers. Offloading applications to remote cloud resources can save energy in a dynamic mobile cloud computing environment. Our proposed algorithm outperforms other methods in energy consumption reduction and number of finished jobs.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hailiang Jiang, Yumin Chen, Liru Kong, Guoqiang Cai, Hongbo Jiang
Summary: Learning Vector Quantization (LVQ) is a clustering method with supervised information, simple structures, and powerful functions. This article proposes a granular LVQ clustering algorithm, which overcomes the sensitivity to initial values of LVQ by adopting the neighborhood granulation technology and LVQ, and is tested on multiple UCI data sets to demonstrate its effectiveness.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Mathematics
Linjie He, Yumin Chen, Caiming Zhong, Keshou Wu
Summary: The study proposes a granule-based elastic network regression model to address the problem of handling uncertain data in traditional linear regression models. Experimental results show that the granular elasticity network has the advantage of a good fit.
Article
Computer Science, Artificial Intelligence
Xiao Zhang, Yumin Chen, Linjie He
Summary: Sentiment analysis is a vital task in natural language processing. Existing models for sentiment analysis, such as LSTM sequence models and attention mechanism, have flaws including the loss of information in long sequences, gradient explosion, and information redundancy. To address these issues and improve performance, a model combining multi-head attention and LSTM network is proposed.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Sihong Lin, Kunbin Zhang, Dun Guan, Linjie He, Yumin Chen
Summary: Intrusion detection systems have become an important tool for network security due to frequent attacks caused by the rapid growth of network traffic. Autoencoder, a neural network model, is effective in intrusion detection but suffers from overfitting and gradient disappearance issues. To solve these problems, a novel autoencoder called Granular AutoEncoders (GAE) is proposed, which constructs a granulation reference set using random sampling and granulates training samples based on single-feature similarity. The GAE is further applied to intrusion detection by designing an anomaly detection algorithm. Experimental results validate the correctness and effectiveness of the GAE-based intrusion detection method, showing its superiority over correlation algorithms in detecting anomalies.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yumin Chen, Xiao Zhang, Zhuang Ying, Bingyu Yao, Bin Lin
Summary: The neural network has good nonlinear fitting ability, but suffers from complexity, high iterations, and long training time. To address these issues, we propose a new neural network model called the Granular Neural network Classifier (GNC), which introduces the concept of granulation and related operations. GNC achieves similar accuracy to traditional multi-layer neural networks with fewer hidden layers, and solves the problem of gradient disappearance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Mathematics
Qiangqiang Chen, Linjie He, Yanan Diao, Kunbin Zhang, Guoru Zhao, Yumin Chen
Summary: The most popular algorithms in unsupervised learning are clustering algorithms, which group samples into classes or clusters based on their distances. Traditional clustering algorithms struggle with set-based and uncertain nonlinear data. In this study, we propose the granular vectors relative distance and granular vectors absolute distance based on neighborhood granule operation. We also introduce the neighborhood granular meanshift clustering algorithm, which outperforms traditional clustering algorithms like Kmeans and Gaussian Mixture.
Article
Computer Science, Artificial Intelligence
Xingyu Fu, Yingyue Chen, Jingru Yan, Yumin Chen, Feng Xu
Summary: The random forest, a widely used ensemble learning method, has universal applicability but struggles with uncertain data and thus produces poor classification results. To address this issue, a broad granular random forest algorithm is proposed by studying granular computing theory and breadth concepts. The algorithm granulates the relationships between features, defines operation rules for granular vectors, and introduces the granular decision tree model. The final result is obtained through a multiple granular decision tree voting method, yielding better classification performance compared to the traditional random forest algorithm.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Engineering, Biomedical
Yanan Diao, Qiangqiang Chen, Yan Liu, Linjie He, Yue Sun, Xiangxin Li, Yumin Chen, Guanglin Li, Guoru Zhao
Summary: This study proposes a modified fuzzy granularized logistic regression (FG_LogR) algorithm to improve the accuracy of cross-individual gesture recognition. It has the potential for clinical application.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yingyue Chen, Yumin Chen
Summary: By introducing neighborhood rough set model and variable precision neighborhood rough set model, this paper addresses the issues of handling real-value data and weak fault tolerance in traditional rough sets. The proposed method enhances the fault-tolerant ability of classification systems and designs an algorithm to select feature subsets. Experimental results demonstrate the effectiveness and compactness of the feature subset selection.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
