Article
Neurosciences
Kai Yang, Li Tong, Ying Zeng, Runnan Lu, Rongkai Zhang, Yuanlong Gao, Bin Yan
Summary: Recent studies have shown that recognizing and monitoring different valence emotions can effectively prevent human errors caused by cognitive decline. This study explores effective electroencephalography (EEG) features for recognizing different valence emotions. The results show that first-order difference, second-order difference, high-frequency power, and high-frequency differential entropy features perform better in emotion recognition. Time-domain features, especially first-order difference and second-order difference features, have shorter computing time, making them suitable for real-time emotion recognition applications. Features extracted from the frontal, temporal, and occipital lobes are more effective in recognizing different valence emotions. Furthermore, when the number of electrodes is reduced by 3/4, using features from 16 electrodes located in these brain regions achieves a classification accuracy of 91.8%, only about 2% lower than using all electrodes. These findings provide important guidance for feature extraction and selection in EEG-based emotion recognition.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Chemistry, Analytical
Rajamanickam Yuvaraj, Prasanth Thagavel, John Thomas, Jack Fogarty, Farhan Ali
Summary: Advances in signal processing and machine learning have accelerated EEG-based emotion recognition research. This study compared the classification accuracy of various sets of EEG features to identify emotional states. By evaluating the performance on five independent datasets, it was found that the FD-CART feature-classification method achieved the highest accuracy for valence and arousal. These findings suggest the reliability of the FD features derived from EEG data for emotion recognition, and may contribute to the development of a real-time EEG-based emotion recognition system.
Article
Computer Science, Artificial Intelligence
Dheeb Albashish, Abdelaziz Hammouri, Malik Braik, Jaffar Atwan, Shahnorbanun Sahran
Summary: The study proposed a hybrid metaheuristic model based on BBO-SVM-RFE for feature selection, which outperformed other methods in terms of accuracy and number of selected features. Results revealed the high potential of BBO-SVM-RFE in reliably searching the feature space to obtain the optimal combination of features.
APPLIED SOFT COMPUTING
(2021)
Article
Orthopedics
Hidetoshi Nakao, Masakazu Imaoka, Mitsumasa Hida, Ryota Imai, Misa Nakamura, Kazuyuki Matsumoto, Kenji Kita
Summary: This study used support vector machine-recursive feature elimination (SVM-RFE) to identify the factors related to hallux valgus (HV) and their importance. Analysis of data from 864 participants aged ≥ 18 years showed that age, sex, and body weight were associated with HV.
BMC MUSCULOSKELETAL DISORDERS
(2023)
Article
Chemistry, Multidisciplinary
Ioannis Zorzos, Ioannis Kakkos, Stavros T. Miloulis, Athanasios Anastasiou, Errikos M. Ventouras, George K. Matsopoulos
Summary: In this study, a shallow convolutional neural network was proposed for fatigue detection using EEG data. By combining time-frequency domain features extracted with Morlet wavelet analysis and higher-level characteristics learned by the model, a resilient solution with high prediction accuracy (97%) was achieved, while reducing training time and computing costs.
APPLIED SCIENCES-BASEL
(2023)
Article
Plant Sciences
Aida Shomali, Sasan Aliniaeifard, Mohammad Reza Bakhtiarizadeh, Mahmoud Lotfi, Mohammad Mohammadian, Mohammad Sadegh Vafaei Sadi, Anshu Rastogi
Summary: High light stress directly affects the photosynthesis apparatus, making breeding plants with tolerance against this stress highly demanded. Chlorophyll fluorescence can be used to indicate plant stress and was compared in plants exposed to high light and control conditions. Artificial neural network algorithms were applied to identify reliable features for screening plant tolerance against high light. The selected features were then used to categorize tomato genotypes and validated using measurements of foliar hydrogen peroxide and malondialdehyde contents.
PLANT PHYSIOLOGY AND BIOCHEMISTRY
(2023)
Article
Environmental Sciences
Yaa Takyiwaa Acquaah, Balakrishna Gokaraju, Raymond C. Tesiero, Gregory H. Monty
Summary: This study compared feature extraction techniques to detect the number of people in an area, finding that the SVM model based on wavelet scattering features achieved the best performance with an accuracy of 100%. The ResNet-50 model utilizing transfer learning with deep features from thermal imagery outperformed the VGG-16 model.
Article
Computer Science, Artificial Intelligence
Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh
Summary: This study proposes an innovative method to transform EEG signals into weight vectors of an autoencoder to address the issue of informative confusion in multiclass classification of MI based on EEG data. The extracted features from the weight vectors are used in a support vector machine as a classifier network to improve the decoding performance of BCIs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Jihyoung Ryu
Summary: This paper presents a unique framework for evaluating blind image quality using support vector machine (SVM) regression. Various image characteristics are included in the framework and an SVM regression model is trained to predict image quality ratings. Experimental results demonstrate the high accuracy and robustness of the proposed framework, indicating its potential to improve BIQA approaches and broaden its applications in image transmission, compression, and restoration.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Multidisciplinary
Lijuan Zhao, Long Zhang, Hao Zhang, Yanqing Hu
Summary: An adaptive multi-band denoising model based on the Morlet wavelet filter and sparse representation is proposed to accurately extract the bearing fault-induced impulse features from vibration signals corrupted by noise and random impulses. The model filters the signal in the frequency domain using the Morlet wavelet filter to locate the desired frequency band associated with fault components. The in-band noise is eliminated by sparse representation, and the fault characteristic frequency is extracted through demodulation using the Hilbert transform.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Biochemical Research Methods
Xiao-Nei Zhang, Qing-Hao Meng, Ming Zeng, Hui-Rang Hou
Summary: A new feature named wavelet-spatial domain feature (WSDF) was developed by combining discrete wavelet transform (DWT) and one-versus-rest common spatial pattern (OVR-CSP) for decoding olfactory EEG signals. Experimental results showed superior classification performance of WSDF on two datasets.
JOURNAL OF NEUROSCIENCE METHODS
(2021)
Article
Computer Science, Artificial Intelligence
Xin Zheng, Shouzhi Liang, Bo Liu, Xiaoming Xiong, Xianghong Hu, Yuan Liu
Summary: The paper proposes a new architecture named GMADL for subgraph feature extraction, utilizing dictionary learning approaches to enhance discrimination of model features in graph data. By designing an analysis dictionary and constructing multi-view support vector machine classifiers, the efficiency of feature extraction is improved and the classification model prediction accuracy is enhanced by utilizing information from multiple views. Comparisons with state-of-the-art approaches demonstrate the feasibility and competitiveness of the proposed architecture in graph classification.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Xin Ma, Yu Luo, Jian Shi, Hailiang Xiong
Summary: This paper presents a system for quick and effective fault detection of substation power transformers using non-contact measurement. The collected AE signals were preprocessed and three machine learning algorithms were designed for classification and testing. The results show that the combination of 2DPCA preprocess with SVM algorithm achieved the best comprehensive effectiveness.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Sengul Dogan, Ilknur Tuncer, Mehmet Baygin, Turker Tuncer
Summary: Fatigue detection is a critical application area for machine learning, and electroencephalography (EEG) signals are commonly used inputs. This study proposes a hand-crafted framework for accurately detecting fatigue, using wavelet packet decomposition and multilevel feature extraction. Two validation techniques, tenfold cross-validation and leave-one-subject-out (LOSO) validation, are applied to obtain robust classification results. The proposed framework achieves high classification performance with 99.90% and 82.08% accuracies using tenfold CV and LOSO CV, respectively, demonstrating its efficacy in accurately detecting fatigue.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Biomedical
Qi Xin, Shaohai Hu, Shuaiqi Liu, Ling Zhao, Yu-Dong Zhang
Summary: This paper proposes an Attention Mechanism-based Wavelet Convolution Neural Network for epilepsy EEG classification, achieving high accuracy through multi-scale wavelet analysis and attention mechanism.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Oncology
Ana Bartolo, Isabel M. Santos, Sara Monteiro
Summary: Research finds that young women with cancer have reproductive health concerns related to fertility status, children's health, and dyadic relationships. Personal circumstances and previous therapies can affect these concerns, and nurses play a key role in accompanying patients over an extended period.
Review
Clinical Neurology
E. Sarrias-Arrabal, G. Izquierdo-Ayuso, M. Vazquez-Marrufo
Summary: This review explores the use of the Attention Network Test (ANT) in studying neurological diseases and identifies anatomical structures associated with the three attentional networks. The prefrontal cortex, parietal region, thalamus, and cerebellum are found to be particularly important in the Alertness Network.
Article
Behavioral Sciences
Ana Bartolo, Isabel M. Santos, Raquel Guimaraes, Salome Reis, Sara Monteiro
Summary: The study found that biased cognitive processing towards reproduction-related cues exists for all young women, but attentional bias is significantly associated with concerns about partner disclosure of fertility status only for breast cancer survivors. The desire to have a (or another) biological child is also a significant predictor of higher concerns related to fertility potential for all young women.
BEHAVIORAL MEDICINE
(2022)
Article
Psychology, Applied
A. Fort, B. Collette, M. Evennou, C. Jallais, B. Charbotel, A. N. Stephens, A. Hidalgo-Munoz
Summary: Driving anxiety can significantly impact an individual’s quality of life, particularly in unemployed individuals. The study found that the extremely anxious group was underrepresented in males, while overrepresented in the 35-44 age group and unlicensed drivers.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR
(2021)
Article
Psychology, Clinical
Joana Carvalho, Liliana Ferreira, Rita Rico, Ana Bartolo, Isabel M. Santos
Summary: This study found that age predicts women's cognitive and emotional appraisal of sex pictures, with older women reporting increased pleasantness and subjective arousal to sexually moderate and explicit pictures. Additionally, sexual beliefs and exposure time moderate some of these predictions, highlighting the role of contextual factors in women's evaluation of erotica.
JOURNAL OF SEX & MARITAL THERAPY
(2022)
Review
Environmental Sciences
Sandra Silva, Ana Bartolo, Isabel M. Santos, Anabela Pereira, Sara Monteiro
Summary: This study presents a systematic review of factors associated with distress in elderly cancer patients. The research found that being female, single or widowed, having low income, an advanced diagnosis, functional limitations, comorbidities, and little social support were consistently associated with emotional distress. The impact of age, cancer type, and treatment on anxiety and depression symptoms in elderly patients is still unclear.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Multidisciplinary Sciences
Esteban Sarrias-Arrabal, Ruben Martin-Clemente, Alejandro Galvao-Carmona, Maria Luisa Benitez-Lugo, Manuel Vazquez-Marrufo
Summary: Recent studies have found that nonphase-locked brain activity can reveal cognitive mechanisms that cannot be observed in phase-locked activity. The main aim of this study was to investigate the potential roles of nonphase-locked alpha and gamma activities in cognitive processes. The results showed that nonphase-locked alpha activity is bilaterally represented in the scalp, while nonphase-locked gamma activity exhibits higher desynchronization in the ipsilateral hemisphere.
SCIENTIFIC REPORTS
(2022)
Review
Medicine, General & Internal
Ana F. Oliveira, Sofia Fernandes, Juliana D. Reis, Ana Torres, Isabel M. Santos, Diane Von Ah
Summary: In recent years, there has been increasing attention on the impact of cancer-related cognitive impairment (CRCI) in working non-central nervous system (CNS) cancer survivors. This study aims to comprehensively summarize quantitative evidence on the relationship between CRCI and work-related outcomes in adult non-CNS cancer survivors at working age through a systematic review and meta-analysis.
Article
Psychology, Clinical
Antonio R. Hidalgo-Munoz, Esther Cuadrado, Rosario Castillo-Mayen, Barbara Luque, Carmen Tabernero
Summary: This study analyzed respiratory activity to investigate if variations in breathing rate can serve as predictive factors for subsequent affective states after social interactions. The results suggest that breathing rate can be a reliable indicator to infer subjective feelings and can be incorporated into modern emotion monitoring systems.
APPLIED PSYCHOPHYSIOLOGY AND BIOFEEDBACK
(2022)
Article
Neurosciences
Elena Pala, Irene Escudero-Martinez, Anna Penalba, Alejandro Bustamante, Marcel Lamana-Vallverdu, Fernando Mancha, Rafael F. Ocete, Pilar Pinero, Alejandro Galvao-Carmona, Marta Gomez-Herranz, Soledad Perez-Sanchez, Francisco Moniche, Alejandro Gonzalez, Joan Montaner
Summary: This study investigated the association between blood-biomarkers representing different atrial fibrillation (AF)-related pathways and silent brain infarcts (SBI), white matter hyperintensities (WMH), and cognitive decline in AF patients with low embolic risk. The results showed that BMP-10 and Ang-2 were increased in AF patients with SBI, suggesting their potential usefulness in detecting SBI in these patients.
JOURNAL OF STROKE & CEREBROVASCULAR DISEASES
(2022)
Article
Geriatrics & Gerontology
Maria-Luisa Benitez-Lugo, Carmen Suarez-Serrano, Alejandro Galvao-Carmona, Manuel Vazquez-Marrufo, Gema Chamorro-Moriana
Summary: Aging poses challenges to social and health due to changes in physical and cognitive functions. This study examined the effectiveness of a feedback-based protocol using technology to improve physical and cognitive functions in older adults. The results showed significant improvements in physical variables and memory, suggesting that this intervention can prevent and promote healthy aging.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Review
Multidisciplinary Sciences
Antonio R. Hidalgo-Munoz, Christophe Jallais, Myriam Evennou, Alexandra Fort
Summary: This study provides a systematic review on the link between anxiety and driving behavior, revealing an association between driving anxiety and cautious driving, negative emotions, and avoidance. It also reviews the effects of anti-anxiety drugs on driving tasks, finding that benzodiazepines may impact attention and reaction times. The findings of this study are important for estimating the consequences for traffic safety and designing effective awareness campaigns.
Article
Public, Environmental & Occupational Health
Alexandra Fort, Myriam Evennou, Christophe Jallais, Barbara Charbotel, Antonio Hidalgo-Munoz
Summary: The aim of this study was to quantify the proportion of the French population affected by driving anxiety. An online survey was conducted among 5000 French adults, and the results showed that nearly 80% of the sample expressed at least some level of driving anxiety. Women reported higher levels of driving anxiety than men, and younger individuals had higher levels of anxiety. Additionally, individuals living in larger urban areas (such as Paris) and those in lower-qualified occupational categories reported higher levels of driving anxiety on average. These results highlight the extent of driving anxiety in France.
JOURNAL OF TRANSPORT & HEALTH
(2023)
Review
Communication
Antonio-R. Hidalgo-Munoz, Daniel Acle-Vicente, Alejandro Garcia-Perez, Carmen Tabernero-Urbieta
Summary: Currently, there has been an increase in the number of schoolchildren with ADHD, leading to the exploration of alternative neurotechnologies in classrooms. This review aims to compile scientific evidence on the application and implementation of these techniques in schools. Neurofeedback is the most widely used neurotechnology, while tDCS has a more clinical approach. However, further ecological studies and the emergence of new professional figures in neuroeducation are needed.
Article
Nutrition & Dietetics
Irene Escudero-Martinez, Fernando Mancha, Angela Vega, Montserrat Zapata, Rafael F. Ocete, Lucia alvarez, Pilar Algaba, Antonio Lopez-Rueda, Pilar Pinero, Elena Fajardo, Jose Roman Fernandez-Engo, Eva M. Martin-Sanchez, Alejandro Galvao-Carmona, Elena Zapata-Arriaza, Lucia Lebrato, Blanca Pardo, Juan Antonio Cabezas, Maria Irene Ayuso, Alejandro Gonzalez, Francisco Moniche, Joan Montaner
Summary: This study found that Mediterranean Diet could reduce the risk of silent brain infarcts in patients with AF. Higher consumption of fiber from fruit was associated with a lower risk, while higher consumption of high glycemic load foods was associated with a higher risk of SBI in this population.
NUTRITION AND METABOLIC INSIGHTS
(2022)
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)
