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Showing 2 results for Xgboost
Mrs Sareh Bagheri Matak, Dr Elham Askari, Dr Sara Motamed,
Volume 16, Issue 2 (8-2025)
Abstract
Leukemia is one of the most common and dangerous types of cancer in the world. In many cases, the disease is curable if detected in its early stages. One of the effective tools for early detection is the analysis of microarray data, which measures the expression of thousands of genes simultaneously. However, the large volume of features and the presence of noise make the analysis process complex and time-consuming. Therefore, the selection of effective genes plays a key role in increasing the accuracy and reducing the computational cost of learning models. In this paper, a two-step hybrid approach is presented for feature selection and classification of leukemia types. In the first step, the features are filtered using the mutual information criterion and the genes with the highest correlation with the disease label are selected. In the second step, the XGBoost model is used to rank and stably select the features to identify the genes that are most important in different iterations. In the final stage, classification will be performed using the temporal fusion transformer method, which allows for fast and efficient learning of complex patterns among selected genes. Experimental results on real microarray datasets show that the proposed method outperforms the baseline methods with an accuracy of 99.2% and has been able to identify key genes effective in differentiating leukemia types by effectively reducing the data dimensions.
Dr Mehdi Farrokhbakht, Mr Ali Akbar Akhavan,
Volume 16, Issue 2 (8-2025)
Abstract
Fraud is a phenomenon that involves deviations and manipulations in financial statements. These actions can lead to tax non-compliance and erode the trust of investors and other stakeholders. Given the intricate nature and vast amount of financial data within organizations, leveraging artificial intelligence as a sophisticated tool can greatly enhance fraud detection in financial statements and bolster confidence in the face of evolving fraudulent tactics. Fraud or deception in the financial information of individuals or organizations reduces the level of trust and confidence that people have in the reliability and integrity of this information. This can lead to serious negative impacts, including loss of trust from customers, investors, and other entities, negative financial and legal consequences, and the exposure of illegal or improper operations that may involve financial crimes. This paper introduces an intelligent method for detecting fraud in financial statements. Initially, the Apriori algorithm is utilized to select pertinent features in the financial data. Subsequently, the performance of the proposed method is enhanced by augmenting the dataset using the GAN-CNN network. Finally, fraud detection is executed with the assistance of XGBoost. The results demonstrate that the proposed method has achieved a fraud detection accuracy of 95.3%.
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