Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...

Abstract: Timely detection of construction changes on islands is crucial for environmental conservation, resource management, and sustainable development. Current methodologies typically employ ...

Abstract: Medical image segmentation remains a challenging task due to the intricate nature of anatomical structures and the wide range of target sizes. In this paper, we propose a novel U-shaped ...

Abstract: This study aims to develop a novel deep learningbased approach to support the automated mushroom growth monitoring using an object tracking algorithm in conjunction with instance ...

Abstract: A Convolutional Neural Network (CNN) are a class of artificial neural networks specifically designed to process data with a grid-like topology, such as images, making them well-suited for ...

Abstract: Accurate localization and segmentation of power equipment are critical for automated inspection systems, particularly in detecting structural and thermal anomalies. However, the task remains ...

Abstract: Glaucoma, a leading cause of irreversible blindness, requires precise segmentation of the optic disc and optic cup in fundus images for early diagnosis and progression monitoring. This study ...

Abstract: The poultry industry has been driven primarily by broiler chicken production and has grown into the world’s largest animal protein sector. Automated detection of chicken carcasses on ...

Abstract: This study proposes a robust and efficient two-stage deep learning framework aimed at the accurate classification of Chest X-ray images into NORMAL and PNEUMONIA categories. The methodology ...

Abstract: The shape and size of the placenta are closely related to fetal development in the second and third trimesters of pregnancy. Accurately segmenting the placental contour in ultrasound images ...