Cracking the Code: How Deep Learning unmasks Complex Fraud Schemes (Published)
In the fast-paced and high-stakes world of finance, the fight against fraud is a continuous and evolving challenge. Deep learning has emerged as a revolutionary tool, capable of processing vast amounts of data and predicting sophisticated fraud patterns with unprecedented accuracy. Unlike traditional rule-based systems, which remain static and predictable, deep learning models dynamically adapt to the ever-changing tactics employed by fraudsters, offering a level of detection that was previously unattainable. Our research delves into the use of advanced transformer models and pre-training techniques, which significantly enhance the precision and flexibility of fraud detection systems. However, implementing deep learning is not without its challenges, including issues related to data quality and the inherent complexity of these models, often referred to as their “black box” nature. Despite these challenges, the benefits are substantial: deep learning not only identifies elusive fraud schemes but also reduces the incidence of false positives, which can be costly and disruptive. Financial institutions are increasingly integrating deep learning with traditional detection methods to create a more robust and comprehensive defense against fraud. Advances in explainable AI are helping to demystify these complex models, making them more transparent and easier to understand. Additionally, transfer learning is enhancing the efficiency of these systems, allowing models trained on one task to be adapted for others with minimal data. This research underscores the critical role of deep learning in strengthening financial systems, providing a formidable barrier against fraud that evolves as quickly as the threats themselves. As financial institutions continue to adopt and refine these technologies, the potential for deep learning to transform fraud detection and prevention is immense. This makes deep learning an indispensable asset in the ongoing battle to protect financial integrity and security.
Keywords: deep learning, explainable AI, financial fraud detection, transfer learning, transformer models
A Comparative Study on the Detection of Pneumonia in Chest X-Ray Images Utilizing Deep Learning Models (Published)
Pneumonia continues to pose a notable public health issue on a global scale, highlighting the crucial need for precise and prompt identification to lessen its effects on patient prognosis. Chest X-ray imaging is a common diagnostic tool for identifying pneumonia due to its non-invasiveness and wide availability. Recently, convolutional neural networks (CNNs), a type of deep learning method, have shown promise in automating the detection of pneumonia from X-ray images. In this paper, we present a comprehensive comparative study of three popular deep learning models—VGG16, Inception V3, and ResNet 50V2—for pneumonia detection in X-ray datasets. The dataset used consists of 5,863 chest X-ray images collected from Kaggle, which are classified into two main categories: pneumonia and normal. The ResNet 50V2 model did very well in the experiments; it was able to correctly identify pneumonia 95.97% of the time, which was better than both VGG16 (93.58%) and Inception V3 (93.38%). We also conduct an analysis of performance metrics such as validation loss, validation accuracy, recall, and precision, and calculate the area under the receiver operating characteristic (ROC) curve (AUC) for each model. We also talk about how to compare ROC curves and precision-recall curves, which lets you see how well the models do at telling the difference between things and how well they do across a number of evaluation metrics. Our study contributes to the development of efficient and high-performance deep learning models for improving the diagnosis and treatment processes for pneumonia patients in the medical field.
Keywords: Chest X-ray, Comparative Analysis, Inception V3, Pneumonia, ResNet 50V2, VGG16, convolutional neural networks, deep learning
Using Deep Learning Convolutional Neural System for Discrete Minke Whale Appreciation (Published)
The only known predictable aggregation of dwarf minke whales .occurs in the Australian offshore waters of the northern Great Barrier Reef in May-August each year. The identification of individual whales is required for research on the whales’ population characteristics and for monitoring the potential impacts of tourism activities, including commercial swims with the whales. At present, it is not cost-effective for researchers to manually process and analyze the tens of thousands of underwater images collated after each observation/tourist season, and a large data base of historical non-identified imagery exists. This study reports the first proof of concept for recognizing individual dwarf minke whales using the Deep Learning Convolutional Neural Networks (CNN).The “off-the-shelf” Image net-trained VGG16 CNN was used as the feature-encoder of the per-pixel sematic seg-mentation Automatic Minke Whale Recognizer (AMWR). The most frequently photographed whale in a sample of 76 individual whales (MW1020) was identified in 179 images out of the total 1320 images provided. Training and image augmentation procedures were developed to compensate for the small number of available images. The trained AMWR achieved 93% prediction accuracy on the testing subset of 36 positive/MW1020 and 228 negative/not-MW1020 images, where each negative image contained at least one of the other 75 whales. Furthermore on the test subset, AMWR achieved 74% precision, 80% recall, and 4% false-positive rate, making the presented approach comparable or better to other state-of-the-art individual animal recognition results.
Keywords: convolutional neural networks, deep learning, dwarf minke whales, image recognition, photo-identification, population biology
Analysis on Deep Learning Performance with Low Complexity (Published)
In this article, presenting deep learning to the LTE-A uplink channel estimation system. This work involved creating two SC-FDMA databases, one for training and one for testing, based on three different channel propagation models. The first part of this work consists in applying artificial neural networks to estimate the channel of the SC-FDMA link. Neural network training is an iterative process consisting of adapting the values (weights and biases) of its parameters. After training, the neural network is tested and implemented in the recipient. The second part of this work addresses the same experiment, but uses deep learning, not traditional neural networks. The simulation results show that deep learning has improved significantly compared to traditional methods for bit error rate and processing speed. The third part of this work is devoted to complexity research. Deep learning has been shown to provide better performance than less complex MMSE estimators.
Keywords: ANN, Artificial Intelligence, LTE-A, SC-FDMA, and portable, channel estimation, communications systems, deep learning, mobile