Design and implementation of a robust system for recognizing alphabets using artifical neural network

Kapoor, Sanjiv; Singh, Ashish; Goswami, Utkarsh; Kumar, Saswat; Chitranshi, Gaurav

doi:10.1109/icrito.2016.7785026

Cited by 1 publication

(1 citation statement)

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“…ANNs are particular computational models, which are able to represent knowledge based on massive parallel processing and pattern recognition based on past experience or examples [37]. An ANN is defined through an initial layer on the basis of the available inputs, a final layer which represents the output of the computation and a hidden layer which is defined in terms of potential multi-layers through which the inputs undergo various transformation and calculation steps as long as the final layer is reached and the output is generated.…”

Section: Background On Machine Learningmentioning

confidence: 99%

A Feature-Based Model for the Identification of Electrical Devices in Smart Environments

Tundis

Faizan

Mühlhäuser

2019

Sensors

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Smart Homes (SHs) represent the human side of a Smart Grid (SG). Data mining and analysis of energy data of electrical devices in SHs, e.g., for the dynamic load management, is of fundamental importance for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and reduction of the carbon dioxide emission, by knowing how the energy demand of a building is composed in the SG. Advanced monitoring and control mechanisms are necessary to deal with the identification of appliances. In this paper, a model for their automatic identification is proposed. It is based on a set of 19 features that are extracted by analyzing energy consumption, time usage and location from a set of device profiles. Then, machine learning approaches are employed by experimenting different classifiers based on such model for the identification of appliances and, finally, an analysis on the feature importance is provided.

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Section: Background On Machine Learningmentioning

confidence: 99%