The bumper beam assembly of an automobile is crucial for absorbing impact energy and shielding occupants from front and rear collisions. Crash testing is a type of destructive testing that is typically used to verify ...
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The landscape of computing technologies is changing rapidly, straining existing softwareengineering practices and tools. The growing need to produce and maintain increasingly complex multi-architecture applications m...
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The aging population has led to an increased need for reliable fall detection systems that can provide timely assistance to individuals who have fallen. This paper presents a multi-sensor fusion system designed to det...
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This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweigh...
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In this paper, a bridgeless buck PFC structure is presented. To Fulfill soft switching for the main switches an auxiliary circuitry is utilized. The main switches of the proposed structure operate under ZVS condition ...
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Context and Motivation: Due to their increasing complexity, everyday softwaresystems are becoming increasingly opaque for users. A frequently adopted method to address this difficulty is explainability, which aims to...
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In this paper, we examine the cybersecurity vulnerability assessment method of medical software. Medical software processes patient sensitive data and is linked to various medical devices and systems in real time. Due...
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Medium Voltage Direct Current (MVDC) systems have traditionally been used in specialized applications such as shipboard power systems, railway networks, and more recently, DC links between AC networks. Recently, the g...
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The pervasive issue of fraudulent transactions presents a considerable challenge for financial institutions globally. Developing innovative fraud detection systems is critical to maintaining customer confidence. Howev...
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ISBN:
(纸本)9783031752001;9783031752018
The pervasive issue of fraudulent transactions presents a considerable challenge for financial institutions globally. Developing innovative fraud detection systems is critical to maintaining customer confidence. However, several factors complicate the creating of effective and efficient fraud detection systems. Notably, fraudulent transactions are infrequent, resulting in imbalanced transaction datasets where legitimate transactions vastly outnumber instances of fraud. This data imbalance can concede the performance of fraud detection. Additionally, stringent data privacy regulations prevent the sharing of customer data, hindering the development of high-performing centralized models. Furthermore, fraud detection mechanisms must remain transparent to avoid impairing the user experience. This research proposes an approach utilizing Federated Learning (FL) with Explainable Artificial Intelligence (XAI) to overcome these obstacles. FL allows financial organizations to train fraud detection models collaboratively without requiring direct data sharing. So, customer confidentiality and data privacy are never compromised. Simultaneously, the incorporation of XAI guarantees that the model's predictions are interpretable by human experts. Experimental evaluations using real-time transaction datasets consistently demonstrate that the FL-based fraud detection system performs well. This study establishes the potential of FL as a reliable, privacy-preserving tool in combating fraud.
Modern computer graphics systems usually manage a set of internal data to store control parameters and all kind of graphics data. The behind problem on these graphics system data is that they are frequently updated an...
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