Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine Learning (2)
- Machine learning (2)
- Artificial Intelligence (1)
- Audio Signal Analysis (1)
- Autoencoders (1)
-
- Brain Parcellation (1)
- COVID-19 (1)
- Clinical Epidemiology (1)
- Cost-Sensitive ML (1)
- Cough Analysis (1)
- Cough Sound (1)
- Crowdsourced Data (1)
- Deep Learning (1)
- Descriptive Epidemiology (1)
- E-Health (1)
- Functional Connectivity (1)
- Image Guided Neurosurgery (1)
- Lifestyle data (1)
- Medical Imaging (1)
- Mobile computing (1)
- Multi-Modal Medical Image Registration (1)
- Primary Care (1)
- Primary Health Care (1)
- Robot Localization (1)
- Type-2 diabetes (1)
- Ultrasound (1)
- Unsupervised (1)
Articles 1 - 5 of 5
Full-Text Articles in Artificial Intelligence and Robotics
Respiratory Pattern Analysis For Covid-19 Digital Screening Using Ai Techniques, Annita Tahsin Priyoti
Respiratory Pattern Analysis For Covid-19 Digital Screening Using Ai Techniques, Annita Tahsin Priyoti
Electronic Thesis and Dissertation Repository
Corona Virus (COVID-19) is a highly contagious respiratory disease that the World Health Organization (WHO) has declared a worldwide epidemic. This virus has spread worldwide, affecting various countries until now, causing millions of deaths globally. To tackle this public health crisis, medical professionals and researchers are working relentlessly, applying different techniques and methods. In terms of diagnosis, respiratory sound has been recognized as an indicator of one’s health condition. Our work is based on cough sound analysis. This study has included an in-depth analysis of the diagnosis of COVID-19 based on human cough sound. Based on cough audio samples from …
Developing Artificial Intelligence And Machine Learning To Support Primary Care Research And Practice, Jacqueline K. Kueper
Developing Artificial Intelligence And Machine Learning To Support Primary Care Research And Practice, Jacqueline K. Kueper
Electronic Thesis and Dissertation Repository
This thesis was motivated by the potential to use "everyday data", especially that collected in electronic health records (EHRs) as part of healthcare delivery, to improve primary care for clients facing complex clinical and/or social situations. Artificial intelligence (AI) techniques can identify patterns or make predictions with these data, producing information to learn about and inform care delivery. Our first objective was to understand and critique the body of literature on AI and primary care. This was achieved through a scoping review wherein we found the field was at an early stage of maturity, primarily focused on clinical decision support …
Neurosurgical Ultrasound Pose Estimation Using Image-Based Registration And Sensor Fusion - A Feasibility Study, Utsav Pardasani
Neurosurgical Ultrasound Pose Estimation Using Image-Based Registration And Sensor Fusion - A Feasibility Study, Utsav Pardasani
Electronic Thesis and Dissertation Repository
Modern neurosurgical procedures often rely on computer-assisted real-time guidance using multiple medical imaging modalities. State-of-the-art commercial products enable the fusion of pre-operative with intra-operative images (e.g., magnetic resonance [MR] with ultrasound [US] images), as well as the on-screen visualization of procedures in progress. In so doing, US images can be employed as a template to which pre-operative images can be registered, to correct for anatomical changes, to provide live-image feedback, and consequently to improve confidence when making resection margin decisions near eloquent regions during tumour surgery.
In spite of the potential for tracked ultrasound to improve many neurosurgical procedures, it …
Machine Learning Of Lifestyle Data For Diabetes, Yan Luo
Machine Learning Of Lifestyle Data For Diabetes, Yan Luo
Electronic Thesis and Dissertation Repository
Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The …
Deep Learning Via Stacked Sparse Autoencoders For Automated Voxel-Wise Brain Parcellation Based On Functional Connectivity, Céline Gravelines
Deep Learning Via Stacked Sparse Autoencoders For Automated Voxel-Wise Brain Parcellation Based On Functional Connectivity, Céline Gravelines
Electronic Thesis and Dissertation Repository
Functional brain parcellation – the delineation of brain regions based on functional connectivity – is an active research area lacking an ideal subject-specific solution independent of anatomical composition, manual feature engineering, or heavily labelled examples. Deep learning is a cutting-edge area of machine learning on the forefront of current artificial intelligence developments. Specifically, autoencoders are artificial neural networks which can be stacked to form hierarchical sparse deep models from which high-level features are compressed, organized, and extracted, without labelled training data, allowing for unsupervised learning. This thesis presents a novel application of stacked sparse autoencoders to the problem of parcellating …