Hey, I'm Ashiq — Software Engineer with Full-Stack Expertise.

Musculoskeletal fractures are a significant health concern and require accurate diagnosis for effective treatment planning. However, the limited availability of large-scale, annotated datasets for fracture detection and classification in radiographic images has hindered progress in developing robust machine learning models. We present FracAtlas, a comprehensive dataset containing 4,083 X-ray images with annotations for fracture classification, localization via bounding boxes, and segmentation masks.

In this paper, we present an integrated approach to real-time mosquito detection using our multiclass dataset (MosquitoFusion) containing 1204 diverse images and leverage cutting-edge technologies, specifically computer vision, to automate the identification of Mosquitoes, Swarms, and Breeding Sites. The pre-trained YOLOv8 model, trained on this dataset, achieved a mean Average Precision (mAP@50) of 57.1%, with precision at 73.4% and recall at 50.5%. The integration of Geographic Information Systems (GIS) further enriches the depth of our analysis, providing valuable insights into spatial patterns.

Optical Character Recognition (OCR) technology is a vital tool for digitizing printed content, enabling efficient data extraction and enhancing document accessibility. Traditional OCR techniques rely on pre-stored templates for fonts or structured documents. Recent advancements in Machine Learning (ML), particularly Convolutional Neural Network (CNN) and transformer-based architectures, have enhanced OCR technologies with human-like intelligence. However, these models often fall short due to limitations in the diversity of document types, layouts, and content in the training datasets, particularly for complex Bangla documents. In this paper, we address the challenge of a limited, diverse dataset by introducing BanglaDocAtlas, a versatile and multi-class annotated dataset specifically designed to advance Bangla document layout analysis.