Article
Chemistry, Multidisciplinary
Jiawei Chen, Jianhua Yang, Jianfeng He
Summary: This study proposes a joint extraction model of Chinese electronic medical records based on deep learning, which effectively improves the effect of medical information extraction and determines the risk factors of venous thrombosis through rule reasoning.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Amanda B. Zheutlin, Luciana Vieira, Ryan A. Shewcraft, Shilong Li, Zichen Wang, Emilio Schadt, Susan Gross, Siobhan M. Dolan, Joanne Stone, Eric Schadt, Li Li
Summary: This study developed a novel approach for predicting postpartum hemorrhage and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. The results suggest that the model has the potential to reduce PPH morbidity and mortality through early detection and prevention.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2021)
Article
Psychology, Multidisciplinary
Ava Hajian, Victor R. Prybutok, Hsia-Ching Chang
Summary: Electronic health record (EHR) systems can reduce healthcare costs and medical errors, but the majority of patients are not motivated to share their health records. Blockchain is a reliable alternative to improve current systems. This study explores how blockchain is related to patients' behavior with EHR systems and provides empirical evidence.
COMPUTERS IN HUMAN BEHAVIOR
(2023)
Article
Computer Science, Artificial Intelligence
Kaiye Yu, Zhongliang Yang, Chuhan Wu, Yongfeng Huang, Xiaolei Xie
Summary: This paper proposes a deep in-hospital resource utilization prediction approach to jointly estimate the in-hospital costs and length of stays from patients' admission records via multi-task learning. The approach can effectively utilize heterogeneous information in records and use a multi-view learning framework along with attention networks to better predict resource utilization.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Pharmacology & Pharmacy
Shuai Zhao, Hengfei Li, Xuan Jing, Xuebin Zhang, Ronghua Li, Yinghao Li, Chenguang Liu, Jie Chen, Guoxia Li, Wenfei Zheng, Qian Li, Xue Wang, Letian Wang, Yuanyuan Sun, Yunsheng Xu, Shihua Wang
Summary: This study used traditional Chinese medicine clinical electronic medical records to construct a heterogeneous medical record network, and identified eight non-overlapping subgroups of Type 2 diabetes patients. The findings provide important guidance for personalized treatment.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Health Care Sciences & Services
Marvin Chia-Han Yeh, Yu-Hsiang Wang, Hsuan-Chia Yang, Kuan-Jen Bai, Hsiao-Han Wang, Yu-Chuan (Jack) Li
Summary: This study utilized neural networks to predict lung cancer risk based on electronic medical records, showing high accuracy. It can effectively assist in early screening for patients in specific age groups.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2021)
Article
Management
Kaitlin D. Wowak, Sean Handley, Ken Kelley, Corey M. Angst
Summary: This study explores how hospitals' sourcing strategy for electronic medical record (EMR) systems impacts patient care quality and how this impact changes over time. The research finds a positive correlation between sourcing strategy change towards single-sourcing and conformance quality, but this effect diminishes over time. The results provide critical insights into how sourcing decisions affect performance and how these effects evolve due to changes in regulations, technology, and competition.
Article
Public, Environmental & Occupational Health
Jingfeng Chen, Chonghui Guo, Menglin Lu, Suying Ding
Summary: The objective of this study is to identify and predict a unifying diagnosis (UD) from electronic medical records (EMRs) using a data-driven approach. The results showed that the proposed method effectively extracted a typical diagnosis code co-occurrence pattern, achieved accurate prediction of UD based on patients' diagnostic and admission information, and outperformed other methods overall.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Health Care Sciences & Services
Hossein Estiri, Zachary H. Strasser, Jeffy G. Klann, Pourandokht Naseri, Kavishwar B. Wagholikar, Shawn N. Murphy
Summary: This study utilizes past medical information collected in EHRs to predict death after COVID-19 infection and identifies age, pneumonia history, diabetes with complications, cancer, and pulmonary diseases as important risk factors for mortality prediction.
NPJ DIGITAL MEDICINE
(2021)
Article
Computer Science, Information Systems
Jennifer C. Gander, Mahesh Maiyani, Larissa L. White, Andrew T. Sterrett, Brianna Guney, Pamala A. Pawloski, Teri DeFor, YuanYuan Olsen, Benjamin A. Rybicki, Christine Neslund-Dudas, Darsheen Sheth, Richard Krajenta, Devaki Purushothaman, Stacey Honda, Cyndee Yonehara, Katrina A. B. Goddard, Yolanda K. Prado, Habibul Ahsan, Muhammad G. Kibriya, Briseis Aschebrook-Kilfoy, Chun-Hung Chan, Sarah Hague, Christina L. Clarke, Brooke Thompson, Jennifer Sawyer, Mia M. Gaudet, Heather Spencer Feigelson
Summary: This study developed an algorithm using electronic medical records to identify cancer cases not captured in tumor registries, and its performance was validated in multiple integrated healthcare systems. The algorithm can identify cancer cases regardless of when the diagnosis occurred and is of great importance for cancer care research and quality improvement projects.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2022)
Article
Public, Environmental & Occupational Health
Zhe Xu, Matthew Arnold, Luanluan Sun, David Stevens, Ryan Chung, Samantha Ip, Jessica Barrett, Stephen Kaptoge, Lisa Pennells, Emanuele Di Angelantonio, Angela M. Wood
Summary: Incorporating variability of risk factors from electronic health records improves cardiovascular disease (CVD) risk discrimination for individuals with type 2 diabetes.
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
(2022)
Article
Management
Xin (David) Ding, Xiaosong (David) Peng
Summary: This study examines the impacts of electronic medical records (EMR) on hospital performance. The findings indicate that advanced stages of EMR can help mitigate hospital complexity and improve the process of care. Additionally, clinical focus can substitute for lower stages of EMR and complement higher stages of EMR in mitigating the potential negative impacts of complexity on the process of care.
Article
Computer Science, Interdisciplinary Applications
Farnaz H. Foomani, D. M. Anisuzzaman, Jeffrey Niezgoda, Jonathan Niezgoda, William Guns, Sandeep Gopalakrishnan, Zeyun Yu
Summary: This study developed time series medical generative adversarial networks (GANs) to generate synthetic wound prognosis factors using limited information from routine care. By incorporating temporal information from weekly follow-ups, the model improved classification accuracy and realism of EMR data generation, leading to significant advancements in wound healing prediction compared to previous models.
JOURNAL OF BIOMEDICAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Damian P. Kotevski, Matthew Field, Kathryn Broadley, Claire M. Vajdic, Robert I. Smee, Yvonne N. Nemes
Summary: This study evaluated the performance of Microsoft Presidio with customization on the Australian radiation oncology EMR and found that it can be used for the safe use and sharing of cancer data, although additional checks on person names are required.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Zfania Tom Korach, Stephen Gradwohl, Amanda Messinger, Kelly Bookman, Kevin Cohen, Li Zhou, Foster Goss
Summary: The study compared knowledge-based and unsupervised statistical methods for ranking clinical information in Emergency Department patients with chest or back pain complaints. The results showed that the unsupervised statistical method outperformed the knowledge-based ranking for problems, but underperformed for medications.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Health Care Sciences & Services
Shahadat Uddin, Tasadduq Imam, Md Ekramul Hossain, Ergun Gide, Omid Ameri Sianaki, Mohammad Ali Moni, Ashwaq Amer Mohammed, Vandana Vandana
Summary: The study on type 2 diabetes utilized supervised machine learning algorithms to develop predictive models, with random forest identified as the best performing classifier. It can be used for automated surveillance of patients at risk of developing diabetes from administrative claim data.
INFORMATICS FOR HEALTH & SOCIAL CARE
(2022)
Article
Environmental Sciences
Shahadat Uddin, Arif Khan, Haohui Lu, Fangyu Zhou, Shakir Karim
Summary: This study explored the impact of suburban road networks on COVID-19 vulnerability and severity by analyzing data from Greater Sydney, Australia. The findings suggest that centrality measures of the suburban road network are strong predictors for COVID-19 transmission. These insights could inform stakeholders and policymakers in developing strategies and policies to prevent and contain highly infectious pandemics such as the Delta variant of COVID-19.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Health Care Sciences & Services
Shahadat Uddin, Shangzhou Wang, Arif Khan, Haohui Lu
Summary: This study examines the progression of chronic diseases and their risk factors using a healthcare dataset sample of hospitalized patients. The results show that certain chronic diseases, such as cardiovascular diseases and diabetes, have a high prevalence in progressing to other chronic diseases, which is statistically significant. The progression frequencies increase with time and age, and the patients' sex also affects the disease progressions differently.
Article
Multidisciplinary Sciences
Shahadat Uddin, Stephen Ong, Petr Matous
Summary: Stakeholder engagement is a crucial factor affecting project outcomes, but there is a lack of empirical evidence on the differences in stakeholder engagement patterns between public, private, and public-private partnership (PPP) projects. This study uses social network research methods to capture and compare these engagement structures quantitatively. The findings reveal significant differences in network size, edge number, density, and betweenness centralization across the three types of projects. Additionally, the density varies significantly between 'within budget' and cost overrun projects for private and PPP projects. The study highlights the importance of network data and analytical techniques in managing relationships in complex project ecosystems.
Article
Computer Science, Artificial Intelligence
Taima Rahman Mim, Maliha Amatullah, Sadia Afreen, Mohammad Abu Yousuf, Shahadat Uddin, Salem A. Alyami, Khondokar Fida Hasan, Mohammad Ali Moni
Summary: Human Activity Recognition (HAR) is a valuable research field for clinical applications, where machine learning algorithms play a significant role. The proposed Gated Recurrent Unit-Inception (GRU-INC) model effectively utilizes both temporal and spatial information of time-series data, achieving high F1-scores on various publicly available datasets. The combination of GRU with Attention Mechanism and Inception module with Convolutional Block Attention Module (CBAM) contributes to the superior recognition rate and lower computational cost of the GRU-INC model. This framework has the potential to be applied in activity-associated clinical and rehabilitation applications.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Md Shofiqul Islam, Khondokar Fida Hasan, Sunjida Sultana, Shahadat Uddin, Pietro Lio', Julian M. W. Quinn, Mohammad Ali Moni
Summary: We propose a hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This method fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features, improving the model's performance for prediction. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset.
Article
Chemistry, Multidisciplinary
Nazim Choudhury, Shahadat Uddin
Summary: One of the characteristics of dynamic networks is the evolution of their actors and links. The link prediction mechanism in dynamic networks can capture the growth mechanisms of social networks. Researchers have utilized the temporal patterns of dynamic networks for dynamic link prediction. However, little attention has been given to the temporal variations of actor-level network structure and neighborhood information. This study attempts to build dynamic similarity metrics considering the temporal similarity and correlation between different actor-level evolutionary information of non-connected actor pairs. These metrics are used as dynamic features in the link prediction model and show improved performance compared to static similarity metrics.
APPLIED SCIENCES-BASEL
(2023)
Review
Health Care Sciences & Services
Palak Mahajan, Shahadat Uddin, Farshid Hajati, Mohammad Ali Moni
Summary: Machine learning models are utilized to create and improve disease prediction frameworks, and ensemble learning is a technique that combines multiple classifiers to enhance performance. In this study, the performance accuracies of different ensemble techniques (bagging, boosting, stacking, and voting) are assessed against five highly researched diseases. The findings reveal that stacking has the most accurate performance and can assist researchers in understanding current trends in disease prediction models that employ ensemble learning.
Review
Health Care Sciences & Services
Haohui Lu, Shahadat Uddin
Summary: This study presents a comprehensive review of graph machine learning methods and their applications in disease prediction using electronic health data. The commonly used approaches are shallow embedding and graph neural networks. While graph neural networks have shown outstanding results in disease prediction, they still face challenges in interpretability and dynamic graphs.
Article
Health Care Sciences & Services
Fangyu Zhou, Shahadat Uddin
Summary: In recent years, the amount of data on drugs and their associated adverse drug reactions (ADRs) has significantly increased. A high hospitalization rate worldwide has been reported due to these ADRs. This has led to extensive research on predicting ADRs in the early stages of drug development to minimize future risks. In this study, a drug-to-drug network was constructed using non-clinical data sources to reveal the relationships between drug pairs based on their shared ADRs. The network features were combined with drug features and fed into various machine learning models, showing that logistic regression had the highest mean AUROC score (82.1%) among all the models tested, indicating the potential importance of the network-based approach in future ADR prediction.
Article
Computer Science, Interdisciplinary Applications
Shahadat Uddin, Arif Khan, Haohui Lu
Summary: Research on COVID-19 has seen significant growth in recent years and has been a dominant topic in health-related publications. This study explores the impact of COVID-19 research on journal performance using the Impact Factor and six years of data. The results show that journals publishing COVID-19-related articles experienced a significant increase in their Impact Factor, with lower Impact Factor journals contributing the most to this growth. It suggests that journals prioritizing COVID-19 research may experience increased visibility and Impact Factor growth in the long term.
JOURNAL OF INFORMETRICS
(2023)
Article
Computer Science, Information Systems
Alireza Tavakolian, Alireza Rezaee, Farshid Hajati, Shahadat Uddin
Summary: The study presents a hybrid deep model, GAOCNN, for predicting hospital readmission and length of stay. The model utilizes one-dimensional convolutional layers and optimizes the layer parameters through a genetic algorithm. Experimental results demonstrate the high accuracy of the model in predicting readmission and length of stay for patients with various conditions. This research provides a platform for managing healthcare resources.
Article
Health Care Sciences & Services
Md. Martuza Ahamad, Sakifa Aktar, Md. Jamal Uddin, Md. Rashed-Al-Mahfuz, A. K. M. Azad, Shahadat Uddin, Salem A. Alyami, Iqbal H. Sarker, Asaduzzaman Khan, Pietro Lio, Julian M. W. Quinn, Mohammad Ali Moni
Summary: Good vaccine safety and reliability are crucial for countering infectious diseases effectively. This study aims to reduce adverse reactions to COVID-19 vaccines by identifying common factors through patient data analysis and classification. Patient medical histories and postvaccination effects were examined, and statistical and machine learning approaches were used. The analysis revealed that prior illnesses, hospital admissions, and SARS-CoV-2 reinfection were significantly associated with poor patient reactions.
Proceedings Paper
Computer Science, Theory & Methods
Fangyu Zhou, Shahadat Uddin
Summary: This paper introduces a network-based approach using graph neural networks to support the early detection of adverse drug events. The method models each patient as a subgraph and achieves high accuracy and recall in identifying cohorts associated with adverse drug events.
PROCEEDINGS OF 2023 AUSTRALIAN COMPUTER SCIENCE WEEK, ACSW 2023
(2023)
Proceedings Paper
Computer Science, Theory & Methods
Fangyu Zhou, Shahadat Uddin
Summary: In recent years, there has been an exponential growth in drug-related data and adverse drug reactions (ADRs), leading to a comparatively high hospitalization rate worldwide. To minimize risks, extensive research has been conducted to predict ADRs. Due to the high cost and time-consuming nature of lab experiments, researchers are exploring the use of data mining and machine learning techniques in this field. This paper constructs a weighted drug-drug network by integrating various data sources, revealing underlying relationships between drugs based on common ADRs. Network features are extracted from this network, such as weighted degree centrality and weighted PageRanks, which are concatenated with original drug features to train and test seven classical machine learning algorithms. Experiment results show that adding these network measures benefits all tested machine learning methods, with logistic regression achieving the highest mean AUROC score (0.821) across all ADRs. Weighted degree centrality and weighted PageRanks are identified as the most important network features in the logistic regression classifier. This evidence strongly supports the fundamental role of the network approach in future ADR prediction, where network edge weights play a crucial role in the logistic regression model.
PROCEEDINGS OF 2023 AUSTRALIAN COMPUTER SCIENCE WEEK, ACSW 2023
(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)