Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IJISCS (International Journal of Information System and Computer Science)
MALACH OBISA AMONGA
Department of Computer Science, Chuka University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
A HYBRID RESNET–INCEPTION DEEP LEARNING ARCHITECTURE FOR ENHANCED WILDLIFE IDENTIFICATION MALACH OBISA AMONGA
IJISCS (International Journal of Information System and Computer Science) Vol 10, No 2 (2026): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v10i2.1889

Abstract

Accurate wildlife identification in unconstrained environments remains a significant challenge due to intra-class similarity, occlusions, illumination variations, and complex backgrounds. This study of hybrid deep learning architecture that enhances classification performance by integrating complementary feature learning mechanisms within a unified framework. The model captures both deep hierarchical representations and multi-scale spatial features through a feature-level fusion strategy, resulting in a more discriminative and robust representation of wildlife images. The hybrid approach was trained and evaluated on the Animals with Attributes 2 (AwA2) dataset and further validated on the WildlifeReID-10k dataset to assess its generalization capability. Experimental results show that the model achieves a classification accuracy of 98%, along with high precision, recall, and F1-score, demonstrating strong reliability and effectiveness. Furthermore, the model maintains high performance on external data, confirming its robustness and ability to generalize across diverse real-world conditions. These findings highlight the effectiveness of hybrid deep learning approaches in addressing complex wildlife identification challenges and support their application in ecological monitoring and biodiversity conservation
Co-Authors