Mathematical Theory to Code: From Basics to Advanced

Alhamdulillah 🤲
I am truly grateful to share an important milestone of my academic journey.

📌 I have published a total (10) research papers in the year 2025, including 3 Q1 journals, 1 Q2 journal, 1 Q3 journal, and 5 conference papers. My works are listed below:

🔹 Q1 Journals
1️⃣ Securing Aviation Networks: A Generalized and Robust Machine Learning Solution for Monitoring Abnormalities in Commercial Aircraft
Journal: Results in Engineering, Elsevier (Q1, Impact Factor: 7.9)
Position: 1st Author
🔗 https://www.sciencedirect.com/science/article/pii/S2590123025049278

2️⃣ Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis
Journal: Alexandria Engineering Journal, Elsevier (Q1, Impact Factor: 6.8 )
Position: 1st Author
🔗 https://www.sciencedirect.com/science/article/pii/S1110016825005307

3️⃣ Improving sleep disorder diagnosis through optimized machine learning approaches
Journal: IEEE Access, IEEE (Q1, Impact Factor: 3.6)
Position: 1st Author
🔗 https://ieeexplore.ieee.org/abstract/document/10856004

🔹 Q2 Journal
4️⃣ Advancements in Breast Cancer Detection: A Review of Global Trends, Risk Factors, Imaging Modalities, Machine Learning, and Deep Learning Approaches
Journal: BioMedInformatics, MDPI (Q2, CiteScore: 3.4)
Position: 1st Author
🔗 https://www.mdpi.com/2673-7426/5/3/46

🔹 Q3 Journal
5️⃣ MangoImageBD: An extensive mango image dataset for identification and classification of various mango varieties in Bangladesh
Journal: Data in Brief, Elsevier (Q3, CiteScore: 2.6, Impact Factor: 1.4)
Position: Co-author
🔗 https://www.sciencedirect.com/science/article/pii/S2352340925006328

🔹 Conference Papers (IEEE)
6️⃣ Early Detection of Autism Spectrum Disorder in Toddlers: A Fast and Efficient Machine Learning Approach
Position: 1st Author
🔗 https://ieeexplore.ieee.org/abstract/document/11013280

7️⃣ Clinical Laboratory Data-Based Bladder Cancer Prediction Using Machine Learning Approach
Position: 1st Author
🔗 https://ieeexplore.ieee.org/abstract/document/11013314

8️⃣ A Hybrid ViT-GRU Model for Breast Cancer Detection: Addressing Class Imbalance Challenges
Position: Co-author
🔗 https://ieeexplore.ieee.org/abstract/document/11013095

9️⃣ Advancing Kidney Disease Diagnosis Using Convolutional Neural Networks on Medical Imaging
Position: Co-author
🔗 https://ieeexplore.ieee.org/abstract/document/11013262

🔟 Retinal Fundus Image Classification Using Generative Adversarial Networks
Position: Co-author
🔗 https://ieeexplore.ieee.org/abstract/document/11013430

✨ I sincerely thank my co-authors, mentors, friends, and family for their continuous support and encouragement. This motivates me to keep contributing impactful research in machine learning, healthcare, and aviation security.

#2025

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🎉 Research Acceptance Announcement 🎉

I’m pleased to share that our paper titled “Securing Aviation Networks: A Generalized and Robust Machine Learning Solution for Monitoring Abnormalities in Commercial Aircraft” has been accepted for publication in Results in Engineering (Elsevier) — a Q1 journal with an impact factor of 7.9.

🔗 Paper link:
https://www.sciencedirect.com/science/article/pii/S2590123025049278

✈️ Key Highlights of the Study:

1. Analyzed 9,949,927 real-world flight records, one of the largest datasets used in aviation anomaly detection.

2. Achieved 93.63% accuracy without aggressive data reduction, ensuring strong real-world applicability.

3. Developed a scalable XGBoost-based ML framework capable of handling massive aviation datasets.

4. Identified key predictive features (timestep, baroaltitude, velocity, longitude, latitude) using robust statistical feature selection.

5. Effectively addressed imbalanced data using SMOTEENN, improving reliability for rare events.

6. Ensured model interpretability and trustworthiness through SHAP and LIME analyses.

7. Provided insights into why smaller datasets may report inflated accuracy and the implications for real-world deployment.

📌 Impact:
This work advances aviation safety by offering a robust, transparent, and generalizable machine learning framework for large-scale aircraft anomaly monitoring, with practical guidance for industry adoption by airlines and aviation authorities.

Grateful to all co-authors and collaborators for their support. Looking forward to seeing this work contribute to safer and smarter aviation systems 🚀

www.sciencedirect.com