AIMC Topic: Malaysia

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Predictive analysis across spatial scales links zoonotic malaria to deforestation.

Proceedings. Biological sciences Jan 16, 2019
The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechani...
Humans Machine Learning Cross-Sectional Studies Animals Case-Control Studies Models, Theoretical Malaysia Models, Statistical Remote Sensing Technology Malaria Forests Spacecraft Spatial Analysis Epidemiological Monitoring Zoonoses Forestry Plasmodium knowlesi
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Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.

International journal of phytoremediation May 4, 2017
Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied...
Neural Networks, Computer Waste Disposal, Fluid Water Pollutants, Chemical Malaysia Plant Oils Environmental Restoration and Remediation Biodegradation, Environmental Industrial Waste Biological Oxygen Demand Analysis Manufacturing Industry Chrysopogon Palm Oil
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Artificial neural network modeling of the water quality index using land use areas as predictors.

Water environment research : a research publication of the Water Environment Federation Feb 1, 2015
This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were em...
Neural Networks, Computer Prognosis Agriculture Models, Theoretical Environmental Monitoring Malaysia Water Quality Rivers Conservation of Natural Resources City Planning Forestry
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Secondary triage classification using an ensemble random forest technique.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2015
BACKGROUND: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time.
Humans Reproducibility of Results Support Vector Machine Age Factors Malaysia Triage Emergency Service, Hospital Vital Signs
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