11,156 research outputs found
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap
with arXiv:1403.0623 by other author
Climate Services for Resilient Development (CSRD) Partnership’s work in Latin America
The Climate Services for Resilient Development (CSRD)
Partnership is a private-public collaboration led by USAID,
which aims to increase resilience to climate change in
developing countries through the development and
dissemination of climate services. The partnership
began with initial projects in three countries: Colombia,
Ethiopia, and Bangladesh. The International Center for
Tropical Agriculture (CIAT) was the lead organization for
the Colombian CSRD efforts – which then expanded to
encompass work in the whole Latin American region
A cognitive architecture for emergency response
Plan recognition, cognitive workload estimation and human assistance have been extensively studied in the AI and human factors communities, resulting in many techniques being applied to domains of various levels of realism. These techniques have seldom been integrated and evaluated as complete systems. In this paper, we report on the development of an assistant agent architecture that integrates plan recognition, current and future user information needs, workload estimation and adaptive information presentation to aid an emergency response manager in making high quality decisions under time stress, while avoiding cognitive overload. We describe the main components of a full implementation of this architecture as well as a simulation developed to evaluate the system. Our evaluation consists of simulating various possible executions of the emergency response plans used in the real world and measuring the expected time taken by an unaided human user, as well as one that receives information assistance from our system. In the experimental condition of agent assistance, we also examine the effects of different error rates in the agent's estimation of user's stat or information needs
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