Journal of Computational Methods in Engineering Applications

Efficient Bank Fraud Detection with Machine Learning

Rong Zhang — 2023-10-23

The rapid expansion of IT technology has led to a significant increase in financial transactions, accompanied by a corresponding rise in fraudulent activities. This paper tackles the challenge of detecting fraud in various forms, such as credit card fraud, banker cheque fraud, and online funds transfer fraud, which have become increasingly sophisticated. Traditional methods struggle to keep pace with these evolving fraud techniques, necessitating advanced approaches. We propose the use of machine learning algorithms to enhance the detection of fraudulent transactions. Utilizing the BankSim dataset from Kaggle, which includes features like age, gender, payment domain, and transaction amount, we conducted a comprehensive analysis. The dataset was preprocessed to handle missing values and balance the instances of fraud. We then applied several machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM), training these models on a training set and evaluating them on a test set. The performance of these models was assessed using precision, recall, and F1-measure metrics. Our findings demonstrate that the SVM algorithm achieved the highest accuracy at 99.23%, significantly outperforming the other algorithms and previous studies. This study highlights the effectiveness of machine learning, particularly SVM, in developing robust fraud detection systems, offering a promising solution to improve financial security.

Deep Learning-Based Multifunctional End-to-End Model for Optical Character Classification and Denoising

Shuguang Xiong — 2023-11-15

Optical Character Recognition (OCR) has revolutionized document processing by converting scanned documents, PDFs, and images captured by cameras into editable and searchable text. This technology is crucial for digitizing historical documents, streamlining data entry processes, and improving accessibility for the visually impaired through text-to-speech technologies. Despite its widespread application, OCR faces significant challenges, especially in accurately recognizing text in noisy or degraded images. Traditionally, OCR systems have treated noise reduction and character classification as separate stages, which can compromise the overall effectiveness of text recognition. Our research introduces a groundbreaking Multifunctional End-to-End Model for Optical Character Classification and Denoising, which integrates these functions within a unified framework. By employing a dual-output autoencoder, our model concurrently denoises images and recognizes characters, thereby enhancing both the efficiency and accuracy of OCR. This paper outlines the model's development and implementation, explores the interplay between denoising and classification, and presents compelling experimental results that demonstrate marked improvements over conventional OCR methods.

Machine Learning-Based System Reliability Analysis with Gaussian Process Regression

Lisang Zhou — 2023-11-13

Machine learning-based reliability analysis methods have shown great advancements for their computational efficiency and accuracy. Recently, many efficient learning strategies have been proposed to enhance the computational performance. However, few of them explores the theoretical optimal learning strategy. In this article, we propose several theorems that facilitates such exploration. Specifically, cases that considering and neglecting the correlations among the candidate design samples are well elaborated. Moreover, we prove that the well-known U learning function can be reformulated to the optimal learning function for the case neglecting the Kriging correlation. In addition, the theoretical optimal learning strategy for sequential multiple training samples enrichment is also mathematically explored through the Bayesian estimate with the corresponding lost functions. Simulation results show that the optimal learning strategy considering the Kriging correlation works better than that neglecting the Kriging correlation and other state-of-the art learning functions from the literatures in terms of the reduction of number of evaluations of performance function. However, the implementation needs to investigate very large computational resource.

Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China

Ping Ren — 2023-11-20

In recent years, with the changing landscape of international education and the growing demand from Chinese students, study abroad consultancy firms in China need to adopt transformational development strategies to address challenges and maintain competitiveness. This study investigated the relationships between key performance indicators and several factors through a questionnaire survey of 158 consultancy firms. The factors examined included service diversification, technology adoption, talent management, and regulatory compliance. Descriptive statistical analysis was employed to analyze the data. The results showed that service scope diversification was positively correlated with firm performance. Technology adoption was positively correlated with operational efficiency. Talent management was positively correlated with service quality. Regulatory compliance was positively correlated with firm reputation. Consultancy firms that took progressive approaches in diversifying services, adopting new technologies, cultivating talent, and ensuring compliance demonstrated superior performance, efficiency, quality, and reputation compared to their less innovative counterparts. This research provides empirical evidence to support the transformation of Chinese study abroad consultancy firms. It also highlights the need for future studies to consider causality and contextual variations to gain deeper insights into this issue.