Related papers: Using Machine Learning to Create an Early Warning …
Machine learning-based estimates of poverty and wealth are increasingly being used to guide the targeting of humanitarian aid and the allocation of social assistance. However, the ground truth labels used to train these models are typically…
Accurate weather prediction is essential for many aspects of life, notably the early warning of extreme weather events such as rainstorms. Short-term predictions of these events rely on forecasts from numerical weather models, in which,…
This paper presents an out-of-sample prediction comparison between major machine learning models and the structural econometric model. Over the past decade, machine learning has established itself as a powerful tool in many prediction…
There is increasing interest in the use of HPC machines for urgent workloads to help tackle disasters as they unfold. Whilst batch queue systems are not ideal in supporting such workloads, many disadvantages can be worked around by…
Despite their popularity, machine learning predictions are sensitive to potential unobserved predictors. This paper proposes a general algorithm that assesses how the omission of an unobserved variable with high explanatory power could…
Early warning systems (EWSs) are critical for forecasting and preventing economic and financial crises. EWSs are designed to provide early warning signs of financial troubles, allowing policymakers and market participants to intervene…
Local authorities in England, such as Leicestershire County Council (LCC), provide Early Help services that can be offered at any point in a young person's life when they experience difficulties that cannot be supported by universal…
The proliferation of early diagnostic technologies, including self-monitoring systems and wearables, coupled with the application of these technologies on large segments of healthy populations may significantly aggravate the problem of…
Competitive analysis of online algorithms has commonly been applied to understand the behaviour of real-time systems during overload conditions. While competitive analysis provides insight into the behaviour of certain algorithms, it is…
In this paper, machine learning models are used to predict outcomes for patients with persistent post-concussion syndrome (PCS). Patients had sustained a concussion at an average of two to three months before the study. By utilizing…
This paper studies algorithmic decision-making in the presence of strategic individual behaviors, where an ML model is used to make decisions about human agents and the latter can adapt their behavior strategically to improve their future…
Homelessness among US veterans remains a critical public health challenge, yet risk prediction offers a pathway for proactive intervention. In this retrospective prognostic study, we analyzed electronic health record (EHR) data from…
With the availability of high precision digital sensors and cheap storage medium, it is not uncommon to find large amounts of data collected on almost all measurable attributes, both in nature and man-made habitats. Weather in particular…
Many works in biomedical computer science research use machine learning techniques to give accurate results. However, these techniques may not be feasible for real-time analysis of data pulled from live hospital feeds. In this project,…
In many nations, diabetes is becoming a significant health problem, and early identification and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Diabetes…
There is significant interest in deploying machine learning algorithms for diagnostic radiology, as modern learning techniques have made it possible to detect abnormalities in medical images within minutes. While machine-assisted diagnoses…
The prominent inequality of wealth and income is a huge concern especially in the United States. The likelihood of diminishing poverty is one valid reason to reduce the world's surging level of economic inequality. The principle of…
Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior…
An estimated 300 million people worldwide suffer from asthma, and this number is expected to increase to 400 million by 2025. Approximately 250,000 people die prematurely each year from asthma out of which, almost all deaths are avoidable.…
Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising…