Non-vocalized Arabic words are ambiguous words, because non-vocalized words may have different meanings. Therefore, these words may have more than one root. Many Arabic root extraction algorithms have been conducted to extract the roots of non-vocalized Arabic words. However, most of them return only one root and produce lower accuracy than reported when they are tested on different datasets. Arabic root extraction algorithm is an urgent need for applications like information retrieval systems, indexing, text mining, text classification, data compression, spell checking, text summarization, question answering systems and machine translation. In this work, a new rule-based Arabic root extraction algorithm is developed and focuses to overcome the limitation of previous works. The proposed algorithm is compared to the algorithm of Khoja, which is a well-known Arabic root extraction algorithm that produces high accuracy. The testing process was conducted on the corpus of Thalji, which is mainly built to test and compare Arabic roots extraction algorithms. It contains 720,000 word-root pairs from 12000 roots, 430 prefixes, 320 suffixes, and 4320 patterns. The experimental result shows that the algorithm of Khoja achieved 63%, meanwhile the proposed algorithm achieved 94% of accuracy.

The only link between the seller and the buyer is the information that available on the trading platforms about the products. All researchers in e-commerce agreed on the importance of providing information on products and services in e-commerce platforms to achieve customer satisfaction This paper aims to reveal how the availability of information on E-commerce platform about After-Sales Services can affect on customers’ purchase decisions. The study used descriptive research and the Kingdom of Saudi Arabia’s Dammam city as the study population. Also, it used the convenience random sampling approach to overcome bias in this research. The study covered all dimensions of After-Sales Services. 533 respondents completed the study questionnaire. The researcher used many statistical analyses tests before using deep learning. Furthermore, the study used hierarchical clustering and K-means to cluster consumers. The results show that respondents of this research can be clustered in three different groups based on behavior regarding demographic attributes. This study’s findings show that there is a positive relationship between the dimensions of and customers’ purchase decisions. the study presented several suggestions for platforms to keep their customers and make more profits.

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