6 citations · 15 across the 23 of their papers we have counts for
25 papers
WALDO: One-Shot Exemplar-Conditioned Object Detection in Cluttered Scenes
Kishor Datta Gupta, Ahmed Rafi Hasan, Md. Mahfuzur Rahman +2
Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is absent, large vision-language m…
TriCLE: Tri-Modal Vision-Language Reasoning for Edge-Deployed Fine-Grained Clustering
Kishor Datta Gupta, Md. Mahfuzur Rahman, Fahad Rahman +4
Edge platforms used for aerial observation must interpret aircraft imagery under limited memory, limited compute, and intermittent connectivity. This setting is difficult for stand…
GPF-LiveNews: A Streaming Evaluation Protocol for Group-Conditioned Framing in Large Language Models
Mohd Ariful Haque, Fahad Rahman, Kishor Datta Gupta +1
Deployed language models are evaluated in a non-stationary environment: model versions, retrieval layers, safety systems, and real-world inputs all change over time. Static bias be…
LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception
Justin williams, Kishor Datta Gupta, Roy George +1
Vision-language-action (VLA) models have shown strong semantic grounding and task generalization in manipulation, but aerial deployment remains difficult because drones require low…
LiteVLA-Edge: Quantized On-Device Multimodal Control for Embedded Robotics
Justin Williams, Kishor Datta Gupta, Roy George +1
Vision-Language-Action (VLA) models provide a unified framework for perception, language conditioning, and action generation, but many existing systems remain difficult to deploy i…
Verbatim Data Transcription Failures in LLM Code Generation: A State-Tracking Stress Test
Mohd Ariful Haque, Kishor Datta Gupta, Mohammad Ashiqur Rahman +1
Many real-world software tasks require exact transcription of provided data into code, such as cryptographic constants, protocol test vectors, allowlists, and calibration tables. T…
TinyLLM: Evaluation and Optimization of Small Language Models for Agentic Tasks on Edge Devices
Mohd Ariful Haque, Fahad Rahman, Kishor Datta Gupta +2
This paper investigates the effectiveness of small language models (SLMs) for agentic tasks (function/tool/API calling) with a focus on running agents on edge devices without relia…
Lite VLA: Efficient Vision-Language-Action Control on CPU-Bound Edge Robots
Justin Williams, Kishor Datta Gupta, Roy George +1
The deployment of artificial intelligence models at the edge is increasingly critical for autonomous robots operating in GPS-denied environments where local, resource-efficient rea…
Physics-Based Benchmarking Metrics for Multimodal Synthetic Images
Kishor Datta Gupta, Marufa Kamal, Md. Mahfuzur Rahman +3
Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accuracy, especially for domain-specific or…
Beyond Visual Similarity: Rule-Guided Multimodal Clustering with explicit domain rules
Kishor Datta Gupta, Mohd Ariful Haque, Marufa Kamal +3
Traditional clustering techniques often rely solely on similarity in the input data, limiting their ability to capture structural or semantic constraints that are critical in many…
Continuous Monitoring of Large-Scale Generative AI via Deterministic Knowledge Graph Structures
Kishor Datta Gupta, Mohd Ariful Haque, Hasmot Ali +3
Generative AI (GEN AI) models have revolutionized diverse application domains but present substantial challenges due to reliability concerns, including hallucinations, semantic dri…
VLCE: A Knowledge-Enhanced Framework for Image Description in Disaster Assessment
Md. Mahfuzur Rahman, Kishor Datta Gupta, Marufa Kamal +5
General-purpose vision-language models (VLMs) such as LLaVA and QwenVL produce descriptions of disaster imagery that lack domain-specific vocabulary and actionable detail. We propo…
UrbanInsight: A Distributed Edge Computing Framework with LLM-Powered Data Filtering for Smart City Digital Twins
Kishor Datta Gupta, Md Manjurul Ahsan, Mohd Ariful Haque +2
Cities today generate enormous streams of data from sensors, cameras, and connected infrastructure. While this information offers unprecedented opportunities to improve urban life,…
Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM
Mohd Ariful Haque, Justin Williams, Sunzida Siddique +4
The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions…
A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning
Sarwar Saif, Md Jahirul Islam, Md. Zihad Bin Jahangir +4
The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data. Traditional methods, where everything was stored in one place, had big pro…
SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment
Ariful Haque, Sunzida Siddique, Md. Mahfuzur Rahman +5
The integration of Large Language Models (LLMs) such as GitHub Copilot, ChatGPT, Cursor AI, and Codeium AI into software development has revolutionized the coding landscape, offeri…
An Extensive and Methodical Review of Smart Grids for Sustainable Energy Management-Addressing Challenges with AI, Renewable Energy Integration and Leading-edge Technologies
Parag Biswas, Abdur Rashid, abdullah al masum +4
Energy management decreases energy expenditures and consumption while simultaneously increasing energy efficiency, reducing carbon emissions, and enhancing operational performance.…
Power Plays: Unleashing Machine Learning Magic in Smart Grids
Abdur Rashid, Parag Biswas, abdullah al masum +2
The integration of machine learning into smart grid systems represents a transformative step in enhancing the efficiency, reliability, and sustainability of modern energy networks.…
Trustworthy XAI and Application
MD Abdullah Al Nasim, A. S. M Anas Ferdous, Abdur Rashid +4
Artificial Intelligence (AI) is an important part of our everyday lives. We use it in self-driving cars and smartphone assistants. People often call it a "black box" because its co…
UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking
Md. Mahfuzur Rahman, Sunzida Siddique, Marufa Kamal +2
Unmanned Aerial Vehicles (UAVs), have greatly revolutionized the process of gathering and analyzing data in diverse research domains, providing unmatched adaptability and effective…
Physical Rule-Guided Convolutional Neural Network
Kishor Datta Gupta, Marufa Kamal, Rakib Hossain Rifat +2
The black-box nature of Convolutional Neural Networks (CNNs) and their reliance on large datasets limit their use in complex domains with limited labeled data. Physics-Guided Neura…
Deep Learning for Automated Wound Classification And Segmentation
Md. Zihad Bin Jahangir, Sumaiya Akter, MD Abdullah Al Nasim +2
Wounds, such as foot ulcers, pressure ulcers, leg ulcers, and infected wounds, come up with substantial problems for healthcare professionals. Prompt and accurate segmentation is c…
Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks
Md Abdullah Al Nasim, Parag Biswas, Abdur Rashid +4
Machine learning (ML) is a rapidly developing area of medicine that uses significant resources to apply computer science and statistics to medical issues. ML's proponents laud its…
AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey
Parag Biswas, Abdur Rashid, Angona Biswas +3
Reduced environmental effect, lower operating costs, and a stable and sustainable energy supply for current and future generations are the main reasons why power optimization is im…