AIMC Topic: Poverty

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Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters.

International journal of environmental research and public health
Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children's TMC prediction stu...

Balancing national economic policy outcomes for sustainable development.

Nature communications
The 2030 Sustainable Development Goals (SDGs) aim at jointly improving economic, social, and environmental outcomes for human prosperity and planetary health. However, designing national economic policies that support advancement across multiple Sust...

Machine learning and phone data can improve targeting of humanitarian aid.

Nature
The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have distributed ...

Multivariate random forest prediction of poverty and malnutrition prevalence.

PloS one
Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies' programming. However, state of the art models ofte...

Artificial intelligence for good health: a scoping review of the ethics literature.

BMC medical ethics
BACKGROUND: Artificial intelligence (AI) has been described as the "fourth industrial revolution" with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health ...

Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries.

Globalization and health
The World Health Organization and other institutions are considering Artificial Intelligence (AI) as a technology that can potentially address some health system gaps, especially the reduction of global health inequalities in low- and middle-income c...

Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation.

PloS one
Participatory modelling is an emerging approach in the decision-making process through which stakeholders contribute to the representation of the perceived causal linkages of a complex system. The use of fuzzy cognitive maps (FCMs) for participatory ...

COVID-19: The role of artificial intelligence in empowering the healthcare sector and enhancing social distancing measures during a pandemic.

South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
Indiscriminatory in its spread, COVID-19 has engulfed communities from all social backgrounds throughout the world. While healthcare professionals work tirelessly testing for the virus and caring for patients, they too have become casualties of the p...

Ensemble machine learning and forecasting can achieve 99% uptime for rural handpumps.

PloS one
Broken water pumps continue to impede efforts to deliver clean and economically-viable water to the global poor. The literature has demonstrated that customers' health benefits and willingness to pay for clean water are best realized when clean water...

Reflections of Low-Income, Second-Generation Latinas About Experiences in Depression Therapy.

Qualitative health research
Depression is higher among second-generation Latinas compared with immigrants, but mental health treatment is stigmatized. Therefore, second-generation Latinas were interviewed after completing an eight-session depression treatment program to gain in...