NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.

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Neural network adaptation process effectiveness dependent of constant training data availability

Neurocomputing ◽

10.1016/j.neucom.2009.03.017 ◽

2009 ◽

Vol 72 (13-15) ◽

pp. 3138-3149 ◽

Cited By ~ 20

Author(s):

Ewa Dudek-Dyduch ◽

Ryszard Tadeusiewicz ◽

Adrian Horzyk

Keyword(s):

Neural Network ◽

Data Availability ◽

Training Data ◽

Adaptation Process ◽

Network Adaptation

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Latent Dirichlet Allocation based organisation of broadcast media archives for deep neural network adaptation

2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) ◽

10.1109/asru.2015.7404785 ◽

2015 ◽

Author(s):

Mortaza Doulaty ◽

Oscar Saz ◽

Raymond W. M. Ng ◽

Thomas Hain

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Latent Dirichlet Allocation ◽

Network Adaptation ◽

Broadcast Media ◽

Dirichlet Allocation

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NEAT Snake: a both evolutionary and neural network adaptation approach

10.14210/cotb.v11n1.p052-053 ◽

2020 ◽

Author(s):

Alisson Steffens Henrique ◽

Vinicius Almeida dos Santos ◽

Rodrigo Lyra

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Decision Making ◽

Genetic Algorithms ◽

Real Time ◽

Relative Position ◽

Network Adaptation ◽

The Neural Network

There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.

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