Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science The Indonesian Journal of Computer Science
Obagbuwa, Ibidun Christiana
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Automatic wildlife species identification on camera trap images using deep learning approaches: a systematic review Mamapule, Siyabonga; Esiefarienrhe, Bukohwo Michael; Obagbuwa, Ibidun Christiana
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp968-977

Abstract

The foundation of systematic research depends on precise species identification, functioning as a critical component in the processes of biological research. Wildlife biologists are prompting for more effective techniques to fulfill the expanding need for species identification. The rise in open source image data showing animal species, captured by digital cameras and other digital methods of collecting data, has been monumental. This rapid expansion of animal image data, integrated with state-of-the-art machine learning techniques such as deep learning which has shown significant capabilities for automating species identification. This paper focuses on the role of deep neural network architectures in furthering technological advancements in automating species identification in recent years. To advocate further investigation in this field, an examination of machine learning architectures for species identification was presented in this work. This examination focuses primarily on image analyses and discusses their significance in wildlife conservation. Fundamentally, the aim of this article is to offer insights into the present advancements in automating species identification and to act as a reference for scholars who are keen to integrate machine learning techniques into ecological studies. Systems designed through Artificial Intelligence are extensive in providing toolkits for systematic identification of species in the upcoming years.
Meteorological Drought Forecast using Deep Learning and Ensemble Machine Learning: A systematic review literature Phiri, Reatlegile; Esiefarienrhe, Bukohwo Michael; Obagbuwa, Ibidun Christiana
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5077

Abstract

Meteorological drought is commonly defined as a prolonged deficiency in precipitation relative to the climatological norm for a given region. However, limitations in robustly quantifying and monitoring drought severity continue to impede decision-making across multiple sectors. Conventional tools, have exhibited substantial limitations in terms of accuracy, spatial–temporal resolution, and generalizability. This paper presents a systematic literature review (SLR) focusing on emerging applications of machine learning (ML) and deep learning (DL) to prediction and monitoring meteorological drought, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. An initial pool of 79 peer-reviewed articles published between 2021 and 2025 were identified. The review process examined the articles based on predefined inclusion and exclusion criteria, 19 studies were ultimately retained for detailed analysis. Quality assessment scores for these studies ranged from 71.4% to 100%. The review highlights the increasing use of hybrid ML and DL models, which combine modeling paradigms, as an effective strategy to improve drought forecasting performance, exhibit strong predictive capabilities and offer a compelling alternative to traditional single-model approaches.
Co-Authors Esiefarienrhe, Bukohwo Michael Mamapule, Siyabonga Phiri, Reatlegile