Research

Research interests rooted in machine learning, accessibility, and engineering systems.

My research work aims to connect technical depth with practical usefulness, especially where intelligent systems can improve human outcomes.

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Publications, ongoing investigations, and technical writing collected with more structure.

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Conference

7

Journal

2

Book Chapter

1

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All202420252026

2024 27th International Conference on Computer and Information Technology (ICCIT)

2024Conference

Residual Block-Driven CNN for Accurate White Blood Cell Image Analysis and Classification

Antu Roy Chowdhury, Sadman Hasib Emon, Md Abu Ismail Siddique, Saraf Anzum Shreya

Our findings show that using residual blocks is a very effective approach for detecting and classifying white blood cells (WBCs). As residual block offers lossless data restoration, We built a custom CNN model with residual blocks and achieved an accuracy of around 97.6...

Convolutional neural networksFeature extractionMedical diagnosisOrganisms

2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)

2025Conference

Real-Time Detection and Translation of Bangla Sign Language Characters Using Deep Learning

Sadman Hasib Emon, Antu Roy Chowdhury, Md Abu Ismail Siddique, Md. Siyam Sade

This paper introduces a real-time system for detecting and translating Bangla sign characters into written Bangla using an optimized deep learning model deployed within a Flask-based web interface....

Assistive technologiesAuditory systemBangla Sign Character DetectionCVzone

2026 5th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE)

2026Conference

CyberBiLSTM: A Bidirectional LSTM Architecture for Cybersecurity via High-Precision SQL Injection Attack Detection

Md. Mehedi Hassan, Antu Roy Chowdhury, Rubaeat Ahammed, Arif Hossen

This work introduces CyberBiLSTM, a deep learning model trained on a newly aggregated and diverse dataset of over 244,112 SQL injection queries. The model achieved a test accuracy of 98.43% and a precision of 99.79%, outperforming five benchmark classifiers. In particul...

Application securityBenchmark testingBiLSTMBidirectional long short term memory

Pre-print

Conference

A Generalized Residual-Driven CNN Framework for Multi-Dataset White Blood Cell Classification

Antu Roy Chowdhury, Md Abu Ismail Siddique

Developing a highly generalizable, custom deep residual network trained and validated across 4 distinct white blood cell datasets to overcome cross-domain data distribution shifts in digital hematology....

Convolutional Neural Networks (CNN)Cross-Dataset ValidationDeep LearningModel Generalization

2026 5th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE)

2026Conference

CNN-Transformer Fusion Network for Multi-Class Leaf Disease Classification

Saiful Islam, Sharaf Tasnim, Antu Roy Chowdhury, Md Abu Ismail Siddique

The current study relied on a curated subset of 24 classes with high image counts, which may limit the model’s immediate applicability to rarer botanical pathologies or imbalanced field data. Furthermore, while the model excels in controlled datasets, practical deployme...

Computer architectureComputer visionContext modelingConvolutional neural networks

2025 28th International Conference on Computer and Information Technology (ICCIT)

2026Conference

Adaptive Deep Sequential Networks Empowered by Multi-Model Feature Selection for Effective Phishing URL Identification

Abdullah Al Mamun, Md. Kamal Hosain, Antu Roy Chowdhury, Md Arif Hossen

Phishing through malicious URLs remains a widespread and evolving cyber threat, with traditional defenses like blacklists increasingly falling short against modern attacker tactics. To address this, a new study titled Adaptive Deep Sequential Networks Empowered by Multi...

BiGRUBiLSTMCybersecurity XGBoostFeature Selection

2024 27th International Conference on Computer and Information Technology (ICCIT)

2024Conference

A Hybrid Approach for Accurate Brain Tumor Detection Using Deep Learning Techniques

Saraf Anzum Shreya, Md. Abu Ismail Siddique, Antu Roy Chowdhury, Mst. Fateha Samad

Assessed five widely-used deep learning models—ResNet50, Xception, DenseNet121, VGG19, and VGG16 with custom layers—for detecting brain tumors. We tracked the performance of each model over multiple epochs by analyzing metrics such as accuracy, loss, and ROC curves, ens...

Brain modelingBrain tumorsComputer visionDeep learning

Recognition

Research-adjacent milestones, awards, and project highlights.

Champion at Programming Contest 2k22

Programming Contest Organised by DEpartment of ETE for 20 Series

2022

Champion at Programming Contest 2k22

Achievement details coming soon.

C/C++LabProblem SolvingProgramming