10 citations · 19 across the 12 of their papers we have counts for
14 papers
ChitraMiti: Benchmarking Visual Grounding and Modality Reliance in Bengali Geometric Reasoning
Khan Raiyan Ibne Reza, Sanjana Aktar Maria, Sumaiya Tabassum Nimi +1
Evaluation of vision-language models (VLMs) for multimodal mathematical reasoning remains limited for low-resource languages and for geometry problems that require reading a diagra…
Do Small Models Use the Law You Give Them? Measuring Context Use on a Bilingual Bangladesh Legal Benchmark
Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa +3
Fine-tuning can improve legal question-answering accuracy without improving how models use law supplied in context. We study this distinction in bilingual Bangladeshi legal QA, whe…
Time Present and Time Past: Benchmarking Large Language Models on Temporally Evolving Document Understanding
Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Fahmid Hasan Chowdhury +4
Evolving documents, such as laws, tax codes, and software documentation, are amended, replaced, and sometimes reverted over time, so a question has different correct answers at dif…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
Mutasim Fuad Sarker, Adiba Rahman Namira, Wafa Binte Alam +3
Cardiac arrest remains one of the most lethal conditions encountered in intensive care units. Despite the growing availability of electronic health record data, existing mortality…
Do Small Models Use the Law You Give Them? Context-Injected Fine-Tuning for Legal QA in Bangladesh
Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa +3
A small language model can receive the governing statutory provision and still answer incorrectly. We test whether fine-tuning on examples containing relevant law improves later us…
Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery
Md Adnan Arefeen, Biplob Debnath, Ravi K. Rajendran +2
In satellite applications, user queries often take the form of open-ended natural language, extending beyond a fixed set of predefined categories. This open-vocabulary nature poses…
TrafficLens: Multi-Camera Traffic Video Analysis Using LLMs
Md Adnan Arefeen, Biplob Debnath, Srimat Chakradhar
Traffic cameras are essential in urban areas, playing a crucial role in intelligent transportation systems. Multiple cameras at intersections enhance law enforcement capabilities,…
iRAG: Advancing RAG for Videos with an Incremental Approach
Md Adnan Arefeen, Biplob Debnath, Md Yusuf Sarwar Uddin +1
Retrieval-augmented generation (RAG) systems combine the strengths of language generation and information retrieval to power many real-world applications like chatbots. Use of RAG…
LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs
Md Adnan Arefeen, Biplob Debnath, Srimat Chakradhar
Question-answering (QA) is a significant application of Large Language Models (LLMs), shaping chatbot capabilities across healthcare, education, and customer service. However, wide…
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning
Md Adnan Arefeen, Zhouyu Li, Md Yusuf Sarwar Uddin +1
With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload amo…
FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics
Md Adnan Arefeen, Sumaiya Tabassum Nimi, Md Yusuf Sarwar Uddin
Detection-driven real-time video analytics require continuous detection of objects contained in the video frames using deep learning models like YOLOV3, EfficientDet. However, runn…
EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference
Sumaiya Tabassum Nimi, Md Adnan Arefeen, Md Yusuf Sarwar Uddin +1
Collaborative inference enables resource-constrained edge devices to make inferences by uploading inputs (e.g., images) to a server (i.e., cloud) where the heavy deep learning mode…
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification
Md Adnan Arefeen, Sumaiya Tabassum Nimi, Md Yusuf Sarwar Uddin +1
In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem. The core of our technique is a layer selection strateg…
Neural Network Based Undersampling Techniques
Md. Adnan Arefeen, Sumaiya Tabassum Nimi, M Sohel Rahman
Class imbalance problem is commonly faced while developing machine learning models for real-life issues. Due to this problem, the fitted model tends to be biased towards the majori…