Computational Sustainability and Artificial Intelligence in the Developing World

@article{Quinn2014ComputationalSA,
  title={Computational Sustainability and Artificial Intelligence in the Developing World},
  author={John A. Quinn and Vanessa Fr{\'i}as-Mart{\'i}nez and Lakshminarayanan Subramanian},
  journal={AI Mag.},
  year={2014},
  volume={35},
  pages={36-47}
}
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. 
View via Publisher
aaai.org
19 Citations
Background Citations
9
Methods Citations
2

Figures from this paper

19 Citations

Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals

Artificial Intelligence (AI) will not just change our lives but bring about revolutionary transformation. AI can augment efficiencies of good and bad things and thus has been considered both an…

Tackling Climate Change with Machine Learning

TLDR
From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.

Societal Benefits and Risks of Artificial Intelligence : A Succinct Survey

TLDR
This work studied the major societal benefits of AI in the areas of health care, education, and agriculture, discussing the positive change that AI has brought to each and addressing the risks society faces when AI is practiced without proper care.

Machine Learning for the Developing World

TLDR
Insight is provided into systematic differences between ML4D and more traditional machine learning applications and how developing world challenges can motivate the design of novel machine learning methodologies.

Key enablers for deploying artificial intelligence for circular economy embracing sustainable product design: Three case studies

TLDR
This research used qualitative research methodology as two stage process and framework on the key roles of AI in circular product design and the key enablers are proposed based on the findings of qualitative research and theoretical part of the study.

A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

TLDR
A qualitative analysis on the elements of the energy and power management ecosystem in the United States is presented and IoE integration, associated trade-offs, challenges, research opportunities and transferable computational techniques are addressed.

Deep Neural Network-Embedded Internet of Social Computing Things for Sustainability Prediction

TLDR
Taking the issue of sustainability prediction as objective situation, deep neural network-embedded Internet of Social Computing Things (NeSoc) is proposed in this paper, guaranteeing comprehensive resource involvement of social computing.

Making distributed edge machine learning for resource-constrained communities and environments smarter: contexts and challenges

TLDR
This paper analyzes representative real-world business scenarios for edge ML solutions and their contexts in resource-constrained communities and environments and identifies and map the key distinguished contexts of distributed edge ML.

Construction and Research of Internet+ Experimental Teaching Platform Based on Artificial Intelligence

  • Shuangyuan Li
  • Computer Science
    Proceedings of the 2019 3rd International Conference on Education, Management Science and Economics (ICEMSE 2019)
  • 2019
The research of Internet + experimental teaching platform based on artificial intelligence makes full use of artificial intelligence, WEB technology and Internet + to transform and improve the…

Leveraging Data Science to Combat COVID-19: A Comprehensive Review

TLDR
This paper attempts to systematise the various COVID-19 research activities leveraging data science, where data science is defined broadly to encompass the various methods and tools that can be used to store, process, and extract insights from data.

References

SHOWING 1-10 OF 29 REFERENCES

Modeling and Monitoring Crop Disease in Developing Countries

TLDR
A spatial disease density model based on Gaussian process ordinal regression is described, which offers a better representation of the disease level distribution, as compared to the statistical approaches typically applied.

Location specific summarization of climatic and agricultural trends

TLDR
The design of a system that mines disparate information sources on the Web to automatically summarize important climatic and agricultural trends for any specific location and construct a location-specific climaticand agricultural information portal is described.

Coupling Spatiotemporal Disease Modeling with Diagnosis

TLDR
A state space model of malaria spread and a computer vision based system for detecting plasmodium in microscopical blood smear images are introduced, and it is demonstrated the tractability of combining both elements and the improvement in accuracy this brings about.

Road traffic congestion in the developing world

TLDR
A local de-congestion protocol that coordinates traffic signal behavior within a small area and can locally prevent congestion collapse sustaining time variant traffic bursts is presented.

Characterizing dengue spread and severity using internet media sources

TLDR
A system that automatically characterizes the spread and severity of the dengue disease at a fine-grained location granularity based on analyzing news reports from Internet media sources is built.

Automated Vision-Based Diagnosis of Cassava Mosaic Disease

TLDR
Assessment of the feasibility of automated computer vision based diagnosis of CMD finds near-perfect classification to be attainable for leaf images captured under ideal conditions, and outlines a method for performing this classification on natural, cluttered images taken in situ.

Increased-specificity famine prediction using satellite observation data

TLDR
The use of remote sensing satellite data to predict food shortages among different categories of households in famine-prone areas is examined and a method for clustering households in such a way that the cluster decision boundaries are both relevant for improved-specificity famine prediction and are easily communicated is described.

An Agent-Based Model of Epidemic Spread Using Human Mobility and Social Network Information

TLDR
An agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading is proposed and applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus.

Low cost video-based traffic congestion monitoring using phones as sensors

TLDR
By designing roadside monitoring units around camera phones, the prototype radically reduces costs compared to convential CCTV systems and hence makes it practical for deployment in this context and results are shown.

Forecasting socioeconomic trends with cell phone records

TLDR
A battery of different predictive approaches for time series are explored and it is shown that multivariate time-series models yield R-square values of up to 0.65 for certain socioeconomic indicators.