Credit card fraud events take place frequently and then result in huge financial losses [1]. The number of online transactions has grown in large quantities and online credit card transactions holds a huge share of these transactions. Therefore, banks and financial institutions offer credit card fraud detection applications much value and demand. Fraudulent transactions can occur in various ways and can be put into different categories. This paper focuses on four main fraud occasions in real-world transactions. Each fraud is addressed using a series of machine learning models and the best method is selected via an evaluation. This evaluation provides a comprehensive guide to selecting an optimal algorithm with respect to the type of the frauds and we illustrate the evaluation with an appropriate performance measure. Another major key area that we address in our project is real-time credit card fraud detection. For this, we take the use of predictive analytics done by the implemented machine learning models and an API module to decide if a particular transaction is genuine or fraudulent. We also assess a novel strategy that effectively addresses the skewed distribution of data. The data used in our experiments come from a financial institution according to a confidential disclosure agreement.

Real-time Credit Card Fraud Detection Using Machine Learning

Deep learning has emerged as a powerful tool in various domains, revolutionising machine learning research. However, one persistent challenge is the scarcity of labelled training data, which hampers the performance and generalisation of deep learning models. To address this limitation, researchers have developed innovative methods to overcome data scarcity and enhance deep model learning capabilities. Two prevalent techniques that have gained significant attention are transfer learning and self-supervised learning. Transfer learning leverages knowledge learned from pre-training on a large-scale dataset, such as ImageNet, and applies it to a target task with limited labelled data. This approach allows models to benefit from the learned representations and effectively transfer knowledge to new tasks, resulting in improved learning performance and generalisation. On the other hand, self-supervised learning focuses on training models using pretext tasks that do not require manual annotation, allowing them to learn valuable representations from large amounts of unlabelled data. These learned representations can then be fine-tuned for downstream tasks, mitigating the need for extensive labelled data. In recent years, transfer and self-supervised learning have found applications in various fields, including medical image processing, video recognition, and natural language processing. These approaches have demonstrated remarkable achievements, enabling breakthroughs in areas such as disease diagnosis, object recognition, and language understanding. However, while these methods offer numerous advantages, they also have limitations. For example, transfer learning may face domain mismatch issues between the pre-training and target domains, while self-supervised learning requires careful design of pretext tasks to ensure meaningful representations. This review paper explores the recent applications of these pre-training methods in various fields within the past three years. It delves into the advantages and limitations of each approach, assesses the performance of models employing these techniques, and identifies potential directions for future research. By providing a comprehensive review of current pre-training methods, this article offers guidance for selecting the best technique for specific deep learning applications to address the data scarcity issue. 

A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations

Reinforcement learning (RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming (ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively. Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks, showing how they promote ADP formulation significantly. Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has demonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications

Augmented reality apps are a novel technology in e-learning that enhances students’ learning faster in virtual classrooms. Since the start of the COVID-19 pandemic, traditional teaching mechanisms ...

Influence of augmented reality app on intention towards e-learning amidst COVID-19 pandemic

Deep learning-based multi-sensor fusion approach has been widely used for machine condition monitoring. Complementary information from different physical sensors or the same sensors on multiple locations of the monitored target can effectively improve the accuracy of condition monitoring. While, how to distinguish importance of every single sensor for condition monitoring is rarely researched. To address this gap, an adaptive channel weighted convolutional neural network (ACW-CNN) is proposed in this paper to investigate importance of different sensors in fusion approach. The designed ACW layer in a deep neural network can calibrate sensors weight according to sensors importance. The recalibrated channel weights can be used as the guidance for sensor position optimization. Furthermore, a new loss function, that is, Focal Loss, is introduced into ACW-CNN to deal with class imbalance generated in real helicopter transmission system (HTS) monitoring. To validate the effectiveness of the proposed ACW-CNN for condition monitoring, two case studies including condition monitoring to gearbox transmission system and helicopter transmission system are carried out. The results of comparative experiments show the proposed method, that is ACW-CNN with Focal Loss, is superior to other methods in classification accuracy, G-mean, and F-measure for condition monitoring of HTS.

Adaptive Channel Weighted CNN With Multisensor Fusion for Condition Monitoring of Helicopter Transmission System

Abstract Learning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a specific form of learning from demonstration that attempts to estimate the reward function of a Markov decision process from examples provided by the teacher. The reward function is often considered the most succinct description of a task. In simple applications, the reward function may be known or easily derived from properties of the system and hard coded into the learning process. However, in complex applications, this may not be possible, and it may be easier to learn the reward function by observing the actions of the teacher. This paper provides a comprehensive survey of the literature on IRL. This survey outlines the differences between IRL and two similar methods - apprenticeship learning and inverse optimal control. Further, this survey organizes the IRL literature based on the principal method, describes applications of IRL algorithms, and provides areas of future research. 

/pdf/a-survey-of-inverse-reinforcement-learning-2fc5kyf9.pdf

A survey of inverse reinforcement learning

Existing frameworks for transfer learning are incomplete from a systems theoretic perspective. They place emphasis on notions of domain and task, and neglect notions of structure and behavior. In doing so, they limit the extent to which formalism can be carried through into the elaboration of their frameworks. Herein, we use Mesarovician systems theory to define transfer learning as a relation on sets and subsequently characterize the general nature of transfer learning as a mathematical construct. We interpret existing frameworks in terms of ours and go beyond existing frameworks to define notions of transferability, transfer roughness, and transfer distance. Importantly, despite its formalism, our framework avoids the detailed mathematics of learning theory or machine learning solution methods without excluding their consideration. As such, we provide a formal, general systems framework for modeling transfer learning that offers a rigorous foundation for system design and analysis.

A Systems Theory of Transfer Learning

Augmenting a dataset with synthetic samples is a common processing step in machine learning with imbalanced classes to improve model performance. Another potential benefit of synthetic data is the ...

Generative adversarial networks for data augmentation and transfer in credit card fraud detection

Credit card fraud detection can be modeled as an adversarial game between a fraudster and the fraud detection mechanism. Previous work uses a game theoretical adversarial learning approach to model the most successful strategy and preemptively adapt the fraud detection system. The game consists of the adversary's selection of strategy and the fraud detection system's decision to retrain. In the previous work, the detection system is retrained every round of the game. In application, there may be costs and risks associated with training and deploying a new model. Thus, it may be desirable to optimize the decision of whether to retrain the model based on the expected economic disutility. The presented work addresses this desire by using a utilitarian approach to optimally decide whether to retrain the classifier by comparing the economic values of the new and old classifiers. A framework from decision theory is derived within the context of credit card fraud. Further, we show how a utility function can be used to identify the best fraud strategy in economic terms. We add to the literature by extending the adversarial learning model developed in previous work to include a theoretical framework for retraining when it is economically advantageous and judging fraud strategies on their economic cost. Our approaches are tested against the decisions to always retrain and to never retrain.

A utilitarian approach to adversarial learning in credit card fraud detection

Motivating a Systems Theory of AI

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