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Environment

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Deep(er) Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...

Anthropogenic activities impact on atmospheric environmental quality in a gas-flaring community: application of fuzzy logic modelling concept.

Environmental science and pollution research international
We present a modelling concept for evaluating the impacts of anthropogenic activities suspected to be from gas flaring on the quality of the atmosphere using domestic roof-harvested rainwater (DRHRW) as indicator. We analysed seven metals (Cu, Cd, Pb...

Development of GP and GEP models to estimate an environmental issue induced by blasting operation.

Environmental monitoring and assessment
Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The...

Vector-based navigation using grid-like representations in artificial agents.

Nature
Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcem...

Market penetration of intersection AEB: Characterizing avoided and residual straight crossing path accidents.

Accident; analysis and prevention
Car occupants account for one third of all junction fatalities in the European Union. Driver warning can reduce intersection accidents by up to 50 percent; adding Autonomous Emergency Braking (AEB) delivers a reduction of up to 70 percent. However, t...

TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment.

Computational intelligence and neuroscience
This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a mobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train...

Risk-Predicting Model for Incident of Essential Hypertension Based on Environmental and Genetic Factors with Support Vector Machine.

Interdisciplinary sciences, computational life sciences
Essential hypertension (EH) has become a major chronic disease around the world. To build a risk-predicting model for EH can help to interpose people's lifestyle and dietary habit to decrease the risk of getting EH. In this study, we constructed a EH...

Reactive navigation in extremely dense and highly intricate environments.

PloS one
Reactive navigation is a well-known paradigm for controlling an autonomous mobile robot, which suggests making all control decisions through some light processing of the current/recent sensor data. Among the many advantages of this paradigm are: 1) t...

Optic flow-based collision-free strategies: From insects to robots.

Arthropod structure & development
Flying insects are able to fly smartly in an unpredictable environment. It has been found that flying insects have smart neurons inside their tiny brains that are sensitive to visual motion also called optic flow. Consequently, flying insects rely ma...

Continuous detection of human fall using multimodal features from Kinect sensors in scalable environment.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Automatic detection of human fall is a key problem in video surveillance and home monitoring. Existing methods using unimodal data (RGB / depth / skeleton) may suffer from the drawbacks of inadequate lighting condition or u...