Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

Deep Residual Learning for Image Recognition

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DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects

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Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG

Zhang, J., Zhao, Y., Shone, F., Li, Z., Frangi, A. F., Xie, S. Q., & Zhang, Z. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3226860 · Cited by 195

Cooperative USV–UAV marine search and rescue with visual navigation and reinforcement learning-based control

Wang, Y., Liu, W., Liu, J., & Sun, C. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.01.007 · Cited by 186

Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model

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An Appraisal of Incremental Learning Methods

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An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices

Kamara, A. F., Chen, E., & Pan, Z. (2022) · Information Sciences · doi:10.1016/j.ins.2022.02.015 · Cited by 108

A data-driven physics-constrained deep learning computational framework for solving von Mises plasticity

Roy, A. M., & Guha, S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106049 · Cited by 115

Integrated Multiple Kernel Learning for Device-Free Localization in Cluttered Environments Using Spatiotemporal Information

Zhang, J., Li, Y., & Xiao, W. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028574 · Cited by 94

Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach

Zhou, L., Zhao, C., Liu, N., Yao, X., & Cheng, Z. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106157 · Cited by 92

Federated Learning for the Internet-of-Medical-Things: A Survey

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A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting

Syama, S., Ramprabhakar, J., Anand, R., & Guerrero, J. M. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101274 · Cited by 70

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri, M., Aljameel, S. S., Mohammad, R. M. A., Almotiri, S. H., Mirza, S., Anis, F. M., Aboulnour, M., Alomari, D. M., Alhamed, D. H., & Altamimi, H. S. (2021) · Sensors · doi:10.3390/s21217070 · Cited by 74

Leveraging Online Learning for Domain-Adaptation in Wi-Fi-Based Device-Free Localization

Zhang, J., Xue, J., Li, Y., & Cotton, S. L. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3552538 · Cited by 68

HandGest: Hierarchical Sensing for Robust-in-the-Air Handwriting Recognition With Commodity WiFi Devices

Zhang, J., Li, Y., Xiong, H., Dou, D., Miao, C., & Zhang, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3170157 · Cited by 58

Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A survey

Al-Haija, Q. S. A., Altamimi, S., & Alwadi, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124317 · Cited by 58

ConvAttenMixer: Brain tumor detection and type classification using convolutional mixer with external and self-attention mechanisms

Alzahrani, S. M. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101810 · Cited by 58

Physics-infused deep neural network for solution of non-associative Drucker–Prager elastoplastic constitutive model

Roy, A. M., Guha, S., Sundararaghavan, V., & Arróyave, R. (2024) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2024.105570 · Cited by 60

3D Convolutional Neural Networks with Hybrid Attention Mechanism for Early Diagnosis of Alzheimer's Disease

Qin, Z., Liu, Z., Guo, Q., & Zhu, P. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103828 · Cited by 56

Extreme Learning Machine Applied to Software Development Effort Estimation

Carvalho, H. D. P. D., Fagundes, R., & Santos, W. B. (2021) · IEEE Access · doi:10.1109/access.2021.3091313 · Cited by 53

Evolutionary feature selection on high dimensional data using a search space reduction approach

García-Torres, M., Ruíz, R., & Divina, F. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105556 · Cited by 51

Online Spatiotemporal Modeling for Robust and Lightweight Device-Free Localization in Nonstationary Environments

Zhang, J., Li, Y., Xiao, W., & Zhang, Z. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3218666 · Cited by 50

Machine learning classification of maladaptive rumination and cognitive distraction in terms of frequency specific complexity

Aydın, S., & Akın, B. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103740 · Cited by 49

Maximizing intrusion detection efficiency for IoT networks using extreme learning machine

Altamimi, S., & Al-Haija, Q. S. A. (2024) · Discover Internet of Things · doi:10.1007/s43926-024-00060-x · Cited by 48

Enhanced WiFi CSI Fingerprints for Device-Free Localization With Deep Learning Representations

Xue, J., Zhang, J., Gao, Z., & Xiao, W. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3231611 · Cited by 47

Enhancing smart grid resilience with deep learning anomaly detection prior to state estimation

Akagić, A., & Džafić, I. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107368 · Cited by 43

Multimodal emotion recognition based on feature selection and extreme learning machine in video clips

Pan, B., Hirota, K., Jia, Z., Zhao, L., Jin, X., & Dai, Y. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03407-2 · Cited by 46

Intelligent quantification of natural gas pipeline defects using improved sparrow search algorithm and deep extreme learning machine

Xiong, J., Liang, W., Liang, X., & Yao, J. (2022) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2022.06.001 · Cited by 40

Automated detection of heart valve disorders with time-frequency and deep features on PCG signals

Arslan, Ö. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103929 · Cited by 42

A hybrid framework based on extreme learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock prediction

Wu, D., Wang, X., & Wu, S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118006 · Cited by 39

Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

Sheykhinasab, A., Mohseni, A. A., Bahari, A. B., Naruei, E., Davoodi, S., Aghaz, A., & Mehrad, M. (2022) · Journal of Petroleum Exploration and Production Technology · doi:10.1007/s13202-022-01593-z · Cited by 42

Attention-based multimodal glioma segmentation with multi-attention layers for small-intensity dissimilarity

Liu, X., Hou, S., Liu, S., Ding, W., & Zhang, Y. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.03.011 · Cited by 37

A Review of multilayer extreme learning machine neural networks

Vásquez-Coronel, J. A., Mora, M., & Vilches-Ponce, K. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10478-4 · Cited by 39

A comprehensive review of extreme learning machine on medical imaging

Huérfano, Y., Mora, M., Vilches-Ponce, K., Hernández-García, R., Gutiérrez, R., & Vera, M. Á. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126618 · Cited by 37

Optimizing the impact of data augmentation for low-resource grammatical error correction

Solyman, A., Zappatore, M. S., Wang, Z., Mahmoud, Z., Alfatemi, A., Ibrahim, A. O., & Gabralla, L. A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101572 · Cited by 37

Federated learning with hyper-parameter optimization

Kundroo, M., & Kim, T. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101740 · Cited by 33

A new deep technique using R-CNN model and L1NSR feature selection for brain MRI classification

Demir, F., & Akbulut, Y. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103625 · Cited by 35

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Alizamir, M., Ahmed, K. O., Shiri, J., Fard, A. F., Kim, S., Heddam, S., & Kişi, Ö. (2023) · Sustainability · doi:10.3390/su151411275 · Cited by 32

Multi-objective optimization-based adaptive class-specific cost extreme learning machine for imbalanced classification

Li, Y., Zhang, J., Zhang, S., Xiao, W., & Zhang, Z. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.008 · Cited by 31

Key nodes identification in complex networks based on subnetwork feature extraction

Gao, L., Liu, X., Liu, C., Zhang, Y., Fiumara, G., & Meo, P. D. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101631 · Cited by 28

Real-Time Multi-Task ADAS Implementation on Reconfigurable Heterogeneous MPSoC Architecture

Tatar, G., & Bayar, S. (2023) · IEEE Access · doi:10.1109/access.2023.3300379 · Cited by 28

1D Convolutional LSTM-based wind power prediction integrated with PkNN data imputation technique

Shahid, F., Mehmood, A., Khan, R., Smadi, A. A., Yaqub, M., Alsmadi, M. K., & Zheng, Z. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101816 · Cited by 27

A hybrid model for classification of medical data set based on factor analysis and extreme learning machine: FA + ELM

Kaya, Y., & Kuncan, F. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104023 · Cited by 27

Modeling the Stackelberg game with a boundedly rational follower in deterioration supply chain-based interaction with the leader’s hybrid pricing strategy

Khanlarzade, N., & Farughi, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121302 · Cited by 27

Multilayer extreme learning machines and their modeling performance on dynamical systems

Kale, G. A., & Karakuzu, C. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108861 · Cited by 26

MSENet: Mean and standard deviation based ensemble network for cervical cancer detection

Pramanik, R., Banerjee, B., & Sarkar, R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106336 · Cited by 26

Designing a new fast solution to control isolation rooms in hospitals depending on artificial intelligence decision

Ahmed, S. K., Ali, R. M., Lashin, M. M. A., & Sherif, F. F. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104100 · Cited by 25

Boosting Personalized Musculoskeletal Modeling With Physics-Informed Knowledge Transfer

Zhang, J., Zhao, Y., Bao, T., Li, Z., Qian, K., Frangi, A. F., Xie, S. Q., & Zhang, Z. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3227604 · Cited by 25

Prognosticating various acute covid lung disorders from COVID-19 patient using chest CT Images

Suganya, D., & Kalpana, R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105820 · Cited by 27

Multilayer extreme learning machine: a systematic review

Kaur, R., Roul, R. K., & Batra, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14634-4 · Cited by 26

Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study

Mascarenhas, M., Ribeiro, T., González-Haba, M., Agudo, B., Ferreira, J. P. S., Boas, F. V., Afonso, J. P. L., Mendes, F., Martins, M., Cardoso, P., et al. (2023) · Cancers · doi:10.3390/cancers15194827 · Cited by 24

Extreme learning machine versus classical feedforward network

Markowska–Kaczmar, U., & Kosturek, M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06402-y · Cited by 24

Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues, I. R., Neto, S. R. D. S., Kelner, J., Sadok, D. F. H., & Endo, P. T. (2021) · Informatics · doi:10.3390/informatics8020033 · Cited by 26

Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems

Berghout, T., Benbouzid, M., Muyeen, S. M., Bentrcia, T., & Mouss, L. H. (2021) · IEEE Access · doi:10.1109/access.2021.3127084 · Cited by 21

A hybrid neural network and optimization algorithm for forecasting and trend detection of Forex market indices

Perla, S., Bisoi, R., & Dash, P. K. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100193 · Cited by 21

Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning

Usman, M., Sarfraz, M. S., Habib, U., Aftab, M. U., & Javed, S. (2023) · Sensors · doi:10.3390/s23083931 · Cited by 21

Toward Robust and Efficient Musculoskeletal Modeling Using Distributed Physics-Informed Deep Learning

Zhang, J., Ruan, Z., Li, Q., & Zhang, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3325522 · Cited by 24

Unraveling pine wilt disease: Comparative study of stochastic and deterministic model using spectral method

Shah, K., Liu, W., Raezah, A. A., Khan, N., Khan, S. U., Ozair, M., & Ahmad, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122407 · Cited by 21

Committee machine learning: A breakthrough in the precise prediction of CO2 storage mass and oil production volumes in unconventional reservoirs

Davoodi, S., Thanh, H. V., Wood, D. A., Mehrad, M., Al-Shargabi, M., & Rukavishnikov, V. S. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.213533 · Cited by 21

Toward Robust and Accurate Device-Free Localization in Cluttered Environments With Commodity WiFi Devices

Zhang, J., Li, Y., & Xiao, W. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3192322 · Cited by 20

Dual ensemble online modeling for dynamic estimation of hot metal silicon content in blast furnace system

Li, Y., Zhang, J., Zhang, S., & Xiao, W. (2021) · ISA Transactions · doi:10.1016/j.isatra.2021.09.018 · Cited by 19

Variational quantum extreme learning machine

Wang, Y., Lin, K.-Y., Cheng, S., & Li, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.068 · Cited by 20

Image generation models from scene graphs and layouts: A comparative analysis

Hassan, M. U., Alaliyat, S., & Hameed, I. A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.03.021 · Cited by 19

Conditional normalizing flow for multivariate time series anomaly detection

Guan, S., He, Z., Ma, S., & Gao, M. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.09.002 · Cited by 19

Neighborhood multigranulation rough sets for cost-sensitive feature selection on hybrid data

Shu, W., Xia, Q., & Qian, W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126990 · Cited by 19

Surface Roughness Prediction in Ultra-Precision Milling: An Extreme Learning Machine Method with Data Fusion

Shang, S., Wang, C., Liang, X., Cheung, C. F., & Zheng, P. (2023) · Micromachines · doi:10.3390/mi14112016 · Cited by 18

Inter-patient ECG classification with intra-class coherence based weighted kernel extreme learning machine

Xu, Y., Zhang, S., & Xiao, W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120095 · Cited by 18

Applied Control and Artificial Intelligence for Energy Management: An Overview of Trends in EV Charging, Cyber-Physical Security and Predictive Maintenance

Celsi, L. R., & Valli, A. (2023) · Energies · doi:10.3390/en16124678 · Cited by 18

A system-on-chip solution for deep learning-based automatic fetal biometric measurement

Cho, H., Kim, D., Chang, S., Kang, J., & Yoo, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121482 · Cited by 20

Label consistency-based deep semisupervised NMF for tumor recognition

Yang, L., Yan, L., Wei, X., & Yang, X. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105511 · Cited by 17

A deep learning approach for solving linear programming problems

Wu, D., & Lisser, A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.053 · Cited by 17

Fault Diagnosis Based Machine Learning and Fault Tolerant Control of Multicellular Converter Used in Photovoltaic Water Pumping System

Rouabah, B., Toubakh, H., Djemaï, M., Ben‐Brahim, L., & Ghandour, R. (2023) · IEEE Access · doi:10.1109/access.2023.3266522 · Cited by 17

A Hybrid Artistic Model Using Deepy-Dream Model and Multiple Convolutional Neural Networks Architectures

Al-Khazraji, L. R., Abbas, A. R., Jamil, A. S., & Hussain, A. J. (2023) · IEEE Access · doi:10.1109/access.2023.3309419 · Cited by 17

Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review

Wang, X., Zhang, J., Xie, S. Q., Shi, C., Li, J., & Zhang, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3359811 · Cited by 20

A deep learning approach to estimate temperature and flow velocity distributions of wire-wrapped fuel assemblies

Li, Y., Wang, R., Song, Y., Wan, D., Hu, D., & Jiang, C. (2024) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2024.107853 · Cited by 17

Noise/fault aware regularization for incremental learning in extreme learning machines

Wong, H. T., Leung, H. C., Leung, C.-S., & Wong, E. W. M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.026 · Cited by 16

Deep learning for automatic tumor lesions delineation and prognostic assessment in multi-modality PET/CT: A prospective survey

Islam, M. Z., Naqvi, R. A., Haider, A., & Kim, H. S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106276 · Cited by 16

Item Attribute-aware Graph Collaborative Filtering

Li, A., Liu, X., & Yang, B. X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122242 · Cited by 16

Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

Zhang, Y., Ding, J., Sun, J., & Zhang, D. (2023) · steel research international · doi:10.1002/srin.202200832 · Cited by 15

Hyperbolic embedding steered spatiotemporal graph convolutional network for video-based remote heart rate estimation

Shao, H., Luo, L., Chen, S., Hu, C., & Yang, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106642 · Cited by 15

Global-and-local aware network for low-light image enhancement

He, X., Chen, Z., Dai, L., Lei, L., Wu, J., & Sheng, B. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106969 · Cited by 15

No-Reference Image Quality Assessment of Magnetic Resonance images with multi-level and multi-model representations based on fusion of deep architectures

Stępień, I., & Oszust, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106283 · Cited by 14

Attention-Based Deep Gated Fully Convolutional End-to-End Architectures for Time Series Classification

Khan, M., Wang, H., & Ngueilbaye, A. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10484-z · Cited by 14

Integrative Evolutionary-Based Method for Modeling and Optimizing Budget Assignment of Bridge Maintenance Priorities

Abdelkader, E. M., Moselhi, O., Marzouk, M. M., & Zayed, T. M. (2021) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002113 · Cited by 14

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Hameed, M. M., Masood, A., Srivastava, A., Rahman, N. A., Razali, S. F. M., Salem, A., & Elbeltagi, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-61059-6 · Cited by 13

An interval neural network-based Caputo fractional-order extreme learning machine applied to classification

Liu, Y., Shao, Q., Liu, Y., & Yang, D. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112310 · Cited by 13

Data and knowledge driven approach for burden surface optimization in blast furnace

Li, Y., Li, H., Zhang, J., & Zhang, S. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107191 · Cited by 13

Functional extreme learning machine for regression and classification

Liu, X., Zhou, Y., Meng, W., & Luo, Q. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023177 · Cited by 12

Time efficient variants of Twin Extreme Learning Machine

Anand, P., Bharti, A., & Rastogi, R. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200169 · Cited by 12

MADMAX: Browser-Based Malicious Domain Detection Through Extreme Learning Machine

Iwahana, K., Takemura, T., Cheng, J. C., Ashizawa, N., Umeda, N., Sato, K., Kawakami, R., Shimizu, R., Chinen, Y., & Yanai, N. (2021) · IEEE Access · doi:10.1109/access.2021.3080456 · Cited by 12

OEINR-RFH: Outlier elimination based iterative neighbor representation for robust face hallucination

Rajput, S. S., Rai, D., & Kumar, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121553 · Cited by 11

Artificial algae optimization for Virtual Network Embedding problems in 5G network slicing scenarios

Gomes, R., Vieira, D., Pereira, M. B., & de Castro, M. F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122436 · Cited by 10

A Self-Optimized Machine Learning Approach for Constitutive Parameters Identification of Aortic Walls

Li, Y., Hu, D., Wan, D., Yan, X., & Sang, J. (2024) · International Journal of Applied Mechanics · doi:10.1142/s1758825124500558 · Cited by 10

CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex Backgrounds

Sun, W., Na, M., Chen, L., Yang, S., Li, Y., & Tian, S. (2025) · Machines · doi:10.3390/machines13040301 · Cited by 10

Dynamic Programming for Thermal Management of Automotive Fuel Cell Systems: Investigating Hydrogen Saving Potential

Anselma, P. G., Luciani, S., & Tonoli, A. (2023) · IEEE Access · doi:10.1109/access.2023.3272830 · Cited by 10

Incremental Fault Diagnosis Method Based on Metric Feature Distillation and Improved Sample Memory

Min, Q., He, J., Yu, P., & Fu, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3274481 · Cited by 10

Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study

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