Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms.

The work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food distribution services. Specifically, it focuses on dish counting, content identification, and portion size estimation in a dining hall setting. An RGB camera is employed to capture the tray delivery process in a self-service restaurant, providing test images for plate counting and content identification algorithm comparison, using standard evaluation metrics.

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential.

Analyzing the Impact of Image Denoising and Segmentation on Melanoma Classification Using Convolutional Neural Networks.

Early skin cancer detection and its treatment are crucial for reducing death rates worldwide. Deep learning techniques have been used successfully to develop an automatic lesion detection system. This study explores the impact of pre-processing steps such as data augmentation, contrast enhancement, and segmentation on improving the convolutional neural network (CNN) performance for lesion classification.

Use of pager devices in New Zealand public hospitals as a critical communication tool: Barriers & way forward.

Objectives: The aim of this study was to analyse the current use, identify challenges and barriers and propose a way forward for the use of the pager devices in the in-hospital communications.

Methods: Initially, 447 studies were identified through database searching. After checking against the eligibility, 39 studies were included.

Vital signs and early warning score monitoring: perceptions of clinical staff about current practices and introducing an electronic rapid response system.

Aims And Objectives: This study investigated clinical staff perceptions of learning about current monitoring practices and the planned introduction of an electronic system for patient monitoring. The aim of this research was to evaluate the perceptions of clinical staff (nurses and doctors) about the perceived strengths and weaknesses of the current state of the rapid response system (RRS) and how those strengths and weakness would be affected by introducing an electronic RRS.

Methods: This research applied a descriptive study methodology.