RESEARCH / PUBLICATION RECORD

Models should show their work.

Four peer-reviewed conference publications across explainable AI, computer vision, agriculture, and environmental intelligence.

01
First author1 of 8 authors
Published

REMP: A Swin Transformer-Powered Approach to Classifying Rare and Endangered Medicinal Plants

A customized Swin Transformer classified 16 rare and endangered medicinal plant species with 98.46% accuracy, supporting technology-assisted identification and conservation.

Venue
2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)
Publisher / year
IEEE · 2025
Authorship
Sabit Al Alfi · author 1 of 8
DOI
10.1109/QPAIN66474.2025.11171802
View on IEEE ↗
02
Second author2 of 5 authors
Published

Explainable Deep Learning Paradigms for Nitrogen Deficiency Detection: Multi-Crop Assessment of CNN-Based Models for Agricultural Intelligence

An explainable multi-crop assessment using SE-ResNeXt and Grad-CAM, achieving 95.23% classification accuracy across four crop datasets.

Venue
2025 28th International Conference on Computer and Information Technology (ICCIT)
Publisher / year
IEEE · 2025
Authorship
Sabit Al Alfi · author 2 of 5
DOI
10.1109/ICCIT68739.2025.11491378
View on IEEE ↗
03
Co-author8 of 9 authors
Published

Detection of Lemon Leaf Diseases Using Inception V3-Based Machine Learning Model

A comparative deep-learning study of lemon leaf disease classification in which Inception V3 achieved 86.94% validation accuracy, with explainability and augmentation identified as paths toward stronger precision-agriculture systems.

Venue
International Conference on Computational Intelligence and Information Retrieval (ICCIIR 2025) · LNNS 1617
Publisher / year
Springer · 2026
Authorship
Sabit Al Alfi · author 8 of 9
DOI
10.1007/978-3-032-04539-3_10
View on Springer ↗
04
Co-author7 of 12 authors
Published

Forecasting Air Quality: A Comprehensive Survey of Air Pollution Prediction Methods and Applications

A survey of statistical, machine-learning, and deep-learning approaches to air-quality prediction, covering their comparative strengths, limitations, interpretability, and opportunities for real-time monitoring.

Venue
International Conference on Computational Intelligence and Information Retrieval (ICCIIR 2025) · LNNS 1593
Publisher / year
Springer · 2026
Authorship
Sabit Al Alfi · author 7 of 12
DOI
10.1007/978-3-032-02790-0_29
View on Springer ↗