Distributed Search Engine Query Optimization Using Artificial Neural Network

Ali, Liaqat; Al-Sakhnini, Mahmoud M.; Kalra, Deepak; Afzaal, Farheen; Pervaiz, Madiha; Khan, Muhammad Farrukh

doi:10.1109/iccr56254.2022.9995958

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Depending on the context in which they appear in a word, Arabic letters may be written in a wide variety of different ways. For instance, the letter "‫/هأ‬ha" may appear like ‫هـ"‬ ‫ـهـ/‬ ‫"ـه/‬ depending on where it is in the word, whether it is at the beginning, in the middle, or at the conclusion of the sentence [13,14]. Take, for example:…”

Section: Various Forms Of Lettersmentioning

confidence: 99%

Python Solutions to Address Natural Language Challenges

Moaiad,

Alobed,

Alsakhnini

2024

ijmst

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Arabic is one of six official languages, according to UNESCO. It's spoken by more than 422 million Arabs, and 1.5 billion Muslims around the world use it when they pray five times a day. Arabs spoke classical Arabic more than 1400 years ago. On the other hand, dialectal Arabic is the everyday language that is used informally and varies from region to region. Modern Standard Arabic borrows from and adds to other languages to fit the needs of its speakers. Arabic is harder to learn because there are three different ways to speak it: the classical way, the modern way, and the casual way. Arabic is hard to work with on computers for more than one reason. Because Arabic has a lot of inflection and derivation, one lemma can turn into many different words with different meanings.

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Section: Various Forms Of Lettersmentioning

confidence: 99%

Python Solutions to Address Natural Language Challenges

Moaiad,

Alobed,

Alsakhnini

2024

ijmst

View full text Add to dashboard Cite

show abstract

“…[12] HITS Algorithm The algorithm is known as an effective algorithm used for rating and ranking documents in websites based on the link information insert by user in certain areas. [13] Artificial Neural Network ANN algorithm was implemented to do prediction and optimized the system performance. [14] Artificial Neural Network and Genetic Algorithm GA algorithm combined with developed ANN model to get optimal design energy performance.…”

Section: Table 1 -Related Work On Search Engine Optimization Researchersmentioning

confidence: 99%

AComparative Study of Artificial Neural Network and Genetic Algorithm in Search Engine Optimization

Mohamad Madon,

Mohd. Yasin

2023

JSCDM

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Search engine optimization applies search principles in search engines to assign a higher ranking to the most suitable webpage. Nowadays, information searching is done ubiquitously on the World Wide Web with the help of search engines. However, the process needs to be efficient and produce accurate results simultaneously. In this research, the objectives are to implement and evaluate the Artificial Neural Network and Genetic Algorithms. The accuracy result for both algorithms is compared by implementing keyword ranking, Search Engine Result Page visibility, and time retrieval for document-based and e-commerce websites. To achieve them, firstly, the problem and data are defined. Next, two datasets are importedfrom Kaggle and transformed into a more helpful format. Then, the Artificial Neural Network and Genetic Algorithms are implemented on these datasets in Python using Jupyter Notebook tools. Subsequently, the accuracy of these datasetskeyword ranking, Search Engine Result Page visibility, and time retrieval areobserved based on the output and graph. Lastly, an analysis of the results is performed. Conclusively, the Genetic Algorithm demonstrates a higher percentage of accuracy results than the Artificial Neural Network algorithm in keyword ranking and SERP visibility. However, the accuracy results of time retrieval are vice versa. The results in Genetic Algorithm show 9.0%, 9.0%, and 3.0% in the e-commerce dataset for keyword ranking and 4.0%, 51.0%, and 1.0% in the document-based dataset for SERP visibility. Next, the Artificial Neural Network algorithm shows results of 8.0%, 7.0%, and 7.0% in the e-commerce dataset and 3.0%, 50.0%, and 4.0% in the document-based dataset for time retrieval. Therefore, the results validated the ability of the Genetic Algorithm as one of the most applied algorithms in the search engine optimization field.

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Distributed Search Engine Query Optimization Using Artificial Neural Network

Cited by 2 publications

References 26 publications

Python Solutions to Address Natural Language Challenges

Python Solutions to Address Natural Language Challenges

AComparative Study of Artificial Neural Network and Genetic Algorithm in Search Engine Optimization

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