The quality of the web page classification process has a huge impact on information retrieval systems. In this paper, we proposed to combine the results of text and image data classifiers to get an accurate representation of the web pages. To get and analyse the data we created the complicated classifier system with data miner, text classifier, and aggregator. The process of image and text data classification has been achieved by the deep learning models. In order to represent the common view onto the web pages, we proposed three aggregation techniques that combine the data from the classifiers.

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Estimating the text preprocessing algorithms in machine learning classification task helps to select the most accurate combination of word embedding text processing algorithms to map the test data samples to appropriate labels. Increasing the accuracy in classification of text data improves afterwards the response to queries in search engines and gives more relevant results. This work includes several preprocess text data techniques and their combinations for following sentence classification by using support vector machine (SVM). I.

This article presented a survey of two well-known algorithms, TF-IDF and BM-25 methods, for document ranking on a single CPU and parallel processes via HPC. An amazon review dataset with more than two million reviews was measured to measure the rank parameters. We set up the number of workers for the parallel processing during the experiment, which we selected as one and three. Four benchmarks evaluated the preprocess and reading time, vectorization time, TF-IDF transformation time, and overall time. Results metrics have shown a significant difference in speed.