Computational Sustainability and Artificial Intelligence in the Developing World

The developing regions of the world contain most of the human population and the planet's natural resources, and hence are particularly important to the study of sustainability. Despite some difficult problems in such places, a period of enormous technology-driven change has created new opportunities to address poor management of resources and improve human well-being.

[1]  Lakshminarayanan Subramanian,et al.  Computing the disappearance of crop land using satellite images , 2012 .

[2]  Alberto Rubio,et al.  Measuring the impact of epidemic alerts on human mobility using cell-phone network data , 2011 .

[3]  Jesus Virseda,et al.  Cell Phone Analytics: Scaling Human Behavior Studies into the Millions , 2013 .

[4]  Vanessa Frías-Martínez,et al.  An Agent-Based Model of Epidemic Spread Using Human Mobility and Social Network Information , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[5]  Vanessa Frías-Martínez,et al.  Forecasting socioeconomic trends with cell phone records , 2013, ACM DEV '13.

[6]  Lakshminarayanan Subramanian,et al.  Road traffic congestion in the developing world , 2012, ACM DEV '12.

[7]  Jennifer R. Aduwo,et al.  Automated Vision-Based Diagnosis of Cassava Mosaic Disease , 2010, ICDM.

[8]  John A. Quinn,et al.  Low cost video-based traffic congestion monitoring using phones as sensors , 2013, ACM DEV '13.

[9]  Fred N. Kiwanuka,et al.  Automated Blood Smear Analysis for Mobile Malaria Diagnosis , 2022 .

[10]  Tobey Clark.  Mobile Point-of-Care Monitors and Diagnostic Device Design. , 2015, Biomedical instrumentation & technology.

[11]  Lakshminarayanan Subramanian,et al.  Punjab-IDSS: Dengue surveillance, early detection and containment , 2012 .

[12]  Víctor Soto,et al.  Prediction of socioeconomic levels using cell phone records , 2011, UMAP'11.

[13]  John A. Quinn,et al.  Increased-specificity famine prediction using satellite observation data , 2010, ACM DEV '10.

[14]  Lakshminarayanan Subramanian,et al.  Characterizing dengue spread and severity using internet media sources , 2013, ACM DEV '13.

[15]  J. Virseda,et al.  Computing Cost-Effective Census Maps From Cell Phone Traces , 2012 .

[16]  Umar Saif,et al.  FluBreaks: early epidemic detection from Google flu trends. , 2012, Journal of medical Internet research.

[17]  Vanessa Frías-Martínez,et al.  On the relationship between socio-economic factors and cell phone usage , 2012, ICTD.

[18]  Vanessa Frías-Martínez,et al.  Estimation of urban commuting patterns using cellphone network data , 2012, UrbComp '12.

[19]  Ernest Mwebaze,et al.  Modeling and Monitoring Crop Disease in Developing Countries , 2011, AAAI.

[20]  Wei Chu,et al.  Gaussian Processes for Ordinal Regression , 2005, J. Mach. Learn. Res..

[21]  Lakshminarayanan Subramanian,et al.  Traffic density estimation from highly noise image sources , 2012 .

[22]  John A. Quinn,et al.  Coupling Spatiotemporal Disease Modeling with Diagnosis , 2012, AAAI.

[23]  Lakshminarayanan Subramanian,et al.  Location specific summarization of climatic and agricultural trends , 2011, WWW.