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This thesis, entitled €High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study€ investigates the computational issues related to the resolution of linear systems o...

Abstract Multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems assisted by reconfigurable intelligent surfaces (RIS) have become a viable way to improve next-generation wireless networks. Howe...

The article explores the strategic role of accounting and financial support within modern enterprise decision-making systems. It examines how accounting has evolved from a transactional function into a core analytical an...

The shift toward sustainable and intelligent buildings has accelerated the demand for advanced maintenance strategies that reduce energy waste, downtime, and improve reliability. Traditional maintenance approaches (react...

Abstract The rapid adoption of AI is transforming industrial wastewater treatment by enabling real-time monitoring, predictive control, and operational optimization. Publications on the subject accelerated sharply betwee...

Abstract Cloud computing is quite popular because of its scalability and flexibility merits. However, faces significant security risks from malware, which exploits infrastructure weaknesses to cause unauthorized access, ...

Abstract Hot data identification plays a critical role in a wide range of computing systems, including memory hierarchy management, database optimization, and large-scale storage infrastructures. Its importance has furth...

ABSTRACT Influence maximisation (IM) aims to select a small number of nodes that are able to maximise their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective s...

This study employs a design-based research (DBR) framework to examine the impact of a comprehensive curriculum redesign in an introductory Foundations of Security module for undergraduate students in computing and cybers...

Abstract The growing importance of machine learning in geological data computing, as well as in the broader geoscience and energy sectors, makes it essential to evaluate the impact of data augmentation on model performan...

This paper presents an innovative parallel computational methodology employing adaptive fractional differential operators to address Monkeypox epidemiological modeling challenges. The complex dynamics of such models ofte...

Abstract The rising complexity of urban areas, along with growing public safety needs, has made intelligent video surveillance systems more essential. The current surveillance systems that rely on human observation and p...

Abstract Financial institutions continue to face a significant challenge with money laundering and need intelligent, scalable ways to detect illegal financial activity. This paper introduces a machine learning-driven pla...

Abstract Single-image dehazing is essential for safety-critical vision systems, yet existing CNN-based methods are often too heavy for edge devices or fail to adapt to spatially varying haze density. This paper introduce...

Abstract The rapid adoption of encrypted communication protocols has raised privacy levels but has also dealt a significant blow to traditional intrusion detection systems that rely on payload inspection for anomaly dete...