Community financial institutions — community banks, credit unions, and small savings institutions — face the same two loss-generating problems as large, well-capitalized institutions: transaction and account fraud that erodes customer trust and capital, and...
Efficient anomaly detection in big data streams is critical for modern applications such as cybersecurity, Internet of Things (IoT), and financial fraud prevention. Traditional approaches struggle to handle the dynamic, high-dimensional, and interconnected...
This paper discusses and analyzes the transformative role of artificial intelligence in fraud detection within healthcare systems, emphasizing a data-driven approach. The integration of AI in fraud detection not only enhances accuracy but also minimizes...
The rapid adoption of cloud-optimized data engineering systems has revolutionized the way organizations handle vast volumes of data. These systems offer unmatched scalability, flexibility, and cost-efficiency. However, their increasing complexity and...
Due to the overwhelming number of solutions for Big Data processing during the last few years, distributed data engineering frameworks have become one of the most effective tools for real-time analyses and decision-making in numerous domains. However, these...
Data validation is a critical yet resource-intensive component of citizen science projects. As the volume and complexity of citizen science data continue to grow, traditional approaches to quality assurance—relying on expert review or simple filtering rules...
The relationship between artificial intelligence and data quality is fundamentally bidirectional: AI systems depend on high-quality data for reliable performance, while AI techniques offer powerful tools for assessing, monitoring, and improving data quality...
This paper presents a comprehensive AI-driven framework tailored for intelligent threat detection and response within critical infrastructure systems, including but not limited to energy grids, water treatment facilities, and transportation networks. With...
The swift expansion of cloud-based online applications and digital financial services has markedly heightened the intricacy of security risks and financial crime. Conventional rule-based security solutions and standalone analytics platforms are inadequate...
As company networks become more complicated and cyber-attacks more frequent, insider threats have surfaced as a considerable concern to organizational security. Artificial Intelligence (AI) have the capacity to transform how organizations discover and...
Multi-tenant cloud computing scenarios have a high level of security risks because tenants share the same hardware and network. Control of data privacy, access to resources and isolation of these resources pose a significant challenge. Therefore, the...
Prison violence presents a serious challenge to inmate and staff safety, necessitating advanced predictive analytics for effective prevention. Traditional reactive security measures have proven inadequate, highlighting the need for artificial intelligence...
The rapid proliferation of cloud infrastructures has transformed data management, enabling organizations to process, store, and analyse vast amounts of data with unprecedented efficiency. However, the reliability of data pipelines within these...
Ensuring data reliability and mitigating failures are critical challenges in large-scale cloud infrastructures, given their complexity, dynamic nature, and the increasing demand for real-time data processing. Traditional approaches often struggle with...
But even in the age of AI-driven cloud ecosystems, we continue to grapple with how to maintain data reliability when workloads are constantly shifting, data comes from many places, and the real-time expectation is often acute. Data reliability methods that...
s multi-cloud ecosystems continue to gain traction in organizations for their flexibility and scalability, ensuring data reliability across diverse cloud platforms has become a critical challenge. This research explores AI-driven strategies to enhance data...
Artificial intelligence is transforming data governance by enabling organizations to better protect data, address regulatory requirements, and manage enterprise information within unified environments. In recent years, data volumes have increased...
Self-healing systems have emerged as an important advancement in software engineering by enabling software applications to automatically detect, diagnose, and recover from failures without human intervention. These systems improve reliability, minimize...
Cloud computing has become the backbone of modern digital infrastructure, enabling businesses to leverage scalable, on-demand resources for storage, computation, and data management. However, the dynamic nature of cloud environments introduces challenges in...
he exponential adoption of distributed cloud systems has imposed heretofore unseen demands as to data dependability, redundancy, and process performance. The conventional failure detection techniques can only provide a partial solution to the dynamic nature...
This paper presents an advanced AI-powered testing framework aimed at optimizing the software quality assurance (QA) lifecycle for complex and modular software systems. Building upon the foundational work of Kothamali and Banik [1], who introduced a machine...
The explosive growth of social networks has led to the generation of vast, complex datasets, necessitating advanced methods for real-time analysis. Graph-based algorithms provide a natural framework for representing and analyzing social networks, with nodes...
In the era of rapid digital transformation, where organizations increasingly rely on data-driven strategies, the ability to manage and synchronize enterprise data in real time has become a critical strategic requirement. Traditional Master Data Management...
This paper presents a cybersecurity-aware, risk-based quality assurance (QA) methodology for healthcare software platforms. Building on the AI and IoT-enabled monitoring system introduced by Kothamali et al. [1], this study introduces a QA framework that...
Artificial intelligence is reshaping financial analysis, yet the value of advanced algorithms depends heavily on the quality of the data and governance systems surrounding them. This review examines how artificial intelligence strengthens data governance...
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