Analysis of Logistic Regression Regularization in Wild Elephant Classification with VGG-16 Feature Extraction

Aulia  Ichsan; Sugeng  Riyadi; Doughlas  Pardede

doi:10.47709/cnahpc.v6i2.3789

Authors

Aulia Ichsan Universitas Deli Sumatera
Sugeng Riyadi Universitas Deli Sumatera
Doughlas Pardede Universitas Deli Sumatera

DOI:

10.47709/cnahpc.v6i2.3789

Keywords:

Wildlife Classification; Logistic Regression; VGG-16; Lasso Regularization; Ridge Regularization

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Abstract

The research article explores the intersection of image-based wildlife classification and logistic regression regularization, focusing on the classification of wild elephant species. It begins by highlighting the significance of ecological research in biodiversity monitoring and conservation and introduces Convolutional Neural Networks (CNNs) as potent tools for feature extraction from images. The VGG-16 model is particularly emphasized for its ability to capture hierarchical representations of visual features crucial for classification tasks. The integration of VGG-16 feature extraction with logistic regression regularization is proposed as a compelling approach, offering a balance between sophisticated feature representation and efficient classification algorithms. The literature review delves into image-based wildlife classification, emphasizing the role of CNNs, especially VGG-16, in extracting discriminative features. It discusses the fusion of VGG-16 features with logistic regression and the challenges in this field, such as dataset annotation and environmental variability. The method section outlines the dataset acquisition, feature extraction using the VGG-16 architecture, and model configuration using logistic regression with lasso and ridge regularization. The process of finding the optimal regularization parameter (lambda) and model evaluation through cross-validation is detailed. Results showcase the optimal lambda values for lasso and ridge regularization and compare the performance of logistic lasso and logistic ridge models. Misclassification analysis reveals factors influencing classification accuracy, including feature variability and contextual complexity. The discussion reflects on the implications of the findings, emphasizing the importance of lambda selection and addressing challenges in wildlife classification. It suggests avenues for further research, such as advanced modeling techniques and feature engineering approaches. In conclusion, the study contributes to advancing wildlife classification efforts by leveraging state-of-the-art techniques and sheds light on opportunities to enhance classification accuracy in wildlife conservation.

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Author Biographies

Sugeng Riyadi, Universitas Deli Sumatera

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Doughlas Pardede, Universitas Deli Sumatera

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References

Adeli, E., Li, X., Kwon, D., Zhang, Y., & Pohl, K. M. (2020). Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(7), 1713–1728. https://doi.org/10.1109/TPAMI.2019.2901688

Al-Khater, W., & Al-Madeed, S. (2024). Using 3D-VGG-16 and 3D-Resnet-18 deep learning models and FABEMD techniques in the detection of malware. Alexandria Engineering Journal, 89(November 2023), 39–52. https://doi.org/10.1016/j.aej.2023.12.061

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., … Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8

Battu, T., & Reddy Lakshmi, D. S. (2023). Animal image identification and classification using deep neural networks techniques. Measurement: Sensors, 25(October 2022), 100611. https://doi.org/10.1016/j.measen.2022.100611

Bria, A., Marrocco, C., & Tortorella, F. (2020). Addressing class imbalance in deep learning for small lesion detection on medical images. Computers in Biology and Medicine, 120(March), 103735. https://doi.org/10.1016/j.compbiomed.2020.103735

Burrewar, S. S., Haque, M., & Haider, T. U. (2024). Convolutional Neural Network Methods for Detecting Land-Use Changes. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 573–590.

Cao, Z., Wang, K., Wen, J., Li, C., Wu, Y., Wang, X., & Yu, W. (2024). Fine-grained image classification on bats using VGG16-CBAM: a practical example with 7 horseshoe bats taxa (CHIROPTERA: Rhinolophidae: Rhinolophus) from Southern China. Frontiers in Zoology, 21(1), 10. https://doi.org/10.1186/s12983-024-00531-5

Chisom, O. N., Biu, P. W., Umoh, A. A., Obehioye, B. O., Adegbite, A. O., & Abatan, A. (2024). Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet. World Journal of Advanced Research and Reviews, 21(1), 161–171. https://doi.org/10.30574/wjarr.2024.21.1.2720

de Silva, E. M. K., Kumarasinghe, P., Indrajith, K. K. D. A. K., Pushpakumara, T. V., Vimukthi, R. D. Y., de Zoysa, K., … de Silva, S. (2022). Feasibility of using convolutional neural networks for individual-identification of wild Asian elephants. Mammalian Biology, 102(3), 931–941. https://doi.org/10.1007/s42991-021-00206-2

Dhanya, V. G., Subeesh, A., Kushwaha, N. L., Vishwakarma, D. K., Nagesh Kumar, T., Ritika, G., & Singh, A. N. (2022). Deep learning based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture, 6, 211–229. https://doi.org/10.1016/j.aiia.2022.09.007

Dharaniya R, Preetha M, & Yashmi S. (2022). Bird Species Identification Using Convolutional Neural Network. In Advances in Parallel Computing (pp. 380–386). https://doi.org/10.3233/APC220053

Faizal, S., & Sundaresan, S. (2022). Wild Animal Classifier Using CNN. International Journal of Advanced Research in Science, Communication and Technology, (September), 233–239. https://doi.org/10.48175/IJARSCT-7097

Fawwaz, I., Yennimar, Y., Dharsinni, N. P., & Wijaya, B. A. (2023). The Optimization of CNN Algorithm Using Transfer Learning for Marine Fauna Classification. Sinkron, 8(4), 2236–2245. https://doi.org/10.33395/sinkron.v8i4.12893

Firmansyah, I., & Rosnelly, R. (2023). Inception-V3 Versus VGG-16?: in Rice Classification Using Multilayer Perceptron. 2nd International Conference on Information Science and Technology Innovatin (ICoSTEC), 2(1), 1–5. Retrieved from https://prosiding-icostec.respati.ac.id/index.php/icostec/article/view/24

Handayani, M., Rosnelly, R., & Hartono. (2023). Classification of Basurek Batik Using Pre-Trained VGG- 16 and Support Vector Machine. 2nd International Conference on Information Science and Technology Innovatin (ICoSTEC), 3–7.

Hindarto, D., Afarini, N., & Esthi H, E. T. (2023). Comparison Efficacy of VGG16 and VGG19 Insect Classification Models. JIKO (Jurnal Informatika Dan Komputer), 6(3), 189–195. https://doi.org/10.33387/jiko.v6i3.7008

Hooten, M. B., Lu, X., Garlick, M. J., & Powell, J. A. (2020). Animal movement models with mechanistic selection functions. Spatial Statistics, 37. https://doi.org/10.1016/j.spasta.2019.100406

Hridayami, P., Putra, I. K. G. D., & Wibawa, K. S. (2019). Fish Species Recognition Using VGG16 Deep Convolutional Neural Network. Journal of Computing Science and Engineering, 13(3), 124–130. https://doi.org/10.5626/JCSE.2019.13.3.124

Islam, S., Khan, S. I. A., Abedin, M. M., Habibullah, K. M., & Das, A. K. (2019). Bird Species Classification from an Image Using VGG-16 Network. Proceedings of the 2019 7th International Conference on Computer and Communications Management, (July), 38–42. New York, NY, USA: ACM. https://doi.org/10.1145/3348445.3348480

Kolluri, J., Kotte, V. K., Phridviraj, M. S. B., & Razia, S. (2020). Reducing Overfitting Problem in Machine Learning Using Novel L1/4 Regularization Method. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), (June), 934–938. IEEE. https://doi.org/10.1109/ICOEI48184.2020.9142992

Kumar, V., Kedam, N., Sharma, K. V., Mehta, D. J., & Caloiero, T. (2023). Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models. Water (Switzerland), 15(14). https://doi.org/10.3390/w15142572

Margolang, K. F., Riyadi, S., Rosnelly, R., & Wanayumini. (2023). Pengenalan Masker Wajah Menggunakan VGG-16 dan Multilayer Perceptron. Jurnal Telematika, 17(2), 80–87.

Masood, S., Ahsan, U., Munawwar, F., Rizvi, D. R., & Ahmed, M. (2020). Scene Recognition from Image Using Convolutional Neural Network. Procedia Computer Science, 167(2019), 1005–1012. https://doi.org/10.1016/j.procs.2020.03.400

Ng, C. H., Connie, T., Choo, K. Y., & Goh, M. K. O. (2022). Fusion of Visual and Audio Signals for Wildlife Surveillance. International Journal of Technology, 13(6), 1213–1221. https://doi.org/10.14716/ijtech.v13i6.5876

Norman, D. L., Bischoff, P. H., Wearn, O. R., Ewers, R. M., Rowcliffe, J. M., Evans, B., … Freeman, R. (2023). Can CNN?based species classification generalise across variation in habitat within a camera trap survey? Methods in Ecology and Evolution, 14(1), 242–251. https://doi.org/10.1111/2041-210X.14031

PARDEDE, J., & HARDIANSAH, H. (2022). Deteksi Objek Kereta Api menggunakan Metode Faster R-CNN dengan Arsitektur VGG 16. MIND Journal, 7(1), 21–36. https://doi.org/10.26760/mindjournal.v7i1.21-36

Pardede, J., Sitohang, B., Akbar, S., & Khodra, M. L. (2021). Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection. International Journal of Intelligent Systems and Applications, 13(2), 52–61. https://doi.org/10.5815/ijisa.2021.02.04

Pérez-Carabaza, S., Boydell, O., & O’Connell, J. (2021). Habitat classification using convolutional neural networks and multitemporal multispectral aerial imagery. Journal of Applied Remote Sensing, 15(04). https://doi.org/10.1117/1.JRS.15.042406

Qin, J., & Lou, Y. (2019). L1-2 Regularized Logistic Regression. Conference Record - Asilomar Conference on Signals, Systems and Computers, 2019-Novem(April), 779–783. https://doi.org/10.1109/IEEECONF44664.2019.9048830

Rajabizadeh, M., & Rezghi, M. (2021). A comparative study on image-based snake identification using machine learning. Scientific Reports, 11(1), 19142. https://doi.org/10.1038/s41598-021-96031-1

Rismiyati, R., & Luthfiarta, A. (2021). VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification. Telematika, 18(1), 37. https://doi.org/10.31315/telematika.v18i1.4025

Riyadi, S., Hartono, & Wanayumini. (2023). Predicting Children’s Talent Based On Hobby Using C4.5 Algorithm And Random Forest. International Conference on Information Science and Technology Innovation (ICoSTEC), 2(1), 182–186. https://doi.org/10.35842/icostec.v2i1.54

Roberts, P., Helmholz, P., Parnum, I., & Krishna, A. (2023). IMAGE FEATURE EXTRACTION METHODS FOR STRUCTURE DETECTION FROM UNDERWATER IMAGERY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W(1/W2-2023), 1067–1074. https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1067-2023

Rubbens, P., Brodie, S., Cordier, T., Destro Barcellos, D., Devos, P., Fernandes-Salvador, J. A., … Irisson, J. O. (2023). Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science, 80(7), 1829–1853. https://doi.org/10.1093/icesjms/fsad100

Saleh, A., Sheaves, M., & Rahimi Azghadi, M. (2022). Computer vision and deep learning for fish classification in underwater habitats: A survey. Fish and Fisheries, 23(4), 977–999. https://doi.org/10.1111/faf.12666

Samudra, J. T., Rosnelly, R., Situmorang, Z., & Ramadhan, P. S. (2023). Model Klasifikasi Jenis Hewan Dengan SVM, KNN, Logistic Regression Menggunakan Pre-Trained VGG 16. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika Dan Komputer), 22(2), 225. https://doi.org/10.53513/jis.v22i2.8314

Tambunan, F. N., Rosnelly, R., & Situmorang, Z. (2023). Transfer Learning for Feral Cat Classification Using Logistic Regression. International Conference on Information Science and Technology Innovation (ICoSTEC), 2(1), 17–22. https://doi.org/10.35842/icostec.v2i1.30

Tanuwijaya, E., & Roseanne, A. (2021). Modifikasi Arsitektur VGG16 untuk Klasifikasi Citra Digital Rempah-Rempah Indonesia. MATRIK?: Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(1), 189–196. https://doi.org/10.30812/matrik.v21i1.1492

Ukwuoma, C. C., Qin, Z., Yussif, S. B., Happy, M. N., Nneji, G. U., Urama, G. C., … Agobah, H. (2022). Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network. Scientific African, 16(April), e01151. https://doi.org/10.1016/j.sciaf.2022.e01151

Ye, M., Ruiwen, N., Chang, Z., He, G., Tianli, H., Shijun, L., … Ying, G. (2021). A Lightweight Model of VGG-16 for Remote Sensing Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6916–6922. https://doi.org/10.1109/JSTARS.2021.3090085