1 citations · 1 across the 6 of their papers we have counts for
9 papers
Ensemble Classification and Cancer-Specific Gene Signatures from Quality-Aware Hybrid Gene Selection in TCGA RNA-Seq data
Tareque Mohmud Chowdhury, Abu Raihan Mostofa Kamal
Gene-expression classifiers of cancer type are usually developed and validated within one cohort using random splits. Starting from a quality-aware hybrid Boruta selector from earl…
Tree Depth and Sample-Quality-Associated Features: Two Hidden Factors in Boruta Selection for RNA-Seq Data Classification
Tareque Mohmud Chowdhury, Abu Raihan Mostofa Kamal
Random Forest--based selectors such as Boruta depend on tree depth, yet Boruta's most used implementation, BorutaPy, recommends depths of 3 to 7 without published evidence. We test…
Receiver Decides Agent-to-Agent Negotiations
Fardeen Sadab, Alif Hasnain, Afsana Shehrin +2
When one language-model agent asks another for money, which of the two decides how much changes hands? We ran a round-robin tournament of 1,049 negotiations among fifteen agents. I…
Routing Is Not Enough: Diagnosing Intra-Adapter Subspace Contention in MoE+LoRA Fine-Tuning
Mehreen Hossain Chowdhury, Nowshin Mahjabin, Ahmed Shafin Ruhan +3
Multi-domain fine-tuning often combines MoE routing with LoRA, assuming that token-level routing separates domain-specific updates. We test this assumption in MoE+LoRA using Python…
UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imaging
Rafsan Jany, Shadab Tanjeed Ahmad, Ahsan Bulbul +3
Multimodal based approaches often outperform single modality approaches in downstream tasks as the different modalities provide complementary information, yet acquiring paired clin…
AntiFLipper: A Secure and Efficient Defense Against Label-Flipping Attacks in Federated Learning
Aashnan Rahman, Abid Hasan, Sherajul Arifin +5
Federated learning (FL) enables privacy-preserving model training by keeping data decentralized. However, it remains vulnerable to label-flipping attacks, where malicious clients m…
FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning
Md Rafid Haque, Abu Raihan Mostofa Kamal, Md. Azam Hossain
Federated Learning (FL) offers a paradigm for privacy-preserving collaborative AI, but its decentralized nature creates significant vulnerabilities to model poisoning attacks. Whil…
A Pan-cancer Classification Model using Multi-view Feature Selection Method and Ensemble Classifier
Tareque Mohmud Chowdhury, Farzana Tabassum, Sabrina Islam +1
Accurately identifying cancer samples is crucial for precise diagnosis and effective patient treatment. Traditional methods falter with high-dimensional and high feature-to-sample…
BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis
Sadia Alam, Md Farhan Ishmam, Navid Hasin Alvee +3
The widespread availability of code-mixed data can provide valuable insights into low-resource languages like Bengali, which have limited datasets. Sentiment analysis has been a fu…