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Found 9 papers, 5 papers with code
A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization
To address these gaps, we propose a Graph-Based Digital Twin Framework for Supply Chain Optimization, which combines graph modeling with DT architecture to create a dynamic, real-time representation of supply networks.
Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems
This research underscores the potential of multi-agent AI in fostering equitable, sustainable, and culturally sensitive NLP technologies, aligning with the AI Governance, Cultural NLP, and Sustainable NLP pillars of Language Models for Underserved Communities.
Dhoroni: Exploring Bengali Climate Change and Environmental Views with a Multi-Perspective News Dataset and Natural Language Processing
Climate change poses critical challenges globally, disproportionately affecting low-income countries that often lack resources and linguistic representation on the international stage.
Ranked #1 on
Authority Involvement Detection
on Dhoroni
MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement
This study explores the use of deep learning and machine vision to predict moisture content classes from RGB images of wood chips.
Activation Function Optimization Scheme for Image Classification
Finally, we statistically investigate the generalization of the resultant activation functions developed through the optimization scheme.
V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLM
In the rapidly evolving landscape of AI research and application, Multimodal Large Language Models (MLLMs) have emerged as a transformative force, adept at interpreting and integrating information from diverse modalities such as text, images, and Graphical User Interfaces (GUIs).
UTRNet: High-Resolution Urdu Text Recognition In Printed Documents
To address the limitations of previous works, which struggle to generalize to the intricacies of the Urdu script and the lack of sufficient annotated real-world data, we have introduced the UTRSet-Real, a large-scale annotated real-world dataset comprising over 11, 000 lines and UTRSet-Synth, a synthetic dataset with 20, 000 lines closely resembling real-world and made corrections to the ground truth of the existing IIITH dataset, making it a more reliable resource for future research.
Ranked #1 on
Printed Text Recognition
on UPTI
Fake Hilsa Fish Detection Using Machine Vision
In this research, we have proposed a method that can readily identify original Hilsa fish and fake Hilsa fish.
A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
However, manual annotation of lymph node region is a required data preprocessing step in most of the current ML-based ECE diagnosis studies.
