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Malware Detection Using Deep Learning and Correlation-Based Feature Selection

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Stem–Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data

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How Reliable is Distribution of Relaxation Times (Drt) Analysis? A Dual Regression-Classification Perspective on Drt Estimation, Interpretation, and Accuracy

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Re-evaluating Evaluation in Text Summarization

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Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

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Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning

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A Multi-Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing, Internet of Things (IoT) and Machine Learning

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RF-Based UAV Detection and Identification Using Hierarchical Learning Approach

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Spiking Neural Networks applied to the classification of motor tasks in EEG signals

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Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data

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Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

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Homologous organization of cerebellar pathways to sensory, motor, and associative forebrain

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A novel framework for image-based plant disease detection using hybrid deep learning approach

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Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients

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Automatic Detection of Schizophrenia by Applying Deep Learning over Spectrogram Images of EEG Signals

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Artificial Intelligence Technique for Gene Expression by Tumor RNA-Seq Data: A Novel Optimized Deep Learning Approach

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The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo

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Prediction of Water Quality Classification of the Kelantan River Basin, Malaysia, Using Machine Learning Techniques

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Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds

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Identification of Cotton Leaf Lesions Using Deep Learning Techniques

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Multi-Resolution CNN and Knowledge Transfer for Candidate Classification in Lung Nodule Detection

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Deep Representation Learning With Full Center Loss for Credit Card Fraud Detection

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Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

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Climate-driven aerobic habitat loss in the California Current System

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A new Arabic handwritten character recognition deep learning system (AHCR-DLS)

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Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine

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Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests

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Smart-surface: Large scale textile pressure sensors arrays for activity recognition

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Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

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Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks

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Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees’ crowns from UAV multispectral imagery

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Cross-Modality Hierarchical Clustering and Refinement for Unsupervised Visible-Infrared Person Re-Identification

Pang, Z., Wang, C., Zhao, L., Liu, Y., & Gaurav Sharma, A. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3310015 · Cited by 111

Mango leaf disease identification and classification using a CNN architecture optimized by crossover-based levy flight distribution algorithm

Prabu, M., & Chelliah, B. J. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06726-9 · Cited by 103

Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators

Cabana, E., Lillo, R. E., & Laniado, H. (2019) · Statistical Papers · doi:10.1007/s00362-019-01148-1 · Cited by 102

How to assess the prediction accuracy of species presence–absence models without absence data?

Li, W., & Guo, Q. (2013) · Ecography · doi:10.1111/j.1600-0587.2013.07585.x · Cited by 102

Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami

Bai, Y., Mas, E., & Koshimura, S. (2018) · Remote Sensing · doi:10.3390/rs10101626 · Cited by 96

A Sliding Window Variational Outlier-Robust Kalman Filter Based on Student’s t-Noise Modeling

Zhu, F., Huang, Y., Xue, C., Mihaylova, L. S., & Chambers, J. A. (2022) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2022.3164012 · Cited by 102

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

Hayat, A., Morgado‐Dias, F., Bhuyan, B. P., & Tomar, R. (2022) · Information · doi:10.3390/info13060275 · Cited by 107

Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods

Li, J., Chen, Z., Li, X., Jing, L., Zhang, Y., Xiao, H., Wang, S., Yang, W., Wu, L.-J., Li, P., et al. (2023) · Underground Space · doi:10.1016/j.undsp.2023.01.001 · Cited by 102

Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

Shin, P., Sankey, T. T., Moore, M. M., & Thode, A. E. (2018) · Remote Sensing · doi:10.3390/rs10081266 · Cited by 97

Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

Pashaei, M., Kamangir, H., Starek, M. J., & Tissot, P. E. (2020) · Remote Sensing · doi:10.3390/rs12060959 · Cited by 104

Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests

Xu, Z., Shen, X., Cao, L., Coops, N. C., Goodbody, T. R. H., Tai, Z., Zhao, W., Sun, Q., Ba, S., Zhang, Z., et al. (2020) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2020.102173 · Cited by 106

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)