Education and Health care sectors are two predominant areas where societal growth is expected through innovation and technology development. Machine Learning and Deep learning classification models have been entertained in predicting, detecting, and diagnosing major diseases in the early stage. In this research paper, we have analyzed the impact of hidden dense layers in the Convolution neural network to improve the performance of the classification model. Three different classification deep network models have been constructed, analyzed and the result was tested with a diabetes dataset. Results concluded that the more layers with deeper the network better was the classification performance. The classification model with six hidden dense layers outperforms all other less number of hidden dense layers.

The growth of information technology led to the Internet development that in turn helped people in many ways. The major one is to express their views about the products and services through reviews, blogs, feedback, and comments on the website and in social media. The buyers are forced to go through investigation on these reviews/blogs, before choosing any product or service. Out of all online services, Mobile learning app places a vital role to increase the thirst for knowledge. But to identify the suitable mobile learning app, the opinions of the existing customers need to be mined. This research paper analyzes the mobile learning reviews which are available in the corpus. A novel preprocessing framework is proposed in this paper to improve classification accuracy in the dataset - mobile learning app review dataset. The corpus dimension is reduced using SVD through which, the data is prepared for mining. The classification accuracy is evaluated by applying Multinomial Naïve Bayes, Random Forest data mining algorithms and Learning Vector Quantization (LVQ), Elman Neural Network (ENN), Feed Forward Neural Network (FFNN) algorithms with the dataset obtained by the proposed processing method.

No abstract

No abstract