A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation

The Jackknife, the Bootstrap and Other Resampling Plans

Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120

Advances in kernel methods: support vector learning

Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809

Cumulated gain-based evaluation of IR techniques

Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841

Making Large-Scale SVM Learning PracticalPreprint

Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,517

Bayesian Inference in Statistical Analysis.

Kadane, J. B., Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.2307/2285412 · Cited by 3,853

Bayesian Inference in Statistical Analysis

Hill, B. M., Box, G. E. P., & Tiao, G. C. (1974) · Technometrics · doi:10.2307/1267683 · Cited by 1,860

Using statistical testing in the evaluation of retrieval experiments

Hull, D. A. (1993) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/160688.160758 · Cited by 525

More accurate tests for the statistical significance of result differences

Yeh, A. (2000) · Proceedings of the 18th conference on Computational linguistics - · doi:10.3115/992730.992783 · Cited by 378

Statistical inference in retrieval effectiveness evaluation

Savoy, J. (1997) · Information Processing & Management · doi:10.1016/s0306-4573(97)00027-7 · Cited by 161

A Statistical Analysis of the TREC-3 Data

Tague‐Sutcliffe, J., & Blustein, J. (1995) · Cited by 87

Monitoring the damage of armyworm as a pest in summer corn by unmanned aerial vehicle imaging

Tao, W., Wang, X., Xue, J., Su, W., Zhang, M., Yin, D., Zhu, D., Xie, Z., & Zhang, Y. (2022) · Pest Management Science · doi:10.1002/ps.6852 · Cited by 31

Artificial Intelligence-Driven Intrusion Detection in Software-Defined Wireless Sensor Networks: Towards Secure IoT-Enabled Healthcare Systems

Umba, S. M. W., Abu-Mahfouz, A. M. I., & Ramotsoela, D. T. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19095367 · Cited by 36

Comparison of Deep Learning Techniques for Classification of the Insects in Order Level With Mobile Software Application

Özdemir, D., & Kunduracı, M. S. (2022) · IEEE Access · doi:10.1109/access.2022.3163380 · Cited by 31

Vision-Based Deep Learning Algorithm for Detecting Potholes

Gajjar, K., van Niekerk, T., Wilm, T., & Mercorelli, P. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2162/1/012019 · Cited by 31

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Sun, K., Zhu, Y., & Song, J. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8020077 · Cited by 31

Real-Time Illegal Parking Detection Algorithm in Urban Environments

Peng, X., Song, R., Cao, Q. P., Li, Y., Cui, D., Jia, X., Lin, Z., & Huang, G.-B. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3180225 · Cited by 34

Multimodal AI framework for lung cancer diagnosis: Integrating CNN and ANN models for imaging and clinical data analysis

Oncu, E., & Çiftçi, F. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110488 · Cited by 33

High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network

Li, Y., Wen, W., Guo, X., Yu, Z., Gu, S., Yan, H., & Zhao, C. (2021) · PLoS ONE · doi:10.1371/journal.pone.0241528 · Cited by 30

Deep linear graph attention model for attributed graph clustering

Liao, H., Hu, J., Li, T., Du, S., & Peng, B. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108665 · Cited by 30

AdaLN: A Vision Transformer for Multidomain Learning and Predisaster Building Information Extraction from Images

Guo, Y., Wang, C., Yu, S. X., McKenna, F., & Law, K. H. (2022) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0001034 · Cited by 36

An implementation of bi-phase network intrusion detection system by using real-time traffic analysis

Chowdhury, R., Sen, S., Goswami, A., Purkait, S., & Saha, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119831 · Cited by 30

$$SPgen $$: A Benchmark Generator for Spatial Link Discovery Tools

Saveta, T., Fundulaki, I., Flouris, G., & Ngomo, A.-C. N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00671-6_24 · Cited by 29

Bridge Infrastructure Management System: Autoencoder Approach for Predicting Bridge Condition Ratings

Rajkumar, M., Nagarajan, S., & Arockiasamy, M. (2022) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2123 · Cited by 29

Lightweight modified attention based deep learning model for cassava leaf diseases classification

Tewari, A. S., & Kumari, P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17459-3 · Cited by 29

A Deep Learning-Based Ensemble Framework for Robust Android Malware Detection

Nethala, S., Chopra, P., Kamaluddin, K., Alam, S., Alharbi, S. A., & Alsaffar, M. S. (2025) · IEEE Access · doi:10.1109/access.2025.3551152 · Cited by 29

Instance Matching Benchmarks in the Era of Linked Data

Daskalaki, E., Flouris, G., Fundulaki, I., & Saveta, T. (2016) · Journal of Web Semantics · doi:10.1016/j.websem.2016.06.002 · Cited by 29

Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique

Woo, S., Jung, C. G., Lee, J., & Kim, S. (2019) · Sustainability · doi:10.3390/su11123397 · Cited by 29

Sentiment Analysis using Topic-Document Embeddings

Mitroi, M., Truică, C., Apostol, E., & Florea, A. M. (2020) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp51029.2020.9266181 · Cited by 29

Deep learning classification of lipid droplets in quantitative phase images

Sheneman, L., Stephanopoulos, G. N., & Vasdekis, A. E. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249196 · Cited by 29

Development of a hybrid system based on convolutional neural networks and support vector machines for recognition and tracking color changes in food during thermal processing

Cotrim, W. D. S., Félix, L. B., Minim, V. P. R., Campos, R. C., & Minim, L. A. (2021) · Chemical Engineering Science · doi:10.1016/j.ces.2021.116679 · Cited by 30

COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs

Al-Shargabi, A. A., Alshobaili, J. F., Alabdulatif, A., & Alrobah, N. (2021) · Applied Sciences · doi:10.3390/app11167174 · Cited by 30

Tree CycleGAN with maximum diversity loss for image augmentation and its application into gear pitting detection

Qin, Y., Wang, Z., & Xi, D. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108130 · Cited by 29

Detecting Lung Cancer from Histopathological Images using Convolution Neural Network

Karim, D. Z., & Bushra, T. A. (2021) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon54134.2021.9707242 · Cited by 31

Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System

Malden, D. E., Tartof, S. Y., Ackerson, B. K., Hong, V. X., Skarbinski, J., Yau, V., Qian, L., Fischer, H. J., Shaw, S. F., Caparosa, S. L., et al. (2022) · JMIR Public Health and Surveillance · doi:10.2196/41529 · Cited by 26

A Comparative Study: Deepfake Detection Using Deep-learning

Khatri, N., Borar, V., & Garg, R. (2023) · 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence56041.2023.10048888 · Cited by 29

Evaluation of Unsupervised Anomaly Detection Techniques in Labelling Epileptic Seizures on Human EEG

Karpov, O. E., Khoymov, M. S., Maksimenko, V., Grubov, V., Павлович, У. Н., Andrikov, D. A., Kurkin, S., & Hramov, A. E. (2023) · Applied Sciences · doi:10.3390/app13095655 · Cited by 29

Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC)

Guo, P., Mahjoubi, S., Liu, K., Meng, W., & Bao, Y. (2023) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2023.e02625 · Cited by 30

Vegetation detection based on spectral information and development of a low‐cost vegetation sensor for selective spraying

Wang, A., Li, W., Men, X., Gao, B., Xu, Y., & Wei, X. (2022) · Pest Management Science · doi:10.1002/ps.6874 · Cited by 28

Landslide susceptibility prediction and mapping using the LD-BiLSTM model in seismically active mountainous regions

Wang, J., Jaboyedoff, M., Chen, G., Luo, X., Derron, M., Hu, Q., Fei, L., Prajapati, G., Choanji, T., Luo, S., et al. (2023) · Landslides · doi:10.1007/s10346-023-02141-4 · Cited by 28

Alloy synthesis and processing by semi-supervised text mining

Wang, W., Jiang, X. F., Tian, S., Liu, P., Lookman, T., Su, Y., & Xie, J. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01138-w · Cited by 30

AMPS: Predicting popularity of short-form videos using multi-modal attention mechanisms in social media marketing environments

Cho, M. K., Jeong, D., & Park, E. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103778 · Cited by 35

Biclustering data analysis: a comprehensive survey

Castanho, E. N., Aidos, H., & Madeira, S. C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae342 · Cited by 35

Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery

Lu, X., Shen, Y., Xie, J., Yang, X., Qingyao, S., Chen, S., Shen, Z., & Cen, H. (2024) · Plant Phenomics · doi:10.34133/plantphenomics.0265 · Cited by 28

Systematically Scrutinizing the Impact of Substitution Sites on Thermostability and Detergent Tolerance for Bacillus subtilis Lipase A

Nutschel, C., Fulton, A., Zimmermann, O., Schwaneberg, U., Jaeger, K., & Gohlke, H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00954 · Cited by 30

Object-Based Classification of PolSAR Images Based on Spatial and Semantic Features

Zou, B., Xu, X., & Zhang, L. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2968966 · Cited by 29

COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus

Arslan, H. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107666 · Cited by 28

Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks

Toprak, M., Boldrini, C., Passarella, A., & Conti, M. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3155946 · Cited by 31

Features fusion based link prediction in dynamic neworks

Kumar, M., Mishra, S., & Biswas, B. (2021) · Journal of Computational Science · doi:10.1016/j.jocs.2021.101493 · Cited by 28

Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence

Albuquerque, R. W., Vieira, D. L. M., Ferreira, M. E., Soares, L. P., Olsen, S. I., de Araujo, L. S., Vicente, L. E., Tymus, J. R. C., Balieiro, C. P., Matsumoto, M. H., et al. (2022) · Remote Sensing · doi:10.3390/rs14040830 · Cited by 33

An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data

Gao, T., Gao, Z., Sun, B., Qin, P., Li, Y., & Yan, Z. (2022) · Remote Sensing · doi:10.3390/rs14174317 · Cited by 28

Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit

Zeng, Z., Tang, X., Liu, Y., He, Z., & Gong, X. (2022) · BioData Mining · doi:10.1186/s13040-022-00309-7 · Cited by 31

Systematic analysis and prediction of longevity genes in Caenorhabditis elegans

Li, Y., Dong, M., & Guo, Z. (2010) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2010.10.001 · Cited by 29

Rhetorics-based multi-document summarization

Atkinson, J., & Munoz, R. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.01.017 · Cited by 28

Bayesian Multiple-Hypothesis Tracking of Merging and Splitting Targets

Makris, A., & Prieur, C. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2316600 · Cited by 28

A high-performance voting-based ensemble model of graph convolutional extreme learning machines for identifying geochemical anomalies related to mineralization

Chen, J., & Chen, Y. (2023) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2023.105706 · Cited by 27

Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests—A Systematic Review

Mușat, F., Păduraru, D. N., Bolocan, A., Palcău, A. C., Copăceanu, A.-M., Daniel, I., Jinga, V., & Andronic, O. (2024) · Biomedicines · doi:10.3390/biomedicines12122892 · Cited by 27

On Feature Weighting and Selection for Medical Document Classification

Parlak, B., & Uysal, A. K. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-58965-7_19 · Cited by 27

PlaNet: A Neural Network for Detecting Transverse Aeolian Ridges on Mars

Nagle-McNaughton, T., McClanahan, T. P., & Scuderi, L. A. (2020) · Remote Sensing · doi:10.3390/rs12213607 · Cited by 29

Predicting Software Defects with Explainable Machine Learning

dos Santos, G. E., Figueiredo, E., Veloso, A., Viggiato, M., & Ziviani, N. (2020) · Brazilian Symposium on Software Quality (SBQS) · doi:10.1145/3439961.3439979 · Cited by 27

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey, S., & Dixit, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13841-9 · Cited by 27

Urban surface water bodies mapping using the automatic k-means based approach and sentinel-2 imagery

Gašparović, M., & Singh, S. K. (2022) · Geocarto International · doi:10.1080/10106049.2022.2148757 · Cited by 28

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging

Wuttisarnwattana, P., Gargesha, M., Hof, W. V., Cooke, K. R., & Wilson, D. L. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2497285 · Cited by 27

A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis

Martínez, R., Salazar-Ramirez, A., Arruti, A., Irigoyen, E., Martin, J. I., & Muguerza, J. (2019) · IEEE Access · doi:10.1109/access.2019.2908445 · Cited by 28

Derin öğrenme yöntemleri ile dokunsal parke yüzeyi tespiti

Aktaş, A., Demir, Ö., & Doğan, B. (2020) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.652101 · Cited by 30

An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers

Thomas, D. J., Robson, B. A., & Racoviteanu, A. E. (2023) · Frontiers in Remote Sensing · doi:10.3389/frsen.2023.1161530 · Cited by 32

IoT-Enhanced Transport and Monitoring of Medicine Using Sensors, MQTT, and Secure Short Message Service

Bhatti, D. S., Hussain, M. M., Suh, B., Ali, Z., Akobir, I., & Kim, K. (2024) · IEEE Access · doi:10.1109/access.2024.3382508 · Cited by 26

Deep learning model for differentiating acute myeloid and lymphoblastic leukemia in peripheral blood cell images via myeloblast and lymphoblast classification

Park, S., Park, Y. H., Huh, J., Baik, S. M., & Park, D. J. (2024) · Digital Health · doi:10.1177/20552076241258079 · Cited by 26

Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis

Fayaz, S., Babu, L., Paridayal, L., Vasantha, M., Paramasivam, P., Karuppasamy, S., & Chinnaiyan, P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309151 · Cited by 26

Confidence Interval for ${\bm {F_1}}$ Measure of Algorithm Performance Based on Blocked 3 $\bm {\times}$ 2 Cross-Validation

Wang, Y., Li, J., Li, Y., Wang, R., & Yang, X. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2359667 · Cited by 26

A Generative Approach Towards Improved Robotic Detection of Marine Litter

Hong, J., Fulton, M., & Sattar, J. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197575 · Cited by 26

Potential for individual tree monitoring in ponderosa pine dominated forests using unmanned aerial system structure from motion point clouds

Creasy, M. B., Tinkham, W. T., Hoffman, C. M., & Vogeler, J. C. (2021) · Canadian Journal of Forest Research · doi:10.1139/cjfr-2020-0433 · Cited by 26

UAV Based Remote Sensing for Tassel Detection and Growth Stage Estimation of Maize Crop Using Multispectral Images

Kumar, A., Taparia, M., Rajalakshmi, P., Guo, W., Balaji Naik, B., Marathi, B., & Desai, U. B. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323266 · Cited by 26

Human-recognizable CT image features of subsolid lung nodules associated with diagnosis and classification by convolutional neural networks

Jiang, B., Zhang, Y., Zhang, L., de Bock, G. H., Vliegenthart, R., & Xie, X. (2021) · European Radiology · doi:10.1007/s00330-021-07901-1 · Cited by 27

Research on state prediction method of tobacco curing process based on model fusion

Wang, Y., & Qin, L. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03129-5 · Cited by 28

CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Lee, T., Yoon, Y., Chun, C., & Ryu, S.-K. (2021) · Electronics · doi:10.3390/electronics10121402 · Cited by 26

Estimating the cover of Phragmites australis using unmanned aerial vehicles and neural networks in a semi‐arid wetland

Higgisson, W., Cobb, A., Tschierschke, A., & Dyer, F. J. (2021) · River Research and Applications · doi:10.1002/rra.3832 · Cited by 28

Lung image segmentation based on DRD U-Net and combined WGAN with Deep Neural Network

Lian, L., Luo, X., Pan, C., Huang, J., Hong, W., & Xu, Z. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107097 · Cited by 26

Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy

Singh, A., Chakraborty, S., He, Z., Tian, S., Zhang, S., Lustria, M. L. A., Charness, N. H., Roque, N. A., Harrell, E. R., & Boot, W. R. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.980778 · Cited by 26

Identifying Oceanic Eddy With an Edge-Enhanced Multiscale Convolutional Network

Hang, R., Li, G., Mei, X., Dong, C., & Wei, J. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3215696 · Cited by 26

RSCDNet: A Robust Deep Learning Architecture for Change Detection From Bi-Temporal High Resolution Remote Sensing Images

Deepanshi, Barkur, R., Suresh, D., Lal, S., Reddy, C. S., & Diwakar, P. G. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3230941 · Cited by 26

An Accurate Detection Model of Takifugu rubripes Using an Improved YOLO-V7 Network

Zhou, S., Cai, K., Feng, Y., Tang, X., Pang, H., He, J., & Shi, X. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11051051 · Cited by 26

State recognition and temperature rise time prediction of tobacco curing using multi-sensor data-fusion method based on feature impact factor

Pei, W. W., Zhou, P., Huang, J., Sun, G., & Liu, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121591 · Cited by 26

Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction

Moon, H. H., Jeong, J., Park, J. E., Kim, N., Choi, C., Kim, Y., Song, S. W., Hong, C., Kim, J. H., & Kim, H. S. (2024) · Neuro-Oncology · doi:10.1093/neuonc/noae012 · Cited by 27

Logo detection with extendibility and discrimination

Li, K.-W., Chen, S., Su, S., Duh, D., Zhang, H., & Li, S. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1449-1 · Cited by 26

Hierarchical text categorization and its application to bioinformatics

Kiritchenko, S. (2006) · doi:10.20381/ruor-12881 · Cited by 26

Continuous detection and recognition of actions of interest among actions of non-interest using a depth camera

Dawar, N., & Kehtarnavaz, N. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8297079 · Cited by 26

Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring

Savinelli, B., Tagliabue, G., Vignali, L., Garzonio, R., Gentili, R., Panigada, C., & Rossini, M. (2024) · Drones · doi:10.3390/drones8120744 · Cited by 25

A Bayesian Hierarchical Model for Comparing Average F1 Scores

Zhang, D., Wang, Y.-X., Zhao, X., & Wang, X. (2015) · Proceedings · doi:10.1109/icdm.2015.44 · Cited by 25

The impact of artificial intelligence and big data on end-stage kidney disease treatments

Diez-Sanmartín, C., Cabezuelo, A. S., & Belmonte, A. A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115076 · Cited by 24

How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation

Hao, Z., Post, C. J., Mikhailova, E. A., Lin, L., Liu, J., & Yu, K. (2022) · Remote Sensing · doi:10.3390/rs14071561 · Cited by 25

Knowledge‐Informed Deep Neural Networks for Solar Flare Forecasting

Li, M., Cui, Y., Luo, B., Ao, X., Liu, S., Wang, J., Li, S., Du, C., Sun, X., & Wang, X. (2022) · Space Weather · doi:10.1029/2021sw002985 · Cited by 30

Explainable artificial intelligence model to predict brain states from fNIRS signals

Shibu, C. J., Sreedharan, S., Arun, K., Kesavadas, C., & Sitaram, R. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.1029784 · Cited by 26

A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

Khan, P. W., Byun, Y. C., & Jeong, O. (2023) · Scientific Reports · doi:10.1038/s41598-023-35476-y · Cited by 25

ILP-based concept discovery in multi-relational data mining

Kavurucu, Y., Senkul, P., & Toroslu, İ. H. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.02.100 · Cited by 25

The Market for Heritage: Evidence From eBay Using Natural Language Processing

Altaweel, M. R. (2019) · Social Science Computer Review · doi:10.1177/0894439319871015 · Cited by 25

On Variant Discovery in Genomes of Fungal Plant Pathogens

Potgieter, L., Feurtey, A., Dutheil, J. Y., & Stukenbrock, E. H. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00626 · Cited by 25

Deep Consensus Network: Aggregating predictions to improve object detection in microscopy images

Wollmann, T., & Rohr, K. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102019 · Cited by 26

A deep learning semantic segmentation architecture for COVID ‐19 lesions discovery in limited chest CT datasets

Khalifa, N. E. M., Manogaran, G., Taha, M. H. N., & Loey, M. (2021) · Expert Systems · doi:10.1111/exsy.12742 · Cited by 25

Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas–Solid Fluidized Beds

Jiang, S., Wu, K., Francia, V., Ouyang, Y., & Coppens, M. (2024) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c00631 · Cited by 28

Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers

Ashraf, J., Raza, G. M., Kim, B.-S., Wahid, A., & Kim, H.-Y. (2025) · Applied Sciences · doi:10.3390/app15042043 · Cited by 24

Zero-shot fault diagnosis using soft semantic embedding of diffusion-encoded probability

Li, C., Yan, L., Wang, P., Long, J., & Pu, Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103319 · Cited by 25

Decentralized and coordinate-free computation of critical points and surface networks in a discretized scalar field

Jeong, M., Duckham, M., Kealy, A., Miller, H. J., & Peisker, A. (2013) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2013.801578 · Cited by 24

Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

Brester, C., Semenkin, E. S., & Yuryevich, S. M. (2016) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.1515/jaiscr-2016-0018 · Cited by 24

Scan-Flood Fill(SCAFF): An Efficient Automatic Precise Region Filling Algorithm for Complicated Regions

He, Y., Hu, T., & Zeng, D. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00104 · Cited by 24

Primary Categorizing and Masking Cerebral Small Vessel Disease Based on “Deep Learning System”

Duan, Y., Shan, W., Liu, L., Wang, Q., Wu, Z., Liu, P., Ji, J., Liu, Y., He, K., & Wang, Y. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.00017 · Cited by 24

A structure-function knowledge extraction method for bio-inspired design

Chen, C., Ye, T., Li, Y., Liu, Q., Li, S., & Tang, Z. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103402 · Cited by 24

A Two-Phase Fashion Apparel Detection Method Based on YOLOv4

Lee, C.-H., & Lin, C.-W. (2021) · Applied Sciences · doi:10.3390/app11093782 · Cited by 28

Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study

García-Zamalloa, A., Vicente, D. A., Arnay, R., Arrospide, A., Taboada, J., Castilla‐Rodríguez, I., Aguirre, U., Múgica, N., Aldama, L., Aguinagalde, B., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259203 · Cited by 24

A Context-Independent Ontological Linked Data Alignment Approach to Instance Matching

Barbosa, A., Bittencourt, I. I., Siqueira, S. W. M., Dermeval, D., & da Cruz, N. J. T. (2022) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.295977 · Cited by 24

Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China

Yang, R., Luo, F., Ren, F., Huang, W., Li, Q., Du, K., & Yuan, D. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11020131 · Cited by 25

Computing Precision and Recall with Missing or Uncertain Ground Truth

Lamiroy, B., & Sun, T. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-36824-0_15 · Cited by 22

What Is the Optimal Ranking Score Between Precision and Recall? We Can Always Find It and It Is Rarely $F_1$Preprint

Piérard, S. L., Deliège, A., & Droogenbroeck, M. V. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2511.22442

Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models

Yacouby, R., & Axman, D. (2020) · Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) · doi:10.18653/v1/2020.eval4nlp-1.9 · Cited by 858

Extending F1 Metric, Probabilistic Approach

Sitarz, M. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1161 · Cited by 52

Evaluation: From Precision, Recall and F-Factor

Roc, T., & Powers, D. M. W. (2007)