الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Nowadays, big data is one of the most technical challenges confront researchers and companies. The main challenge lies in the fact that big data sources usually formed in a continuous data stream. Thus, many previous researches present incremental data mining approaches to deal with the challenges of the data streams by adapting traditional machine learning algorithms. Artificial Neural Network (ANN) is a common technique in this field. The main challenge is how to optimize the neural network parameters to deal with a huge data arrive overtime. These parameters, which are vital for the performance of a neural network, are called hyperparameters. Earlier optimization approaches have dealt with big data containers instead of big data streams or handled big data streams with time consumed. This thesis proposes an incremental learning approach for ANN hyperparameters optimization over data stream by utilizing Grasshopper Algorithm (GOA) as a swarm intelligence technique. GOA is utilized to make a balance between exploration and exploitation to find the best set of ANN hyperparameters suitable for data stream. The GOA do this by assigning the values of ANN’s hyperparameters. The experimental results confirm that the proposed optimization model yields better accuracy results with appropriate CPU time.