Article
Computer Science, Artificial Intelligence
Swarnajyoti Patra, Barnali Barman
Summary: A novel feature selection technique based on rough set theory is proposed in this work to reduce the dimensionality of hyperspectral images. The technique defines a new criterion by combining relevance and significance measures, and adopts a first order incremental search to select the most informative bands, showing better results compared to existing techniques. The proposed dependency measure definition is completely parameter free and computationally very cheap.
APPLIED SOFT COMPUTING
(2021)
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
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
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
Pei-Yi Hao, Jung-Hsien Chiang, Yu-De Chen
Summary: This paper proposes a novel possibilistic classification algorithm using support vector machines (SVMs) to effectively handle uncertain information and improve classification performance. The algorithm aims at finding a maximal-margin fuzzy hyperplane based on possibility theory and solves a fuzzy mathematical optimization problem. The proposed algorithm retains the advantages of fuzzy set theory and SVM theory, and it is more robust for handling outliers. Experimental results demonstrate the satisfactory generalization accuracy and ability to describe inherent vagueness in the given dataset.
Article
Environmental Sciences
Roya Kolachian, Bahram Saghafian
Summary: Prediction of drought severity class/state using standardized hydrological drought index (SHDI) was conducted in this study. Results showed that considering drought classes as inputs/outputs leads to more accurate predictions, with SHDI3 prediction being more accurate than SHDI1 prediction. Rough set theory (RST) showed slightly better accuracy than support vector classification (SVC) and support vector regression (SVR) in forecasting.
ENVIRONMENTAL EARTH SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Asuncion Jimenez-Cordero, Sebastian Maldonado
Summary: Functional Data Analysis (FDA) is important, but classifying hybrid functional data with both functional and static covariates is challenging. This paper proposes an embedded feature selection approach for SVM classification, optimizing bandwidths and SVM parameters to improve classification rates. The methodology outperformed 17 other approaches, demonstrating robustness through sensitivity analysis.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Shuangjie Li, Kaixiang Zhang, Yali Li, Shuqin Wang, Shaoqiang Zhang
Summary: Feature selection is crucial in many fields, especially in machine learning. The proposed method OFS-Gapknn effectively addresses the challenges of online streaming features by defining a new neighborhood rough set relation and analyzing relevance and redundancy features. Experimental results demonstrate the dominance and significance of this method.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Somaye Moslemnejad, Javad Hamidzadeh
Summary: This study proposed a novel weighted support vector machine to address the noisy sensitivity problem of standard support vector machine for multiclass data classification, by introducing entropy degree and using lower and upper approximation of membership function in fuzzy rough set theory.
Article
Computer Science, Information Systems
Jihong Wan, Hongmei Chen, Tianrui Li, Xiaoling Yang, Binbin Sang
Summary: Feature selection is a crucial data preprocessing approach in data mining, and the interaction between features and their dynamic changes should be taken into consideration to prevent the loss of useful information.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
D. Parvinnezhad, M. R. Delavar, B. C. Pijanowski, C. Claramunt
Summary: Land change models are essential tools for spatial decision support, with only a few incorporating fuzziness and roughness. This study applied a fuzzy-based approach to improve the accuracy of a land change model and found that models integrating FRST performed better. Among them, SVM-FRST and KLR-FRST showed the best goodness of fit measures for urban growth modeling in Tabriz, Iran.
EARTH SCIENCE INFORMATICS
(2021)
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
Automation & Control Systems
Hui-Ping Yin, Hai-Peng Ren
Summary: A symbol detection method based on genetic algorithm support vector machine is proposed to improve the bit error rate performance and simplify the symbol detection process in chaotic baseband wireless communication systems. By converting symbol decoding into a binary classification process, the proposed method outperforms traditional methods in terms of performance.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Mathematics, Applied
Mehwish Naushin, Asit Kumar Das, Janmenjoy Nayak, Danilo Pelusi
Summary: This article proposes a method for dealing with class imbalance using rough-fuzzy theory, which generates synthetic data and removes outliers. The experimental results demonstrate that the method achieves good results in both qualitative and quantitative data handling.
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
Engineering, Multidisciplinary
Chao Lin, Pengjun Wang, Xuehua Zhao, Huiling Chen
Summary: The Double Mutation Salp Swarm Algorithm (DMSSA) improves the stability and performance in solving optimization problems by incorporating a Cuckoo Mutation Strategy and an Adaptive DE Mutation Strategy. Comparisons and tests on benchmark functions demonstrate the superiority of DMSSA. Experiments on classical engineering design optimization problems further confirm its applicability and scalability.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Ying Chen, Huimin Gan, Huiling Chen, Yugang Zeng, Liang Xu, Ali Asghar Heidari, Xiaodong Zhu, Yuanning Liu
Summary: Iris segmentation algorithms based on deep learning lack generalization ability and cannot accurately segment iris images without corresponding ground truth data. Normalization is required to reduce the influence of pupil deformation, but it introduces noise in nonconnected iris regions and decreases recognition rate. This paper proposes an end-to-end unified framework based on deep learning that achieves improved accuracy in iris segmentation and recognition without normalization. The framework includes MADNet for iris segmentation and DSANet for iris recognition, and experiments show that it outperforms other methods on low-quality iris images without ground truth data.
Article
Engineering, Biomedical
Xiao Yang, Rui Wang, Dong Zhao, Fanhua Yu, Ali Asghar Heidari, Zhangze Xu, Huiling Chen, Abeer D. Algarni, Hela Elmannai, Suling Xu
Summary: Breast cancer is the most prevalent malignancy threatening human health, and early screening is crucial for improving treatment success and reducing mortality. Computer-aided technology plays a key role in the analysis and diagnosis of breast cancer real images, and high-quality medical segmentation images can enhance lesion area detection accuracy. This study proposed an enhanced differential evolution algorithm for threshold search, which accelerates convergence and reduces premature convergence. Experimental results showed that the proposed method outperformed other methods in breast cancer image segmentation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Meilin Zhang, Qianxi Wu, Huiling Chen, Ali Asghar Heidari, Zhennao Cai, Jiaren Li, Elsaid Md. Abdelrahim, Romany F. Mansour
Summary: This paper proposes an efficient and intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor classifier. The experimental results demonstrate that the model performs well in different test functions and achieves good results compared to other algorithms on the COVID-19 dataset. Therefore, RRWOA is an effectively improved optimizer.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Multidisciplinary
Chao Lin, Pengjun Wang, Ali Asghar Heidari, Xuehua Zhao, Huiling Chen
Summary: This paper proposes an improved algorithm named RCSSSA based on SSA, which enhances the convergence accuracy and speed by adding real-time update mechanism, communication strategy, and selective replacement strategy. Experimental results demonstrate that RCSSSA can converge faster and achieve better optimization results compared to traditional swarm intelligence and other improved algorithms.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Jiao Hu, Shushu Lv, Tao Zhou, Huiling Chen, Lei Xiao, Xiaoying Huang, Liangxing Wang, Peiliang Wu
Summary: Pulmonary hypertension is a global health problem. This study proposes a model combining the Whale Optimization Algorithm and Kernel Extreme Learning Machine to predict PH mouse models. The selected blood indicators are essential for identifying the models, and the method achieved 100% accuracy and specificity, showing great potential for evaluating and identifying PH mouse models.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Mathematical & Computational Biology
Yupeng Li, Dong Zhao, Zhangze Xu, Ali Asghar Heidari, Huiling Chen, Xinyu Jiang, Zhifang Liu, Mengmeng Wang, Qiongyan Zhou, Suling Xu
Summary: This study establishes a medical prediction model called bSRWPSO-FKNN based on the enhanced particle swarm optimization algorithm and the fuzzy K-nearest neighbor algorithm. It aims to improve the diagnosis of AD by using a dataset related to patients with AD.
FRONTIERS IN NEUROINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Songwei Zhao, Pengjun Wang, Ali Asghar Heidari, Xuehua Zhao, Huiling Chen
Summary: This paper presents a method for COVID-19 X-ray image recognition and segmentation based on an improved crow search algorithm. By introducing variable neighborhood descent and information exchange mutation strategies, a new algorithm (VMCSA) is proposed, which shows better performance in optimization. The proposed algorithm has significant advantages in segmentation results of COVID-19 images and exhibits better robustness compared to other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xiao Yang, Rui Wang, Dong Zhao, Fanhua Yu, Chunyu Huang, Ali Asghar Heidari, Zhennao Cai, Sami Bourouis, Abeer D. Algarni, Huiling Chen
Summary: The sine cosine algorithm (SCA) is a well-known optimization algorithm that has gained attention for its simple structure and excellent optimization capabilities. To overcome the limitations of the original SCA, a modified variant called ARSCA is proposed, which incorporates adaptive quadratic interpolation mechanism and rounding mechanism. Experimental results demonstrate that ARSCA outperforms its competitors in terms of solution quality and ability to escape local optima.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Biomedical
Jiaochen Chen, Zhennao Cai, Ali Asghar Heidari, Huiling Chen, Qiuxiang He, Jose Escorcia-Gutierrez, Romany F. Mansour
Summary: The scholarly world has shown great interest in medical image segmentation due to its complex nature and important role in medical diagnosis and treatment systems. Multi-threshold image segmentation (MTIS) is a popular technique for this purpose, known for its simplicity and straightforwardness. This paper introduces an improved Differential Evolution (DE) algorithm called AGDE, based on MTIS, which was used to evaluate its high performance at IEEE CEC 2017. Experimental results showed that the proposed image segmentation method outperformed its competitors, making it a promising approach for medical image segmentation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Mathematics, Interdisciplinary Applications
Hui Yang, Chunmei Zhang, Ran Li, Huiling Chen
Summary: This paper focuses on the equilibrium problem of an urban public transportation system with time delay. By combining graph theory and the Lyapunov method, the global Lyapunov function is constructed, and the response system can synchronize with the drive system under the adaptive controller.
FRACTAL AND FRACTIONAL
(2023)
Article
Engineering, Multidisciplinary
Li Yuan, Jianping Ji, Xuegong Liu, Tong Liu, Huiling Chen, Deng Chen
Summary: This paper focuses on the improvement and mitigation of the stagnation problems of Slime mould algorithm (SMA) through adjusting its structure and incorporating elite strategy and chaotic stochastic strategy. The experimental results validate the significant enhancing effect of both mechanisms on SMA and its excellent performance in four structural design issues.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Erhui Shang, Huiling Chen, Miaomiao He, Xinlai Zhou, Dongju Chen
Summary: In this paper, a Turing membrane with turning structure was prepared using polybenzimidazole (PBI) as raw material and coordination induced phase inversion method. The morphology of the Turing membrane was controlled by changing the content of polymer. The nanofiltration performance and stability in organic solvents of the Turing membrane were investigated.
CHEMICAL JOURNAL OF CHINESE UNIVERSITIES-CHINESE
(2023)
Article
Engineering, Multidisciplinary
Abdelazim G. Hussien, Guoxi Liang, Huiling Chen, Haiping Lin
Summary: Many real-world complex optimization problems can easily get stuck in local optima and fail to find the optimal solution, so new techniques and methods are needed to address these challenges. Metaheuristic algorithms, such as the Sine Cosine Algorithm (SCA), have gained attention due to their efficiency and simplicity. However, SCA, like other metaheuristic algorithms, has slow convergence and may struggle in sub-optimal regions. This study proposes an enhanced version of SCA called RDSCA that utilizes random spare/replacement and double adaptive weight techniques, resulting in competitive results compared to other metaheuristic algorithms.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Automation & Control Systems
Chunmei Zhang, Huiling Chen, Qin Xu, Yuli Feng, Ran Li
Summary: This article discusses a class of stochastic hybrid delayed coupled systems with multiple weights, and derives several conditions for asymptotic synchronization and topology identification of the systems based on Kirchhoff's Matrix-Tree Theorem and Lyapunov stability theory.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
