Deep learning is a machine learning method
that attempts to learn layered models of inputs that mimics the human brain’s
reasoning process. The layers correspond to distinct levels of concepts where
higher-level concepts are derived from lower-level concepts (hierarchy of
complex concepts that are constructed out of simpler concepts).
US20150294422 illustrates the deep learning
application for dynamic risk management in the IoT connected cars. The deep
learning correlates the driver's usage patterns, driving styles, weather
conditions, traffic or interaction with the vehicle based on a priori knowledge
of how normalized driver data sets behave to predict potential driving risk
dynamically. Real time data are provided using the vehicle-to-infrastructure
(V2I) and vehicle-to-vehicle (V2V) communications. US20150166072 illustrates another deep
learning application for dynamic risk management in the IoT connected cars. The
deep learning correlates multiple vehicles behavior as a function of their
environment (e.g., road conditions pertain to an individual vehicle) in order
to predict vehicle behavior in future environments.
Evaluation of a living environment such as an
interior space highly influences residents and users of the space, the space itself,
and uses and purposes of the space. US20140241627 illustrates the deep learning
application for evaluating the effect of the lighting on human feelings in the
IoT smart home. The deep learning correlates the brightness of the overall
space and human feelings toward images to classify the human feeling categories.
The classification can be used to provide a better smart home lighting system. US20150286638 illustrates another deep
learning application for recognizing the scene type from a set of images in the
IoT smart home. The deep learning correlates the set of images to learn new
features. The scene recognizing system
can be used to provide the context-aware smart home services.
Distributed power generation refers to a mode
of power generation in which electricity is generated by energy sources at
locations distributed across the power grid. US20140340236 illustrates the deep
learning application for securing the distributed power distribution networks in
the IoT smart grids. The basic process in the security surrounding the power
distribution networks involves the notion of discriminating between normal
operations and anomalous operations. The deep learning can be used to discriminate
between a signature considered normal, and one that is abnormal, or as well as
sub-classes of abnormality due to malicious or non-malicious perturbations of
the networks. The deep learning is trained on a training set of timing of
communication input labeled as "normal." Sets of abnormal input can
be labeled and used to train the deep learning to discriminate particular
classes of abnormal signatures.
US20150142466 illustrates the deep learning
application for providing remote medical diagnosis and therapy of the IoT
connected healthcare. The deep learning predicts a risk of mortality and/or
morbidity based on the statistical modeling of health outcomes from historic
and real-time health data.
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