Sökning: "data-driven development"
Visar resultat 1 - 5 av 127 avhandlingar innehållade orden data-driven development.
1. Data-driven Innovation : An exploration of outcomes and processes within federated networks
Sammanfattning : The emergence and pervasiveness of digital technologies are changing many aspects of our lives, including what and how we innovate. Industries and societies are competing to embrace this wave of digitalization by developing the right infrastructures and ecosystems for innovation. LÄS MER
2. Data-Driven User Behavior Evaluation
Sammanfattning : Automotive Original Equipment Manufacturers (OEMs) compete worldwide to stand out with new trends and technologies. Automated Driver Assistance Systems (ADAS) are an example of advanced solutions where a lot of effort is put into the development and utilization of vehicle data. LÄS MER
3. Data-driven personalized healthcare : Towards personalized interventions via reinforcement learning for Mobile Health
Sammanfattning : Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. LÄS MER
4. Sensor-based knowledge- and data-driven methods : A case of Parkinson’s disease motor symptoms quantification
Sammanfattning : The overall aim of this thesis was to develop and evaluate new knowledge- and data-driven methods for supporting treatment and providing information for better assessment of Parkinson’s disease (PD).PD is complex and progressive. There is a large amount of inter- and intravariability in motor symptoms of patients with PD (PwPD). LÄS MER
5. Data driven crop disease modeling
Sammanfattning : The concept of precision farming deals with the creation and use of data from machinery and sensors on and off the field to optimize resources and sustainably intensify food production to keep up with increasing demand. However, in the face of a growing amount of data being collected, smarter data processing and analysis techniques are needed and have prompted the evaluation and incorporation of artificial intelligence (AI) and machine learning (ML) techniques for multiple use cases right from seeding to harvesting. LÄS MER