Improving Lithium Battery: Application of Data Mining

Name: Improving Lithium Battery: Application of Data Mining
Brand: LAP Lambert Academic Publishing
SKU: 9786202025140
Price: 238.87 RON
Availability: InStock

Autor: Bob Chile-Agada

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Legare: Carte broșată

Editura: LAP Lambert Academic Publishing

Disponibilitate: În depozitul extern

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In an age controlled by improved technological innovations and ideas, there is some increasing gradu...

Informații despre carte

Autor

Bob Chile-Agada

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2017

Pagini

104

EAN

9786202025140

Enbook ID

18627987

Editura

LAP Lambert Academic Publishing

Greutate

173

Dimensiuni

150 x 220 x 6

Descriere completă

In an age controlled by improved technological innovations and ideas, there is some increasing gradual depletion in the performance measure of lithium battery which has triggered a shift in its efficiency. This text provides an improvement into lithium battery data life performance prediction using Weka 3.7.1. The classification technique was used to classify the massively extracted dataset from Arbin BT 2000 Battery Testing Repository. However, based on the huge size of the dataset, 20% which was adequate when dealing with huge data size yielded 10,001 instances with 14 attributes. The Multi-Layer Perceptron, Sequential Minimal Optimisation, and Naïve Bayes were the algorithms used to perform the lithium battery data mining, the efficiency. Furthermore, the researcher applied k-fold cross-validation with 90% training data and 10% test data which realised Multi-Layer Perceptron 99.6%, Sequential Minimal Optimization is 99.7%, and Naïve Bayes is 97% with an insignificant error rate.

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Improving Lithium Battery: Application of Data Mining

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