“How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204

Generalized Additive Models: An Introduction with R

Wood, S. N. (2017) · doi:10.1201/9781315370279 · Cited by 13,852

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653

Generalized Additive Models

Hastie, T., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013604 · Cited by 12,575

Generalized Additive Models: An Introduction With R

Chiang, A. Y. (2007) · Technometrics · doi:10.1198/tech.2007.s505 · Cited by 8,532

Generalized Additive Models.

Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263

Developing custom computer vision models with Njobvu‐ AI : A collaborative, user‐friendly platform for ecological research

Appel, C. L., Subramanian, A., Koning, J. S., Ngosi, M. S., Sullivan, C. M., Levi, T., & Lesmeister, D. B. (2025) · Ecological Applications · doi:10.1002/eap.70096 · Cited by 5

Using UAVs and Machine Learning for Nothofagus alessandrii Species Identification in Mediterranean Forests

Cabrera-Ariza, A. M., Aguilera, M., Henríquez-Hernández, P. V., & Santelices-Moya, R. (2023) · Drones · doi:10.3390/drones7110668 · Cited by 5

A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3

Zhang, H., Wang, B., Tang, Z., Xue, J., Chen, R.-H., Kan, H.-P., Lu, S., Feng, L., He, Y., & Yi, S. (2024) · Frontiers in Earth Science · doi:10.1007/s11707-024-1109-y · Cited by 6

Topographic knowledge-aware network for automatic small-scale impact crater detection from lunar digital elevation models

Yang, J., Shuowei, Z., Lin, L., Kang, Z., & Ma, Y. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103831 · Cited by 5

Image Processing of Insulator and Vibration Damper by YOLO Algorithm

Sunduijav, C., Hardt, W., & Bayasgalan, Z. (2021) · International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE) · doi:10.1109/apeie52976.2021.9647507 · Cited by 5

A Large Crowdsourced Street View Dataset for Mapping Road Surface Types in Africa

Zhou, Q., Duan, J., Qiao, J., Liu, Z., & Yang, H. W. (2025) · Scientific Data · doi:10.1038/s41597-025-05153-y · Cited by 5

Semi‐Automated Detection of Right Whales ( Eubalaena spp. ) in Very High‐Resolution Satellite Imagery

Davies, K. T. A., Webster, A., Babu, V., Brillant, S. W., Carlyle, C. G., Lonati, G. L., Sharma, H., & Tsui, O. W. (2025) · Marine Mammal Science · doi:10.1111/mms.70024 · Cited by 4

The Sentinel Bait Station: an automated, intelligent design pest animal baiting system

Charlton, G., Falzon, G., Shepley, A., Fleming, P. J. S., Ballard, G., & Meek, P. D. (2023) · Wildlife Research · doi:10.1071/wr22183 · Cited by 4

Turbidivision: a machine vision application for estimating turbidity from underwater images

Rudy, I. M., & Wilson, M. J. (2024) · PeerJ · doi:10.7717/peerj.18254 · Cited by 4

Impact of Magnification, Image Type, and Number on Convolutional Neural Network Performance in Differentiating Canine Large Cell Lymphoma From Non‐Lymphoma via Lymph Node Cytology

Pacholec, C., Xie, H., Curnin, J., Lin, A., & Zimmerman, K. (2025) · Veterinary Clinical Pathology · doi:10.1111/vcp.70056 · Cited by 4

Estimating species richness with camera traps: modeling the effects of delay period, deployment length, number of sites, and interference imagery

Mashintonio, A. F., Harris, G. M., Stewart, D. R., Butler, M. J., Sanderson, J., & Russell, G. J. (2022) · Wildlife Society Bulletin · doi:10.1002/wsb.1357 · Cited by 4

Anomaly Detection in Spacecraft Telemetry: Forecasting vs. Classification

Lakey, D. T., & Schlippe, T. (2024) · IEEE Space Computing Conference (SCC) · doi:10.1109/scc61854.2024.00009 · Cited by 4

Classifying early-stage soybean fungal diseases on hyperspectral images using convolutional neural networks

Hsiao, C. F., Feyrer, G., & Stein, A. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101023 · Cited by 4

Study of CNN Capacity Applied to Left Ventricle Segmentation in Cardiac MRI

de Toledo, M. A. F., de Lima, D. M., Krieger, J. E., & Gutierrez, M. A. (2021) · SN Computer Science · doi:10.1007/s42979-021-00897-x · Cited by 4

Particulate Matter Levels Classification Using Modified and Combined ResNet Models with Low Features Extraction

Matloob, R. A., & Shakir, M. A. (2023) · 3C TIC Cuadernos de desarrollo aplicados a las TIC · doi:10.17993/3ctic.2023.121.378-398 · Cited by 3

Solving Non-IID in Federated Learning for Image Classification using GANs

Chuenbubpha, T., Boonchoo, T., Haga, J., & Rattanatamrong, P. (2023) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse58229.2023.10202100 · Cited by 4

Development of Nipple Trauma Evaluation System With Deep Learning

Nakamura, M., Sugimori, H., & Ebina, Y. (2024) · Journal of Human Lactation · doi:10.1177/08903344241303867 · Cited by 6

A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords

Turki, H., Chebil, K., Dossou, B. F. P., Emezue, C. C., Owodunni, A. T., Taieb, M. A. H., & Aouicha, M. B. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38448 · Cited by 3

Semantic segmentation using synthetic images of underwater marine-growth

Mai, C., Liniger, J., & Pedersen, S. A. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1459570 · Cited by 3

Effects of input-image size on performance of fish detection and species classification using deep learning

Iwahara, Y., Shibata, Y., Manano, M., Nishino, T., Kariya, R., & Yaemori, H. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103566 · Cited by 3

Identification of honey bee (Apis mellifera) larvae in the hive with faster R-CNN for royal jelly production

Güneş, H., & Gungormus, A. (2022) · Journal of Apicultural Research · doi:10.1080/00218839.2022.2030023 · Cited by 3

Self-supervised Learning for COVID-19 Detection from Chest X-ray Images

Feki, I., Ammar, S., & Kessentini, Y. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-08277-1_7 · Cited by 3

Comparative Study on Predicting Student Grades using Five Machine Learning Algorithms

Lee, H.-J. (2022) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2022.20.11.09 · Cited by 3

Deep learning for the screening of primary ciliary dyskinesia based on cranial computed tomography

Duan, B., Lv, H., Huang, Y., Xu, Z., & Chen, W. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1098893 · Cited by 3

Change detection in moving-camera videos with limited samples using twin-CNN features and learnable morphological operations

Padilla, R., da Silva, A. F., da Silva, E. A. B., & Netto, S. L. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2023.116969 · Cited by 3

Low-Cost Object Detection Models for Traffic Control Devices through Domain Adaption of Geographical Regions

Oh, D., Kang, K., Seo, S., Xiao, J., Jang, K., Kim, K., Park, H., & Won, J.-H. (2023) · Remote Sensing · doi:10.3390/rs15102584 · Cited by 3

Australian Animal Species Selection and Image Data Collection

Zhang, Q., Ahmed, K. E. U., Sharda, N. K., & Wang, H. (2023) · International Conference on Information Visualisation (IV) · doi:10.1109/iv60283.2023.00020 · Cited by 3

A practical guide to (successfully) collect and process images through online surveys

Iglesias, P. A., Ochoa, C., & Revilla, M. (2023) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2023.100792 · Cited by 3

Mitigating the influence of topographies on hyperspectral images for the sorting of iron ore based on variational mode decomposition and spectral property

Nie, C., Li, K., Jiang, J., Jia, L., & Sun, T. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.105109 · Cited by 3

Early Plant Disease Detection Using Infrared and Mobile Photographs in Natural Environment

Silva, M. D., & Brown, D. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-37717-4_21 · Cited by 3

A semi-automatic workflow to process camera trap images in RPreprint

Böhner, H., Kleiven, E. F., Ims, R. A., & Soininen, E. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.05.510927 · Cited by 3

Transforming crocodile traceability: Deep metric learning for identifying Siamese crocodiles

Treeprapin, K., Kaewtip, K., Singchat, W., Ariyaraphong, N., Panthum, T., Duengkae, P., Temsiripong, Y., Srikulnath, K., & Trirongjitmoah, S. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102771 · Cited by 2

Improving the Accuracy of Diagnostic Imaging using Artificial Intelligence: A Method for Assessing Necrotic Tissue in Pressure Injury

Kimura, Y., Ikuta, K., Ohga, M., Umeda, R., Nakagaki, M., Suyama, Y., Kanayama, H., Konishi, M., Nishikawa, H., & Yagi, S. (2025) · Yonago acta medica · doi:10.33160/yam.2025.08.014 · Cited by 2

Computer vision‐based recognition and distinction of Arabidopsis thaliana ecotypes using supervised deep learning models

Sarić, R., Custovic, E., Akagić, A., Trtílek, M., Lewsey, M. G., & Whelan, J. (2025) · The Plant Phenome Journal · doi:10.1002/ppj2.70041 · Cited by 2

Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: A systematic review

Rambabu, L., Edmiston, T., Smith, B. G., Kohler, K., Kolias, A. G., Bethlehem, R. A. I., Keane, P. A., Marcus, H. J., Hutchinson, P. J., & Bashford, T. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000783 · Cited by 2

DuckNet : an open‐source deep learning tool for waterfowl species identification in UAV imagery

Loken, Z., Ringelman, K. M., Mini, A. E., James, J. D., & Mitchell, M. (2025) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.70028 · Cited by 2

Predicting physical activity types in children aged 3–6 from video data using computer vision

Ma, X., Zhao, G., Su, L., Fu, J., Sun, S., Yuan, Y., He, Z., Huang, T. M., Hu, X., Li, Y., et al. (2026) · BMC Public Health · doi:10.1186/s12889-026-26495-1 · Cited by 2

Multi-Objective Evaluation of Lightweight AI Models on Low-Cost Edge Devices Using Pareto Fronts

Rojas-Carrasco, P., & Guinaldo, M. (2026) · Applied Sciences · doi:10.3390/app16083679 · Cited by 2

How Knowledge-Driven Class Generalization Affects Classical Machine Learning Algorithms for Mono-label Supervised Classification

Turki, H., Taieb, M. A. H., & Aouicha, M. B. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96308-8_59 · Cited by 2

Soda maker for field anesthesia as a step towards a non-lethal identification of wild bees and other flower visitors

Toulzac, M., Methlouthi, M., & Perrard, A. (2022) · Osmia · doi:10.47446/osmia10.3 · Cited by 2

Using Vehicle Data to Enhance Prediction of Accident-Prone Areas

Wowo, K. S., Dadwal, R., Graen, T., Fiege, A., Nolting, M., Nejdl, W., Demidova, E., & Funke, T. (2022) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc55140.2022.9922236 · Cited by 2

Intelligent road surface autonomous inspection

Tovanche-Picon, H., Tena, L. A. G., Garcia-Teran, M. A., & Flores-Abad, Á. (2023) · Evolutionary Intelligence · doi:10.1007/s12065-023-00841-3 · Cited by 2

Detection of Lygus Bugs in Strawberry Using Deep Learning

Atefi, A., Ahmadi, M., & Lin, J. (2024) · International Journal of Fruit Science · doi:10.1080/15538362.2024.2438062 · Cited by 2

Circapproved: Digital Pattern Recognition via Artificial Neural Network for the Identification of Normal Penis Parameters for Circumcision Eligibility Using Mobile App

Wahyudi, I., Utomo, C. P., Djauzi, S., Fathurahman, M., Situmorang, G. R., Rodjani, A., Raharja, P. A. R., Yonathan, K., Santoso, B., Khresna, D., et al. (2025) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2025.162358 · Cited by 2

Geospatial Data and Google Street View Images for Monitoring Kudzu Vines in Small and Dispersed Areas

Closa-Tarres, A., Rojano, F., & Strager, M. P. (2025) · Earth · doi:10.3390/earth6020040 · Cited by 3

Deep learning-driven intelligent weed mapping system: Optimizing site-specific weed management

Wang, C., Chen, Z., Sun, D., He, J., Hou, P., Wang, Y., Xu, Z. H., Guo, Z., & Quan, L. (2025) · Crop Protection · doi:10.1016/j.cropro.2025.107284 · Cited by 2

A Meta-Learning Approach for Multicenter and Small-Data Single-Cell Image Analysis

Ye, L., Wang, W., Sun, H., Ye, W., Hou, Y., Zhang, Y., Zhang, Y., Ren, G., Gao, Z., & Qu, X. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c01810 · Cited by 3

Tasks performed in the legal domain through Deep Learning: A bibliometric review (1987–2020)

Montelongo, A., & Becker, J. L. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00113 · Cited by 4

Machine Learning-Based Fingerprinting Positioning in Massive MIMO Networks: Analysis on the Impact of Small Training Sample Size to the Positioning Performance

Lutakamale, A. S., & Manyesela, Y. Z. (2023) · SN Computer Science · doi:10.1007/s42979-023-01759-4 · Cited by 2

A Novel Method for Improving Baggage Classification Using a Hyper Model of Fusion of DenseNet-161 and EfficientNet-B5

Saleh, M. A., Abdouh, M., & Ramadan, M. K. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8100135 · Cited by 1

Optimized wild animal detection technique through effective features selection and classification by using convolutional gated recurrent network

Gundal, S. S., Gundal, S., Kanawade, M., Dighe, S., Phatangare, V., & Dighe, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20301-z · Cited by 1

Harnessing AI for Precise AGB Estimation in Remote Areas

Wijaya, F. W., Prakoso, M., Ramadhan, M. B. V., & Asyhari, T. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2530-9.ch004 · Cited by 1

Development of Machine Learning Wildlife Camera Using ESP32 CAM for Small Mammals

Xin, L. M., & Fadzly, N. (2025) · Pertanika journal of science & technology · doi:10.47836/pjst.33.4.05 · Cited by 2

Towards Edge-Based Idle State Detection in Construction Machinery Using Surveillance Cameras

Küpers, X., Brinke, J. K., Bemthuis, R., & İncel, Ö. D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00071-2_25 · Cited by 1

Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives

Kumar, S., Malviya, R., Sridhar, S. B., Shareef, J., & Wadhwa, T. (2025) · Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry · doi:10.2174/0118715230389406250906142050 · Cited by 1

Feasibility comparison of deep learning image regressions to estimate intracranial pressure from cranial computed tomography in hydrocephalus

Tunthanathip, T., Duangsoithong, R., & Sae-heng, S. (2025) · Journal of Neurosciences in Rural Practice · doi:10.25259/jnrp_131_2025 · Cited by 1

DCC: A Model-Free Frame to Evaluate Data Set Quality

Xie, C., & Li, Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02429 · Cited by 1

Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devices

Goetze, C., Schlippe, T., & Lakey, D. T. (2025) · IEEE Space Computing Conference (SCC) · doi:10.1109/scc66396.2025.00019 · Cited by 1

Patient Comfort With Smartphone-Based and Intraoral Camera Imaging in Teledentistry: Observational Cross-Sectional Study

Lucas, M. B., Huang, B., Ip, R. H. L., Hastings, A. M., Schultz, E. C., Chiang, Y., & Louie, J. P. (2026) · Online Journal of Public Health Informatics · doi:10.2196/95563 · Cited by 1

Chocolate Bushbabies: Cocoa Agroforests as Habitat for Galagos (Galagidae) in the Ashanti Region, Ghana

Hending, D., Anokye, J., Limweta, L., Yombu, M. A. R., Quainoo-Mensah, F., Gorleku, D. O., Butynski, T. M., de Jong, Y. A., Svensson, M. S., Reid, P., et al. (2026) · Ecology and Evolution · doi:10.1002/ece3.74026 · Cited by 1

RESEARCH ON THE IMPROVING RESOURCE PRODUCTIVITY OF ROCK FISH IN THE ARTIFICIAL REEFS USING AI

Yamamoto, T., Tamaki, T., Okazaki, S., Okazaki, Y., Yoshida, H., & Suenaga, Y. (2021) · Journal of Japan Society of Civil Engineers Ser B3 (Ocean Engineering) · doi:10.2208/jscejoe.77.2_i_709 · Cited by 1

DEVELOPMENT OF SYMBIOTIC TECHNOLOGY OF SEA URCHIN AND SEA WEED IN THE EARTHQUAKE DISASTER AREA

Yamamoto, T., TAMAKI, T., Okazaki, S., Yoshida, H., & SUENAGA, Y. (2022) · Journal of Japan Society of Civil Engineers Ser B3 (Ocean Engineering) · doi:10.2208/jscejoe.78.2_i_31 · Cited by 1

THE EFFECT OF CONTRAST ENHANCEMENT ON EPIPHYTE SEGMENTATION USING GENERATIVE NETWORK

Variyar, V. V. S., Sowmya, V., Sivanpillai, R., & Brown, G. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-m-3-2023-219-2023 · Cited by 1

An Image-Set of Coffee Berries for CNN Classification

Ngocho, B. M., Mwangi, E., Kamucha, G., & Jeon, G. (2023) · IEEE AFRICON · doi:10.1109/africon55910.2023.10293226 · Cited by 1

Adaptive Beamforming Technology Using Convolutional Neural Networks Based on Unsupervised Learning for Jamming Suppression

Yun, J., Lee, J., Joo, J., Jung, T., Park, J., & Lee, D. (2023) · The Journal of Korean Institute of Electromagnetic Engineering and Science · doi:10.5515/kjkiees.2023.34.12.927 · Cited by 1

Cardiovascular Disease Detection using Deep Learning

-, A. T., -, A. S., & -, M. L. S. K. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i01.13749 · Cited by 1

Potato powdery scab segmentation using improved GrabCut algorithm

Liu, R., Zhu, T., Wu, J., & Li, J. (2024) · Journal of Agricultural Engineering · doi:10.4081/jae.2024.1585 · Cited by 1

Image-Recognition-Based Embedded System for Excavator Bucket Tracking in Construction Sites

Shin, J., Park, H., Jeong, H., Jeong, H., & Chu, B. (2024) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-024-01025-4 · Cited by 1

Creating a Customized Dataset for Financial Pattern Recognition in Deep Learning

Bakai, M. E., Boutyour, Y., & Idrissi, A. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-65038-3_8 · Cited by 1

OSMIA 10 - FULL VOLUME - numéro complet

Osmia (2022) · Osmia · doi:10.47446/osmia10 · Cited by 1

Building exposure datasets using street-level imagery and deep learning object detection models

Cesarini, L., Figueiredo, R., Romão, X., & Martina, M. L. V. (2022) · EGU General Assembly Conference Abstracts · doi:10.5194/egusphere-egu22-9406

Peer Review #3 of "Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning (v0.2)"

(2022) · PeerJ · doi:10.7287/peerj.13396v0.2/reviews/3

Peer Review #2 of "Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning (v0.1)"

(2022) · PeerJ · doi:10.7287/peerj.13396v0.1/reviews/2

Peer Review #2 of "Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning (v0.2)"

(2022) · PeerJ · doi:10.7287/peerj.13396v0.2/reviews/2

Peer Review #1 of "Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning (v0.1)"

(2022) · PeerJ · doi:10.7287/peerj.13396v0.1/reviews/1

Peer Review #3 of "Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning (v0.1)"

(2022) · PeerJ · doi:10.7287/peerj.13396v0.1/reviews/3

Peer Review #1 of "Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning (v0.2)"

(2022) · PeerJ · doi:10.7287/peerj.13396v0.2/reviews/1

Exploratory Research Opportunities Of Computing In Life Sciences

Guest Editor Dr.Vignesh Ramamoorthy, H. (2022) · International Journal of Life Science and Pharma Research · doi:10.22376/ijpbs/ijlpr/sp20/march.2022.5-200

Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask LearningPreprint

Finotello, R., L’Hermite, D., Quéré, C., Rouge, B., Tamaazousti, M., & Sirven, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.03762

Method for Effective Mosquito Data Classification to Identify Potential Hosts of Malaria with AI Implications Student ResearchersPreprint

Caesar, A., Gaines, W., Gajendran, R., Ram, T., Lee, A., Nwosu, O., Richter, A., Yang, D. D., Lam, B., Meymarian, K., et al. (2022) · doi:10.22541/essoar.167252702.26624552/v1

Method for Effective Mosquito Data Classification to Identify Potential Hosts of Malaria with AI ImplicationsPreprint

Caesar, A., Gaines, W., Gajendran, R., Ram, T., Lee, A., Nwosu, O., Richter, A., Yang, D. D., Lam, B., Meymarian, K., et al. (2023) · doi:10.22541/essoar.167252702.26624552/v2

Method for Effective Mosquito Data Classification to Identify Potential Hosts of Malaria with AI ImplicationsPreprint

Caesar, A., Gaines, W., Gajendran, R., Ram, T., Lee, A., Nwosu, O., Richter, A., Yang, D. D., Lam, B., Meymarian, K., et al. (2023) · doi:10.22541/essoar.167252702.26624552/v3

Using radiomics and deep learning-based imaging biomarkers to predict radiotherapy outcomes and toxicity

Zhang, Z. (2023) · Research Publications (Maastricht University) · doi:10.26481/dis.20230704zz

A First Meta-Analysis Based on Open Access Big Data Mining of Global Predicted Squirrel Distribution Models with Machine Learning for IUCN Conservation Status and Population Trend Policy Assessments

Steiner, M., & Huettmann, F. (2023) · Springer eBooks · doi:10.1007/978-3-031-23547-4_4

Image-based Classification of Air Pollution Using Different Pretrained Cnn Models And A Small Dataset

Matloob, R. A., & Shakir, M. A. (2023) · The Journal of The University of Duhok · doi:10.26682/sjuod.2023.26.1.37

Comparative analysis of pretrained deep neural networks for anomalies detection

Halfpap, I., & Đurović, Ž. (2023) · International Conference on Electrical, Electronic and Computing Engineering (IcETRAN) · doi:10.1109/icetran59631.2023.10192245

Autoshuttle: A Novel Dataset for Advancing Autonomous Driving in Shuttle-Specific Environments

Zhou, L., Salaar, H., Schmidt, M., Dehghani, A., & Arbeiter, G. (2024) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami60510.2024.10432901

Integrating the Concept of Complete Streets into Pavement Preservation

Loken, Z. J. (2023) · Louisiana State University · doi:10.31390/gradschool_theses.5850

Heart Attack Prediction using CNN

Jose, J., Mishra, P., Bansod, J., Pingat, T., & Malvadkar, P. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-18318

Convolutional neural networks models for visual classification of pressure ulcer stages (Preprint)Preprint

Lei, C., Jiang, Y., Xu, K., Liu, S., Cao, H., & Wang, C. (2024) · doi:10.2196/preprints.62774

Enhancing Squat Safety and Performance with Computer Vision and Deep Learning Model

Hazlan, M. A. H., Muhamad, I. H., & Ibrahim, M. Z. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-3851-9_24

Semiautomated mapping of tumor annotations from H&E to fluorescence images of human breast surgical tissues

Niu, T., Ampo, E., Jorns, J. M., Patton, M. D., Lu, T., Ye, D. H., Yen, T. W. F., & Yu, B. (2025) · Multimodal Biomedical Imaging · doi:10.1117/12.3041374

Reconstructing Evapotranspiration in British Columbia Since 1850 Using Publicly Available Tree-Ring Plots and Climate Data

Li, H., & Rex, J. F. (2025) · Remote Sensing · doi:10.3390/rs17050930 · Cited by 2

Generative Dataset Distillation Using Min-Max Diffusion Model

Fan, J., Zhou, Y., Ren, M. C. J., & Yang, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93806-1_5

Exposure Modeling of Transmission Towers for Large-Scale Natural Hazard Risk Assessments Based on Deep-Learning Object Detection Models

Cesarini, L., Figueiredo, R., Romão, X., & Martina, M. L. V. (2025) · Infrastructures · doi:10.3390/infrastructures10070152

Intelligent monitoring of porosity in laser melting deposition based on deep transfer learning

Zheng, F., Peng, K., Zhu, Y., Bai, Q., Wang, Z., Xie, L., & Yin, M. (2025) · Additive manufacturing · doi:10.1016/j.addma.2025.104905 · Cited by 1

Cvxlm: An Explainable Multi-Modal Deep Learning Framework for Plant Disease Diagnosis with Natural Language ReportingPreprint

KİLİM, O., Yi̇ği̇t, T., & Armağan, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5370311

Implementation of a Traffic Sign Recognition System Based on CNN for Autonomous Robots

Gannoun, O., Abadi, A., Djedidi, K., & Njima, C. B. (2025) · International Conference on Control, Automation and Diagnosis (ICCAD) · doi:10.1109/iccad64771.2025.11099234

A Histogram Equalized Median Filtered SIFT–EfficientNet Based on Deep Learning Approach for Lung Disease Detection

Suguna, M., Lekha, P. S., Kumar, C. U. O., Arunmozhi, M., & Prakash, D. (2025) · doi:10.1002/9781394301485.ch5

Detection of Cardiovascular Diseases Using Deep Learning

Lavanya, R. N., & Naresh, V. S. (2025) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-84517-8_14

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 19

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 182

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 55

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