AIMC Topic: Environment

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A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models.

IEEE journal of biomedical and health informatics
We consider the problem in precision health of grouping people into subpopulations based on their degree of vulnerability to a risk factor. These subpopulations cannot be discovered with traditional clustering techniques because their quality is eval...

Predictive risk mapping of human leptospirosis using support vector machine classification and multilayer perceptron neural network.

Geospatial health
Leptospirosis is a zoonotic disease found wherever human is in direct or indirect contact with contaminated water and environment. Considering the increasing number of cases of this disease in the northern part of Iran, identifying areas characterize...

Robot-Assisted Reaching Performance of Chronic Stroke and Healthy Individuals in a Virtual Versus a Physical Environment: A Pilot Study.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The aim of the current study was to examine the role of environment, whether virtual or physical, on robot-assisted reaching movements in chronic stroke and healthy individuals, within a single session. Twenty-three subjects participated in the curre...

An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning.

Journal of medical systems
Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public health problem. The best metho...

An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models.

PloS one
Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variabl...

An Improved DSA-Based Approach for Multi-AUV Cooperative Search.

Computational intelligence and neuroscience
Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and ...

Sensor Information Fusion by Integrated AI to Control Public Emotion in a Cyber-Physical Environment.

Sensors (Basel, Switzerland)
The cyber-physical system (CPS) is a next-generation smart system that combines computing with physical space. It has been applied in various fields because the uncertainty of the physical world can be ideally controlled using cyber technology. In te...

Deep active inference.

Biological cybernetics
This work combines the free energy principle and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies to introduce the "deep active inference" agent. This agent minimi...

Applications of Recurrent Neural Networks in Environmental Factor Forecasting: A Review.

Neural computation
Analysis and forecasting of sequential data, key problems in various domains of engineering and science, have attracted the attention of many researchers from different communities. When predicting the future probability of events using time series, ...

Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran.

Acta tropica
The distribution and abundance of Phlebotomus papatasi, the primary vector of zoonotic cutaneous leishmaniasis in most semi-/arid countries, is a major public health challenge. This study compares several approaches to model the spatial distribution ...