A Hybrid Method for Big Data Analysis Using Fuzzy Clustering, Feature Selection and Adaptive Neuro-Fuzzy Inferences System Techniques: Case of Mecca and Medina Hotels in Saudi Arabia

Alghamdi, Abdullah

doi:10.1007/s13369-022-06978-0

Cited by 10 publications

(6 citation statements)

References 79 publications

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“…Feature selection is technically an essential step in data modelling that involves reducing the number of input variables to improve efficiency and effectiveness [ 21 ]. It acts as a strategic filter, sifting through the available features and filtering out those that contain the most relevant and meaningful information.…”

Section: Feature Selectionmentioning

confidence: 99%

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Fractal feature selection model for enhancing high-dimensional biological problems

Alsaeedi,

Al-Mahmood,

Alnaseri

et al. 2024

BMC Bioinformatics

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The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%.

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Section: Feature Selectionmentioning

confidence: 99%

“…The main goal of modern feature selection models is to enhance system performance by strategically removing redundant attributes, thereby streamlining the analysis process. [ 21 ]. The current feature selection model, or wrapper or filter, has several limitations that can be summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Fractal feature selection model for enhancing high-dimensional biological problems

Alsaeedi,

Al-Mahmood,

Alnaseri

et al. 2024

BMC Bioinformatics

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“…The feature selection of CART algorithm for continuous type metrics is implemented based on mean square error (MSE) [10]. In this study, the quantitative value of the attributes of the word was used as the basis for each node division and selected the optimal division value.…”

Section: Decision Tree Modelmentioning

confidence: 99%

Decision tree based prediction system for word difficulty classification

Jiang

2024

ACE

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In the era of information technology in education, personalised learning is becoming increasingly important, especially in the English learning process. An important aspect of facilitating personalised learning in English reading is the use of a reliable objective word difficulty classification system to quickly capture and understand reading difficulties and core concepts. The aim of this study was to use decision tree modelling to predict the general proficiency of the public in words with different attributes. In addition, a K-Means clustering algorithm was used to categorise words into five classes based on their level of difficulty. By adopting this approach, the prediction of word difficulty becomes both fast and objective, and by testing the accuracy of the model, it was found that our model achieved an accuracy of 0.95. Accurate classification of word difficulty will play an important role in facilitating personalised learning in English reading and improve the efficiency of English reading.

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“…Still, some studies were empirical papers focused on one business type [11] [35] or one business size [36] [13], applying a detection model with successful results. However, the application of big data and ML in the Saudi business research domain was focused on marketing [38] and finance [39] but never on accounting. On the other hand, accounting professionals have been using ML models, especially in some audit procedures has been employed in accounting firms.…”

Section: E the Case Of Saudi Arabiamentioning

confidence: 99%

CADM: Big Data to Limit Creative Accounting in Saudi-Listed Companies

Bineid,

Beloff,

White

et al. 2023

Annals of Computer Science and Information Systems

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Global financial scandals have demonstrated the harmful impact of creative accounting, a practice where managers creatively manipulate financial reports to conceal a company's actual performance and influence stakeholders' decision-making. Studies showed that Saudi-listed companies use it in preparing financial statements. Despite posing a significant risk to the Saudi financial market, detecting it using ordinary auditing procedures remains challenging. Big data analytics has provided practical applications in auditing, and recently, the employment of Deep Learning in fraud detection has delivered remarkably accurate results. Still, limited research has considered it in detecting creative accounting. This study proposes a novel framework using a hybrid learning approach. It suggests training on a simulated dataset of financial statements prepared (i.e., deliberately manipulated) based on financial statements available in the literature for supervised learning. It is then tested on real-world financial reports from the Saudi Open Data and Saudi Statistics. Our framework contributes to the literature with a new governing approach to limit creative accounting and improve financial reporting quality.

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A Hybrid Method for Big Data Analysis Using Fuzzy Clustering, Feature Selection and Adaptive Neuro-Fuzzy Inferences System Techniques: Case of Mecca and Medina Hotels in Saudi Arabia

Cited by 10 publications

References 79 publications

Fractal feature selection model for enhancing high-dimensional biological problems

Fractal feature selection model for enhancing high-dimensional biological problems

Decision tree based prediction system for word difficulty classification

CADM: Big Data to Limit Creative Accounting in Saudi-Listed Companies

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