Intelligent data analysis for sustainable smart grids using hybrid classification by genetic algorithm based discretization

2017 ◽  
Vol 11 (2) ◽  
pp. 137-151 ◽  
Author(s):  
Mrutyunjaya Panda
Keyword(s):  
Genetic Algorithm ◽  
Data Analysis ◽  
Smart Grids ◽  
Intelligent Data Analysis ◽  
Hybrid Classification

Intelligent data analysis for wellbeing monitoring

2005 ◽  
Author(s):  
B. Majeed ◽  
T. Martin ◽  
N. Clarke ◽  
Beum-Seuk Lee
Keyword(s):  
Data Analysis ◽  
Intelligent Data Analysis

scholarly journals Boosting Intelligent Data Analysis in Smart Sensors by Integrating Knowledge and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6168
Author(s):  
Piotr Łuczak ◽  
Przemysław Kucharski ◽  
Tomasz Jaworski ◽  
Izabela Perenc ◽  
Krzysztof Ślot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Layer Structure ◽  
Recognition Performance ◽  
Conjunctive Normal Form ◽  
Hybrid Structure ◽  
Training Data ◽  
Intelligent Data Analysis ◽  
Feed Forward Neural Network ◽  
Sensor Devices

The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prior knowledge with learning from examples, thus allowing sensor devices to be used for the successful execution of machine learning even when the volume of training data is highly limited, using compact underlying hardware. The proposed architecture comprises two interacting functional modules arranged in a homogeneous, multiple-layer architecture. The first module, referred to as the knowledge sub-network, implements knowledge in the Conjunctive Normal Form through a three-layer structure composed of novel types of learnable units, called L-neurons. In contrast, the second module is a fully-connected conventional three-layer, feed-forward neural network, and it is referred to as a conventional neural sub-network. We show that the proposed hybrid structure successfully combines knowledge and learning, providing high recognition performance even for very limited training datasets, while also benefiting from an abundance of data, as it occurs for purely neural structures. In addition, since the proposed L-neurons can learn (through classical backpropagation), we show that the architecture is also capable of repairing its knowledge.

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