This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Delplanque, A., R. Lamprey, S. Foucher, J. Theau, P. Lejeune. Surveying Wildlife and Livestock in Uganda with Aerial Cameras: Deep Learning Reduces the Workload of Human Interpretation by over 70%. – Frontiers in Ecology and Evolution, Vol. 11, 2023, 1270857.Search in Google Scholar
Wood, C. M., A. B. Cruz, S. Kahl. Pairing a User‐Friendly Machine‐Learning Animal Sound Detector with Passive Acoustic Surveys for Occupancy Modeling of an Endangered Primate. – American Journal of Primatology, Vol. 85, 2023, No 8, e23507.Search in Google Scholar
Tanzib, H. M., A. Zaman, M. R. Abir, S. Akter, S. Mursalin, S. S. Khan. Synchronizing Object Detection: Applications, Advancements, and Existing Challenges. – IEEE Access, 2024.Search in Google Scholar
Shinichi, N., L. Malgorzata, F. Roxane, T. Jessica, L. Xun, E. Andrew, J. R. Neil, O’Brien K. Justine, P. J. Benjamin, V. S. Monique, S. Arcot, K. T. Richard. Rapid Literature Mapping on the Recent Use of Machine Learning for Wildlife Imagery. – Peer Community Journal, Vol. 3, 2023.Search in Google Scholar
Aguilar-Lazcano, C. A., I. E. Espinosa-Curiel, J. A. Ríos-Martínez, F. A. Madera-Ramírez, H. Pérez-Espinosa. Machine Learning-Based Sensor Data Fusion for Animal Monitoring: A Scoping Review. – Sensors, Vol. 23, 2023, No 12, 5732.Search in Google Scholar
Popek, Ł., R. Perz, G. Galiński. Comparison of Different Methods of Animal Detection and Recognition on Thermal Camera Images. – Electronics, Vol. 12, 2023, No 2, 270.Search in Google Scholar
Jeantet, L., E. Dufourq. Improving Deep Learning Acoustic Classifiers with Contextual Information for Wildlife Monitoring. – Ecological Informatics, Vol. 77, 2023, 102256.Search in Google Scholar
Binta Islam, S., D. Valles, T. J. Hibbitts, W. A. Ryberg, D. K. Walkup, M. R. Forstner. Animal Species Recognition with Deep Convolutional Neural Networks from Ecological Camera Trap Images. – Animals, Vol. 13, 2023, No 9, 1526.Search in Google Scholar
Rančić, K., B. Blagojević, A. Bezdan, B. Ivošević, B. Tubić, M. Vranešević, B. Pejak, V. Crnojević, O. Marko. Animal Detection and Counting from UAV Images Using Convolutional Neural Networks. – Drones, Vol. 7, 2023, No 3, 179.Search in Google Scholar
Delplanque, A., S. Foucher, J. Théau, E. Bussière, C. Vermeulen, P. Lejeune. From Crowd to Herd Counting: How to Precisely Detect and Count African Mammals Using Aerial Imagery and Deep Learning? – ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 197, 2023, pp. 167-180.Search in Google Scholar
Sharma, S., K. Sato, B. P. Gautam. A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques. – Sustainability, Vol. 15, 2023, No 9, 7128.Search in Google Scholar
Mou, C., A. Liang, C. Hu, F. Meng, B. Han, F. Xu. Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost. – Animals, Vol. 13, 2023, No 20, 3168.Search in Google Scholar
Ibraheam, M., K. F. Li, F. Gebali. An Accurate and Fast Animal Species Detection System for Embedded Devices. – IEEE Access, Vol. 11, 2023, pp. 23462-23473.Search in Google Scholar
Simões, F., C. Bouveyron, F. Precioso. DeepWILD: Wildlife Identification, Localisation and Estimation on Camera Trap Videos Using Deep Learning. – Ecological Informatics, Vol. 75, 2023, 102095.Search in Google Scholar
Roy, A. M., J. Bhaduri, T. Kumar, K. Raj. WilDect-YOLO: An Efficient and Robust Computer Vision-Based Accurate Object Localization Model for Automated Endangered Wildlife Detection. – Ecological Informatics, Vol. 75, 2023, 101919.Search in Google Scholar
Ravi, G., S. Afroz, K. Yamuna, S. Afsha. CNN Based Wildlife Intrusion Detection and Alert System. – International Transactions on Electrical Engineering and Computer Science, Vol. 2, 2023, No 1, pp. 30-36.Search in Google Scholar
Schindler, F., V. Steinhage, S. T. van Beeck Calkoen, M. Heurich. Action Detection for Wildlife Monitoring with Camera Traps Based on Segmentation with Filtering of Trackless (SWIFT) and Mask-Guided Action Recognition (MAROON). – Applied Sciences, Vol. 14, 2024, No 2, 514.Search in Google Scholar
Samreen, S., L. Akhila, R. Ravali, K. Akshitha. Developing Hybrid Deep Neural Networks for Detecting the Movement of Wild Animals and Generating Alarm Messages. – International Journal of Computing and Artificial Intelligence, Vol. 5, 2024, No 2, pp. 119-123.Search in Google Scholar
Velasco-Montero, D., J. Fernández-Berni, R. Carmona-Galán, A. Sanglas, F. Palomares. Reliable and Efficient Integration of AI into Camera Traps for Smart Wildlife Monitoring Based on Continual Learning. – Ecological Informatics, Vol. 83, 2024, 102815.Search in Google Scholar
Kalbhor, M., S. Shinde, D. E. Popescu, D. J. Hemanth. Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis. – Diagnostics, Vol. 13, 2023, No 7, 1363.Search in Google Scholar
Lalinia, M., A. Sahafi. Colorectal Polyp Detection in Colonoscopy Images Using YOLO-V8 Network. – Signal, Image, and Video Processing, Vol. 18, 2024, No 3, pp. 2047-2058.Search in Google Scholar
Zhang, Y., Z. Lu, F. Zhang, H. Wang, S. Li. Machine Unlearning by Reversing the Continual Learning. – Applied Sciences, Vol. 13, 2023, No 16, 9341.Search in Google Scholar
Lazzarini, R., H. Tianfield, V. Charissis. Federated Learning for IoT Intrusion Detection. – AI, 2023, No 4, pp. 509-530.Search in Google Scholar
Dahou, A., A. O. Aseeri, A. Mabrouk, R. A. Ibrahim, M. A. Al-Betar, M. A. Elaziz. Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search. – Diagnostics, 2023, No 13, 1579.Search in Google Scholar
Kollu, V. N., V. Janarthanan, M. Karupusamy, M. Ramachandran. Cloud-Based Smart Contract Analysis in FinTech Using IoT-Integrated Federated Learning in Intrusion Detection. – Data, 2023, No 8, 83.Search in Google Scholar
Zhu, J., J. Cao, D. Saxena, S. Jiang, H. Ferradi. Blockchain-Empowered Federated Learning: Challenges, Solutions, and Future Directions. – ACM Computing Surveys, Vol. 55, 2023, No 11, pp. 1-31.Search in Google Scholar
