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Detection and prediction of foam evolution during the bottling of noncarbonated beverages using artificial neural networks

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Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Wang, Z., Wang, J., Lin, C., Yan, H., Wang, Z., & Ji, L. (2021) · Animals · doi:10.3390/ani11051263 · Cited by 15 · see citing works

Assessment of the resilience of pedestrian roads based on image deep learning models

Ku, D., Choi, M., Oh, H., Shin, S., & Lee, S. (2022) · Proceedings of the Institution of Civil Engineers - Municipal Engineer · doi:10.1680/jmuen.21.00037 · Cited by 15 · see citing works

Pattern Recognition Method for Detecting Engineering Errors on Technical Drawings

Джусупова, Р., Banotra, R., Bosch, J., & Olsson, H. H. (2022) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot54504.2022.9817294 · Cited by 15 · see citing works

Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study

Lei, C., Jiang, Y., Xu, K., Liu, S., Cao, H., & Wang, C. (2025) · JMIR Medical Informatics · doi:10.2196/62774 · Cited by 14 · see citing works

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

Kitzes, J. A., Blake, R. E., Bombaci, S., Chapman, M., Durán, S. M., Huang, T., Joseph, M. B., Lapp, S., Marconi, S., Oestreich, W. K., et al. (2021) · Ecosphere · doi:10.1002/ecs2.3795 · Cited by 14 · see citing works

Occluded object detection and exposure in cluttered environments with automated hyperspectral anomaly detection

Hanson, N., Lvov, G., & Padır, T. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.982131 · Cited by 14 · see citing works

Fit-For-Purpose Creativity Assessment: Automatic Scoring of the Test of Creative Thinking – Drawing Production (TCT-DP)

Cropley, D. H., Theurer, C., Mathijssen, A. C. S., & Marrone, R. L. (2024) · Creativity Research Journal · doi:10.1080/10400419.2024.2339667 · Cited by 18 · see citing works

Applying Knowledge-Based and Data-Driven Methods to Improve Ore Grade Control of Blast Hole Drill Cuttings Using Hyperspectral Imaging

Akbar, S., Abdolmaleki, M., Ghadernejad, S., & Esmaeili, K. (2024) · Remote Sensing · doi:10.3390/rs16152823 · Cited by 13 · see citing works

Desert bighorn sheep (Ovis canadensis) recognition from camera traps based on learned features

Vargas-Felipe, M., Pellegrin, L., Guevara-Carrizales, A. A., López-Monroy, A. P., Escalante, H. J., & Gonzalez-Fraga, J. A. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101328 · Cited by 12 · see citing works

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

Alfonso-Francia, G., Pedraza‐Ortega, J. C., Badillo, M., Toledano‐Ayala, M., Aceves-Fernández, M. A., Rodríguez‐Reséndiz, J., Ko, S., & Tovar‐Arriaga, S. (2022) · Diagnostics · doi:10.3390/diagnostics12123031 · Cited by 12 · see citing works

Animals and climate change: A visual and discourse network analysis of Instagram posts

Koop‐Monteiro, Y., Stoddart, M. C. J., & Tindall, D. B. (2023) · Environmental Sociology · doi:10.1080/23251042.2023.2216371 · Cited by 11 · see citing works

Hydraulic fracturing performance analysis by the mutual information and Gaussian process regression methods

Xu, Y., Liu, D., Xu, W., He, D., & Hao, H. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109285 · Cited by 11 · see citing works

An Assessment of Human Inspection and Deep Learning for Defect Identification in Floral Wreaths

Caballero-Ramírez, D., Baez-Lopez, Y. A., Limón-Romero, J., Tortorella, G. L., & Tlapa, D. (2023) · Horticulturae · doi:10.3390/horticulturae9111213 · Cited by 10 · see citing works

Machine learning-based wood anatomy identification: towards anatomical feature recognition

He, X., Pelt, D. M., Gao, J., Gravendeel, B., Zhu, P., Chen, S., Qiu, J., & Lens, F. (2024) · IAWA Journal - KU Leuven/IAWA Journal · doi:10.1163/22941932-bja10157 · Cited by 10 · see citing works

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

Schneider, S., von Fersen, L., & Dierkes, P. W. (2024) · Frontiers in Conservation Science · doi:10.3389/fcosc.2024.1405243 · Cited by 10 · see citing works

Monitoring of Electrophysiological Functions in Brain‐on‐a‐Chip and Brain Organoids

Song, J., Jeong, H. E., Choi, A. H., & Kim, H. N. (2024) · Advanced NanoBiomed Research · doi:10.1002/anbr.202400052 · Cited by 11 · see citing works

Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning

Alani, S., Guo, H., Fyfe, S., Long, Z., Donnaz, S., & Kim, Y. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.124803 · Cited by 10 · see citing works

Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning

Signaroli, M., Lana, A., Martorell‐Barceló, M., Sanllehi, J., Barcelo‐Serra, M., Aspillaga, E., Mulet, J., & Alós, J. (2022) · PeerJ · doi:10.7717/peerj.13396 · Cited by 10 · see citing works

A Study on Sample Size Sensitivity of Factory Manufacturing Dataset for CNN-Based Defective Product Classification

Kim, D., Seo, S. B., Yoo, N., & Shin, G. (2022) · Computation · doi:10.3390/computation10080142 · Cited by 10 · see citing works

A novel application of deep learning to forensic hair analysis methodology

Airlie, M., Robertson, J. A., Ma, W., Airlie, D., & Brooks, E. (2022) · Australian Journal of Forensic Sciences · doi:10.1080/00450618.2022.2159064 · Cited by 9 · see citing works

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes

Javed, N., López‐Denman, A. J., Paradkar, P. N., & Bhatti, A. (2024) · Acta Tropica · doi:10.1016/j.actatropica.2024.107468 · Cited by 9 · see citing works

An automatic identification method of common species based on ensemble learning

Li, H., Zhang, M., Meng, D.-Y., Bo, G., Li, Z., Huang, C.-F., Li, W.-K., Jiang, H.-L., Wu, R., Li, X., et al. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103046 · Cited by 9 · see citing works

On herbarium specimen images and artificial intelligence

Tessler, M., & Little, D. P. (2025) · New Phytologist · doi:10.1111/nph.70312 · Cited by 10 · see citing works

Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images

Gong, E. J., Bang, C. S., Lee, J. J., Yang, Y. J., & Baik, G. H. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12091361 · Cited by 9 · see citing works

An ingenious deep learning approach for pressure injury depth evaluation with limited data

Ikuta, K., Fukuoka, K., Kimura, Y., Nakagaki, M., Ohga, M., Suyama, Y., Morita, M., Umeda, R., Konishi, M., Nishikawa, H., et al. (2024) · Journal of Tissue Viability · doi:10.1016/j.jtv.2024.05.009 · Cited by 9 · see citing works

Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time

Gao, X. W., Taylor, S., Pang, W., Hui, R., Lü, X., & Braden, B. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.023 · Cited by 9 · see citing works

Human Supervision is Key to Achieving Accurate AI-assisted Wildlife Identifications in Camera Trap Images

Huebner, S. E., Palmer, M. S., & Packer, C. (2024) · Citizen Science Theory and Practice · doi:10.5334/cstp.752 · Cited by 8 · see citing works

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis

Chong, L., Cao, T., Tang, M., Pu, W., & Lei, P. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112060 · Cited by 8 · see citing works

Evaluation methods of pressure injury stages: A systematic review and meta-analysis

Chao, Q., Pei, J., Wei, Y., Yang, Z., Wang, X., Li, D., & Han, L. (2025) · Journal of Tissue Viability · doi:10.1016/j.jtv.2025.100894 · Cited by 8 · see citing works

Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs

Abidoye, I., Ikeji, F., Coupland, C. A., Calaminus, S. D. J., Sander, N., & de Sousa, E. M. V. (2025) · Journal of Imaging · doi:10.3390/jimaging11060183 · Cited by 8 · see citing works

Dataset Transformation System for Sign Language Recognition Based on Image Classification Network

Choi, S.-G., Park, Y., & Sohn, C.-B. (2022) · Applied Sciences · doi:10.3390/app121910075 · Cited by 8 · see citing works

Classification of Kidney Tumor Grading on Preoperative Computed Tomography Scans

Mahootiha, M., Qadir, H. A., Bergsland, J., & Balasingham, I. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-34586-9_6 · Cited by 8 · see citing works

Semantic segmentation framework for atoll satellite imagery: An in-depth exploration using UNet variants and Segmentation Gym

Wang, R., Chowdhury, T., & Ortiz, A. C. (2024) · Applied Computing and Geosciences · doi:10.1016/j.acags.2024.100217 · Cited by 7 · see citing works

IoT Enabled Smart Agriculture System for Detection and Classification of Tomato and Brinjal Plant Leaves Disease

Kasera, R. K., Nath, S., Das, B., Kumar, A., & Acharjee, T. (2025) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v26i1.3826 · Cited by 7 · see citing works

Unveiling the impact of dataset size on machine learning models for anxiety and depression prediction amid the COVID-19 pandemic: determining optimal data collection thresholds

Arora, P., & Dahiya, S. (2025) · Current Psychology · doi:10.1007/s12144-025-07432-8 · Cited by 7 · see citing works

A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition

Heidari, H., Murugappan, M., Mohammed, J. S., & Chowdhury, M. E. H. (2025) · IEEE Access · doi:10.1109/access.2025.3560362 · Cited by 7 · see citing works

Annotation Quality Versus Quantity for Object Detection and Instance Segmentation

Agnew, C., Scanlan, A., Denny, P., Grua, E. M., van de Ven, P. W. J., & Eising, C. (2024) · IEEE Access · doi:10.1109/access.2024.3467008 · Cited by 7 · see citing works

Custom Based Obstacle Detection Using Yolo v3 for Low Flying Drones

Aswini, N., & Uma, S., V (2021) · International Conference on Circuits, Controls and Communications (CCUBE) · doi:10.1109/ccube53681.2021.9702739 · Cited by 7 · see citing works

A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

Ropelewska, E., Sabancı, K., Aslan, M. F., & Azizi, A. (2022) · Horticulturae · doi:10.3390/horticulturae8050431 · Cited by 7 · see citing works

Testing the detection of large, secretive snakes using camera traps

Walkup, D. K., Ryberg, W. A., Pierce, J. B., Smith, E., Childress, J., East, F., Pierce, B. L., Brown, P., Fielder, C. M., & Hibbitts, T. J. (2022) · Wildlife Society Bulletin · doi:10.1002/wsb.1408 · Cited by 7 · see citing works

Sampling biases across interaction types affect the robustness of ecological multilayer networks

Hervías‐Parejo, S., Traveset, A., Nogales, M., Heleno, R., Llewelyn, J., & Strona, G. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103183 · Cited by 6 · see citing works

Weed detection in cabbage fields using RGB and NIR images

Hruška, A., Hamouz, P., Lev, J., Pavlíček, J., Kroulík, M., Hamouzová, K., Košnarová, P., Holec, J., & Kouřím, P. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101232 · Cited by 6 · see citing works

A New Framework Containing Convolution and Pooling Circuits for Image Processing and Deep Learning Applications with Quantum Computing Implementation

Yetış, H., & Karaköse, M. (2022) · Traitement du signal · doi:10.18280/ts.390212 · Cited by 6 · see citing works

Monitoring tropical freshwater fish with underwater videography and deep learning

Jansen, A., van Bodegraven, S., Esparon, A. J., Gadhiraju, V., Walker, S., Buccella, C., Bock, K., Loewensteiner, D. A., Mooney, T. J., Harford, A. J., et al. (2024) · Marine and Freshwater Research · doi:10.1071/mf23166 · Cited by 6 · see citing works

Early and non-destructive prediction of the differentiation efficiency of human induced pluripotent stem cells using imaging and machine learning

Hojo, M. A., Tsuzuki, T., Ozawa, Y., Araki, T., & Sakurai, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-11108-5 · Cited by 6 · see citing works

Detection and Classification of Human-Carrying Baggage Using DenseNet-161 and Fit One Cycle

Ramadan, M. K., Youssif, A. A. A., & El-Behaidy, W. H. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040108 · Cited by 6 · see citing works

Instance segmentation of oyster mushroom datasets: A novel data sampling methodology for training and evaluation of deep learning models

Charisis, C., Wang, M., & Argyropoulos, D. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101146 · Cited by 5 · see citing works

Copy‐Paste Augmentation Improves Automatic Species Identification in Camera Trap Images

Mesnage, C. S., Corbett, A., Curry, J., Rowlands, S., Dutta, A., Everson, R., & Simmons, B. I. (2025) · Ecology and Evolution · doi:10.1002/ece3.72357 · Cited by 5 · see citing works

APD-YOLOv7: Enhancing Sustainable Farming through Precise Identification of Agricultural Pests and Diseases Using a Novel Diagonal Difference Ratio IOU Loss

Li, J., Liu, S., Chen, D., Zhou, S., & Li, C. (2024) · Sustainability · doi:10.3390/su16208855 · Cited by 5 · see citing works

Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites

Fu, H., Ding, Z., & Wang, W. (2024) · Methods · doi:10.1016/j.ymeth.2024.12.010 · Cited by 5 · see citing works

Sounds of the deep: How input representation, model choice, and dataset size influence underwater sound classification performance

Olcay, A., White, P. R., Bull, J. M., Risch, D., Dell, B. L., & White, E. L. (2025) · The Journal of the Acoustical Society of America · doi:10.1121/10.0036498 · Cited by 5 · see citing works

Deep learning‐based ecological analysis of camera trap images is impacted by training data quality and quantity

Bevan, P. A., Pantazis, O., Pringle, H. A. I., Ferreira, G. B., Ingram, D. J., Madsen, E. K., Thomas, L., Thanet, D. R., Silwal, T., Rayamajhi, S., et al. (2026) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.70052 · Cited by 10 · see citing works

Automated single species identification in camera trap images: architecture choice, training strategies, and the interpretation of performance metricsPreprint

Burkard, Y., Francazi, E., Lavender, E., Dubach, T., Wehrli, S., Brodesen, J., Volpi, M., Baity‐Jesi, M., & Moor, H. (2024) · doi:10.32942/x2vw4v

Three critical factors affecting automated image species recognition performance for camera traps

Schneider, S., Greenberg, S., Taylor, G. W., & Kremer, S. C. (2020) · Ecology and Evolution · doi:10.1002/ece3.6147 · Cited by 161 · see citing works

Automated wildlife image classification: An active learning tool for ecological applications

Bothmann, L., Wimmer, L., Charrakh, O., Weber, T. R., Edelhoff, H., Peters, W., Nguyen, H., Benjamin, C., & Menzel, A. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102231 · Cited by 49 · see citing works

The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Rigoudy, N., Dussert, G., Benyoub, A., Besnard, A. G., Birck, C., Boyer, J., Bollet, Y., Bunz, Y., de la Peña, G. C. G., Chetouane, E., et al. (2023) · European Journal of Wildlife Research · doi:10.1007/s10344-023-01742-7 · Cited by 53 · see citing works