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
Engineering, Biomedical
Itzel Aranguren, Arturo Valdivia, Bernardo Morales-Castaneda, Diego Oliva, Mohamed Abd Elaziz, Marco Perez-Cisneros
Summary: This article presents a multilevel thresholding approach based on the LSHADE method for the segmentation of magnetic resonance brain imaging. The proposed method has been tested using three groups of reference images and the statistically verified results demonstrate that the suggested approach improves consistency and segmentation quality. Thus, LSHADE proves to be an efficient metaheuristic algorithm for solving numerical optimization problems in image analysis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Thippa Reddy Gadekallu, Mamoun Alazab, Rajesh Kaluri, Praveen Kumar Reddy Maddikunta, Sweta Bhattacharya, Kuruva Lakshmanna, M. Parimala
Summary: The study implements a crow search-based convolutional neural network model for gesture recognition in the HCI domain, achieving impressive results. By converting data using one-hot encoding and selecting optimal hyper-parameters with CSA, the model is optimized to enhance accuracy in classifying hand gestures, ultimately achieving 100% training and testing accuracy.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jafar Gholami, Farhad Mardukhi, Hossam M. Zawbaa
Summary: Meta-heuristic algorithms, such as the crow search algorithm (CSA), have shown promising results in solving optimization problems, but often suffer from issues such as local optima and premature convergence. This paper introduces an improved version, ICSA, which utilizes a new update mechanism to enhance convergence and local search ability. Experimental results demonstrate that ICSA outperforms traditional CSA and other meta-heuristic algorithms in terms of solution accuracy and efficiency.
Article
Engineering, Multidisciplinary
Weifeng Shan, Hanyu Hu, Zhennao Cai, Huiling Chen, Haijun Liu, Maofa Wang, Yuntian Teng
Summary: This paper proposes an improved Crow Search Algorithm (CCMSCSA) by incorporating mutation and crisscross strategies, which enhances convergence speed and the ability to escape local optima. Experimental results demonstrate that CCMSCSA performs well on optimization problems, being effective for both constrained and unconstrained problems.
JOURNAL OF BIONIC ENGINEERING
(2022)
Article
Construction & Building Technology
Yukai Ke, Jun Xie, Somayeh Pouramini
Summary: In this study, an Improved Crow Search Algorithm (ICSA) combined with EnergyPlus simulation tool was used to optimize office buildings in four cities in Australia, resulting in over 11.8% reduction in energy consumption through energy-saving measures. Comparisons with benchmark algorithms showed that ICSA provides more suitable solutions with shorter calculation time.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yassine Meraihi, Asma Benmessaoud Gabis, Amar Ramdane-Cherif, Dalila Acheli
Summary: The Crow Search Algorithm (CSA) is a recent swarm intelligence optimization algorithm inspired by the social intelligent behavior of crows. It has been widely used in various fields and areas of research, proving its efficiency compared to other state-of-the-art optimization algorithms. In addition to providing an overview of CSA and its variants, the paper also discusses its applications in feature selection, image processing, scheduling, economic dispatch, distributed generation, and other engineering problems, as well as suggesting interesting research areas for CSA enhancement, hybridization, and new applications.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Automation & Control Systems
Ghaleb H. Al-Gaphari, Rowaida Al-Amry, Afrah S. Al-Nuzaili
Summary: This paper introduces three discrete crow-inspired algorithms for improving the performance of the crow search algorithm when solving discrete traveling salesman problems. These algorithms, based on modular arithmetic, basic operators, and dissimilar solutions techniques, ensure a seamless transition from continuous spaces to discrete spaces without losing information. The simulation results show that these proposed algorithms outperform the state-of-the-art discrete optimizers in terms of optimal solution accuracy, errors from optimal solutions, and computational time.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Engineering, Multidisciplinary
Jiaochen Chen, Zhennao Cai, Huiling Chen, Xiaowei Chen, Jose Escorcia-Gutierrez, Romany F. Mansour, Mahmoud Ragab
Summary: This research proposes a 2D Renyi entropy multi-threshold image segmentation method based on an improved Cuckoo Search algorithm to assist pathologists in evaluating histopathological images of Lupus Nephritis. Experimental results show that the proposed method performs well in image segmentation experiments.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Teena Johnson, Tukaram Moger
Summary: Precise power system monitoring focuses on the latest technology based on phasor measurement units (PMUs). The state estimator plays a crucial role in ensuring the security of power system operations. Optimizing the placement of PMUs in the power system network is necessary for economical and efficient utilization. A new method called Crow Search Algorithm (CSA) is compared with the dominant method of binary integer linear programming (BILP) for solving the optimal placement problem of PMUs. The CSA provides multiple location sets for the optimal number of PMUs, which is advantageous for power engineers in the planning stage.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Omar Avalos, Eduardo H. Haro, Octavio Camarena, Primitivo Diaz
Summary: The enhancement of manufacturing processes due to growing demand requires constant updates. Several schemas have been developed to improve manufacturing processes, involving the flexible use of machines and tools to determine the best process planning for specific pieces. Bioinspired optimization techniques, such as the crow search algorithm, have been employed to solve complex engineering problems. Our work applies an improved crow search algorithm to determine optimal flexible process planning with low computational effort, outperforming other approaches in terms of performance and accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Interdisciplinary Applications
Yundi Rao, Dengxu He, Liangdong Qu
Summary: The Crow Search Algorithm is a new meta-heuristic optimizer inspired by the intelligent behavior of crows, and it has great potential for applications in engineering. This paper introduces a hybrid algorithm called PSCCSA, which combines the Crow Search Algorithm with a probability simplified sine cosine algorithm to overcome the limitations of blind location updates in CSA. The results of comparing the proposed algorithm with five other meta-heuristic algorithms and applying it to four classic engineering problems demonstrate its feasibility and effectiveness.
ENGINEERING WITH COMPUTERS
(2023)
Article
Engineering, Multidisciplinary
Yuqi Fan, Huimin Yang, Yaping Wang, Zunshan Xu, Daoxiang Lu
Summary: In this paper, a Variable Step Crow Search Algorithm (VSCSA) is proposed to enhance the searching abilities of the Crow Search Algorithm (CSA) by using the cosine function. Experimental results demonstrate that VSCSA performs well in fitness values, iteration curves, box plots, searching paths, and the Wilcoxon test results.
Article
Computer Science, Information Systems
Li Cao, Yinggao Yue, Yong Zhang, Yong Cai
Summary: The study introduces an improved crow search algorithm to optimize the extreme learning machine, enhancing global search capability and gradually reducing search trajectory amplitude to avoid being attracted by local extremum, ultimately optimizing hidden layer neurons and connection weights for accurate prediction results.
Article
Computer Science, Artificial Intelligence
Kambiz Gholami, Hassan Olfat, Jafar Gholami
Summary: The study proposed a new hybrid algorithm HJCSA by combining JAYA algorithm with CSA algorithm to enhance performance, through evaluation on 20 benchmark functions and comparison with other algorithms, it is found that the optimization algorithm has advantages in better convergence and discovering more accurate solutions.
Article
Operations Research & Management Science
Angel A. Juan, Peter Keenan, Rafael Marti, Sean McGarraghy, Javier Panadero, Paula Carroll, Diego Oliva
Summary: In the context of simulation-based optimization, this paper reviews recent work related to metaheuristics, matheuristics, simheuristics, biased-randomised heuristics, and learnheuristics for solving complex and large-scale optimization problems in various domains. The paper provides an overview of the main concepts and updated references, and highlights the applications of these hybrid optimization-simulation-learning approaches in solving real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits across different application fields. The paper concludes by highlighting open research lines on extending the concept of simulation-based optimization.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Eduardo H. Haro, Omar Avalos, Octavio Camarena, Erik Cuevas
Summary: The increasing demand for products and services due to globalization has highlighted the importance of improving manufacturing processes. Flexible process planning (FPP) has been treated as an optimization problem in the context of distributed manufacturing. In this study, a genetic algorithm is employed for an accurate FPP process, achieving competitive results.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Angel Casas-Ordaz, Diego Oliva, Mario A. Navarro, Alfonso Ramos-Michel, Marco Perez-Cisneros
Summary: This article introduces a method for determining the threshold values for image segmentation using the Runge Kutta (RUN) optimization algorithm. By combining it with opposition-based learning (OBL), a hybrid algorithm called RUN-OBL is created, which can effectively solve high-dimensional problems. Experimental results demonstrate that the proposed approach performs better in terms of image segmentation and optimization of complex problems.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Simrandeep Singh, Nitin Mittal, Harbinder Singh, Diego Oliva
Summary: Image segmentation is a critical stage in image analysis and pre-processing, where pixels are divided into segments based on threshold values. Multi-level thresholding approaches are more effective than bi-level methods, and a new modified Otsu function is proposed that combines Otsu's between-class variance and Kapur's entropy. Experimental results demonstrate the high efficiency of the modified Otsu method in terms of performance metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Diego Oliva, Noe Ortega-Sanchez, Mario A. Navarro, Alfonso Ramos-Michel, Mohammed El-Abd, Seyed Jalaleddin Mousavirad, Mohammad H. Nadimi-Shahraki
Summary: This paper proposes a combination of the minimum cross-entropy method and the Global-best brain storm optimization algorithm for image segmentation. The method aims to find the best configuration of thresholds by optimizing the minimum cross entropy, and extract regions of interest.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Ehsan Bojnordi, Seyed Jalaleddin Mousavirad, Mahdi Pedram, Gerald Schaefer, Diego Oliva
Summary: This paper presents a novel MLP training algorithm based on Levy flight distribution, which uses random walks to explore the search space and optimize the performance of neural networks in pattern classification tasks. Experimental results show the superiority of the proposed algorithm compared to other methods.
NEW GENERATION COMPUTING
(2023)
Article
Agricultural Engineering
Parijata Majumdar, Diptendu Bhattacharya, Sanjoy Mitra, Ryan Solgi, Diego Oliva, Bharat Bhusan
Summary: This paper uses XGBoost and BayGA-RF algorithms to predict the irrigation water demand and suitable fertilizer selection for different growth stages of rice. The results show that this method outperforms other methods in terms of prediction accuracy and achieves an accuracy of 98% in predicting suitable fertilizer selection.
PADDY AND WATER ENVIRONMENT
(2023)
Correction
Engineering, Multidisciplinary
Mohammad Sh. Daoud, Mohammad Shehab, Laith Abualigah, Mohammad Alshinwan, Mohamed Abd Elaziz, Mohd Khaled Yousef Shambour, Diego Oliva, Mohammad A. Alia, Raed Abu Zitar
JOURNAL OF BIONIC ENGINEERING
(2023)
Review
Engineering, Multidisciplinary
Mohammad Sh. Daoud, Mohammad Shehab, Laith Abualigah, Mohammad Alshinwan, Mohamed Abd Elaziz, Mohd Khaled Yousef Shambour, Diego Oliva, Mohammad A. A. Alia, Raed Abu Zitar
Summary: Chimp Optimization Algorithm (ChOA) is a recent metaheuristic swarm intelligence method that has been widely used for various optimization problems. It has impressive characteristics such as few parameters, no need for derivation information, simplicity, flexibility, scalability, and a capability to balance exploration and exploitation. Due to these advantages, ChOA has gained significant research interest and has been applied in several domains. This review paper provides an overview of research publications using ChOA, including introductory information, discussions on its operations and theoretical foundation, and detailed descriptions of its recent versions and applications. The evaluation of ChOA is also provided. Overall, this review paper is helpful for researchers and practitioners in various fields and provides potential future research directions.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Taymaz Akan, Diego Oliva, Ali-Reza Feizi-Derakhshi, Amir-Reza Feizi-Derakhshi, Marco Perez-Cisneros, Mohammad Alfrad Nobel Bhuiyan
Summary: Image segmentation is a fundamental step in image processing with crucial applications in computer vision, medical imaging, and object recognition. Histogram-based thresholding is a prevalent method for image segmentation. The Battle Royal Optimizer (BRO) is a recent optimization algorithm that shows promise in multilevel image thresholding.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mosa E. Hosney, Diego Oliva, Eman M. G. Younis, Abdelmgeid A. Ali, Waleed M. Mohamed
Summary: This paper proposes a wrapper feature selection approach that combines the rat swarm optimization algorithm with genetic operators to improve classification accuracy and reduce the number of features. The approach converts the continuous search space into a discrete space using transfer functions, achieving a balance between local and global search. Experimental results demonstrate the efficiency and effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Somnath Chatterjee, Debyarati Saha, Shibaprasad Sen, Diego Oliva, Ram Sarkar
Summary: This paper presents a two-stage facial expression recognition system using thermal images. The first stage utilizes the MobileNet model to extract features from input images. The second stage employs the Moth-flame Optimization algorithm to select the optimal feature subset. The proposed model achieves an accuracy of 97.47% on the thermal image-based facial expressions dataset while using only 29% features generated from the MobileNet model.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Angel Chavarin, Erik Cuevas, Omar Avalos, Jorge Galvez, Marco Perez-Cisneros
Summary: Homomorphic filtering (HF) is a method that decomposes an image into illumination and reflectance components to improve contrast while preserving edges and sharp features. Finding the optimal filter parameters is challenging and often involves trial-and-error, but this paper proposes using cluster chaotic optimization (CCO) to efficiently search the parameter space. Experimental results show that the proposed method produces competitive results in terms of quality, stability, and accuracy compared to other methods on different datasets.
Article
Computer Science, Information Systems
Mohamed Abd Elaziz, Abdelghani Dahou, Mohammed Azmi Al-Betar, Shaker El-Sappagh, Diego Oliva, Ahmad O. Aseeri
Summary: Social IoT systems improve the user experience in various applications and the developed QAHA algorithm enhances feature selection using Quantum optimization. Experiments demonstrate the efficiency of QAHA in both UCI and SIoT datasets, showing increased accuracy and decreased feature count.
Article
Computer Science, Information Systems
Laith Abualigah, Diego Oliva, Heming Jia, Faiza Gul, Nima Khodadadi, Abdelazim G. Hussien, Mohammad Al Shinwan, Absalom E. Ezugwu, Belal Abuhaija, Raed Abu Zitar
Summary: A novel hybrid optimization algorithm called IPDOA is proposed in this paper to solve various benchmark functions. By enhancing the search process of PDOA using the primary updating mechanism of DMOA, the proposed method aims to address the main weaknesses of the original methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
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)