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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 7

Published on: July 2026

NON-LINEAR OPTIMIZATION AND DISCRETE CONVOLUTIONAL OPERATORS IN DEEP LEARNING NEURAL ARCHITECTURES

Dr. M. Gnana Prasuna M. Uma Rani RAM MOHAN PAMU

Department of Mathematics, Brilliant Institute of Engineering and Technology (An UGC Autonomous Institution),

Abdullapurmet, Near Ramoji Film City, R.R District, Hyderabad, Telangana, India-501505.

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Image classification is a well-known problem in image processing, computer vision, and machine learning. We investigate picture categorization using deep learning in this research. Nowadays bird watching is becoming a common hobby for everyone, but more than 10,000 species are part of the ecosystem, which causes difficulty in identification and prediction. Additionally, the birds may appear in different scenarios and also in different shapes, sizes, and colors. So, we use Convolutional Neural Network (CNN) to build the models, decreasing the dimensionality of images without losing any content by using a built-in convolutional layer. This will identify the input given by the user and starts the image processing and then compares it with a trained model and predicts the species of the bird. The model will return the output with the predicted probability of the species. If the user-given image is not available in the dataset, the model automatically adds it to the dataset which will be useful in building the dataset. This model helps in the classification and recognition of the birds. Key words: Deep Learning (DL) , Convolution Neural Network(CNN) ,Image Classification.

Keywords—component, formatting, style, styling, insert

How to Cite this Paper

Prasuna, M. G., Rani, M. U. & PAMU, R. M. (2026). Non-Linear Optimization and Discrete Convolutional Operators in Deep Learning Neural Architectures. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(7), 1-9. https://doi.org/10.55041/ijcope.v2i7.159

Prasuna, M., et al.. "Non-Linear Optimization and Discrete Convolutional Operators in Deep Learning Neural Architectures." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 7, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i7.159.

Prasuna, M.,M. Rani, and RAM PAMU. "Non-Linear Optimization and Discrete Convolutional Operators in Deep Learning Neural Architectures." International Journal of Creative and Open Research in Engineering and Management 02, no. 7 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i7.159.

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References


  • K. NAVEEN KUMAR & Dr. RMS PARVATHI, JOURNAL OF SCIENTIFIC REPORTS-SCI-Q1, AI Powered Multi Feature Fusion Framework for Retrieving Images Using Color, Texture and Shape Descriptors. || ISSN 2984-8687 || © November 2025.

  • Marini, Andre´ia, Jacques Facon, and Alessandro L. Koerich. ”Bird species classification based on color features.” 2013 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2013.

  • Ansari, Mahvish, et al. ”Bird Species Identification using Deep Learn-ing.” NEW ARCH-INTERNATIONAL JOURNAL OF CONTEMPO-RARY ARCHITECTURE 8.2 (2021): 2191-2199.

  • Abu, Mohd Azlan, et al. ”A study on Image Classification based on Deep Learning and Tensorflow.” International Journal of Engineering Research and Technology 12.4 (2019): 563-569.

  • Singh, Anisha, Akarshita Jain, and Bipin Kumar ”Image based Bird Species Identification.” International Journal of Research in Engineering, IT and Social Sciences 10.04 (2020): 17-24.

  • Harjoseputro, Yulius, Ign Yuda, and Kefin Pudi Danukusumo. ”Mo-bileNets: Efficient convolutional neural network for identification of protected birds.” IJASEIT (International Journal on Advanced Science, Engineering and Information Technology) 10.6 (2020): 2290-2296.

  • Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. ”Imagenet classification with deep convolutional neural networks.” Communica-tions of the ACM 60.6 (2017): 84-90.

  • Sindhwani, Nidhi, et al. ”Performance analysis of deep neural networks using computer vision.” EAI Endorsed Transactions on Industrial Net-works and Intelligent Systems 8.29 (2021): e3-e3.

  • Raj, Satyam, et al. ”Image based bird species identification using convolutional neural network.” Int. J. Eng. Res. Technol 9 (2020): 346.

  • Ghosh, Susanto Kumar, and Mohammad Rafiqul Islam. ”Convolutional Neural Network Based on HOG Feature for Bird Species Detection and ” International Conference on Recent Trends in Image Processing and Pattern Recognition, Springer. Vol. 1035. 2018.

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  • All submissions are screened under plagiarism detection.
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  • Peer Review Type: Double-Blind Peer Review
  • Published on: Jul 18 2026
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