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Egyptian Informatics Journal
Scopus
ISSN 1110-8665 · Elsevier · 838 artikel di indeks
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Artikel Jurnal
LM-CAGF: A prompt-enhanced Cross-Attention and Gated Fusion framework for multimodal fake news detection
Background and Aim:: The rapid proliferation of misinformation, particularly fake news on social media, poses a critical challenge to information credibility and public trust. However, existing fake news detection methods face two major limitations: they often rely on lightweight or task-specific…
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Artikel Jurnal
A multi-norm constraints sparse recovery STAP with coprime sampling structure under array sensor failures
The coprime sampling structures offer significant advantages in achieving large apertures and high degree of freedom (DOF), but sensor failures can severely impact their space time adaptive processing (STAP) performance. To address this issue, a data completion STAP method based on a multi-norm…
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Artikel Jurnal
Brain connectivity analysis for measuring the physical lifting capacity of labors
Assessing the physical capabilities of workers in their work environments is crucial for reducing the risk of musculoskeletal disorders. Physical activity influences brain function and EEG patterns, enabling quantitative assessment of an individual’s physical performance through EEG signal…
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Artikel Jurnal
Ai-infused business model innovation for competitive advantage in the Era of big data and digital transformation
Background: The use of artificial intelligence (AI) in business operations is increasing rapidly. However, existing research provides limited systematic insight into how AI integration reshapes business models. This is especially unclear when big data capabilities within digital transformation…
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Calibrated stacked learning for source-aware screening of ABL1/BCR–ABL1 mutation–TKI resistance
Predicting ABL1/BCR–ABL1 mutation resistance to tyrosine kinase inhibitors (TKIs) remains challenging because available mutation–drug evidence is limited, heterogeneous, and distributed across public bioactivity resources. This study presents an optimized calibrated stacked-learning framework for…
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Artikel Jurnal
Industrial gas turbine emissions prediction using data-driven hybrid stacked ensemble machine under dynamic operating conditions
Industrial gas turbines operate under dynamic conditions in which startup, steady-state, and transient operation exhibit distinct combustion characteristics. Conventional machine learning-based Predictive Emissions Monitoring Systems (PEMS) generally employ a single model trained using aggregated…
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Artikel Jurnal
A domain-informed ensemble learning framework with explainable visual analytics for obesity risk assessment
Obesity is a multifactorial chronic disease driven by complex interactions among genetic predisposition, metabolic processes, and lifestyle behaviors, posing significant challenges for accurate risk prediction and clinical decision-making. In this study, we propose a Domain-Aware Ensemble Learning…
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Feature-engineered ensemble learning methods for insulator fault detection based on ultrasound signals
This paper presents a feature-engineered ensemble method for fault detection in medium-voltage insulator ultrasound signals. We develop a feature extraction framework that captures time-domain, frequency-domain, wavelet-domain, and envelope characteristics of ultrasonic emissions from porcelain…
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Artikel Jurnal
Optimization method for immersive interactive drama plot generation based on virtual reality
This study proposes an optimized method for immersive and interactive dramatic narratives, grounded in integrated design principles that center on multimodal perception, dynamic decision-making, and collaborative generation. The proposed approach establishes an adaptive decision-making mechanism…
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Artikel Jurnal
A Distributed Extreme Learning Machine based approach for self-failure diagnosis in Underwater Wireless Sensor Networks
Underwater Wireless Sensor Networks (UWSNs) are vital for applications including environmental observation, disaster prevention, and deep-sea exploration, yet intermittent node defects severely degrade data fidelity and network reliability. This paper introduces a Distributed Extreme Learning…
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TIR-VD: Binary vulnerability detection based on texture and input-related embeddings
The rapid expansion of IoT systems fuels the reuse of vulnerable code in firmware binaries, exposing IoT devices to network attacks. Current dynamic analysis, which relies on emulation and fuzzing, suffers from high latency, hindering large-scale vulnerability detection that is especially critical…
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Artikel Jurnal
GUIDE: Generative User Intent Dual-Encoder for tourism recommendation
Tourism and point-of-interest (POI) recommendation is inherently intent-dependent and context-sensitive, yet many existing methods primarily rely on user–item interactions or short-term behavioral sequences, making it difficult to capture the rich semantics of natural-language travel demands. To…
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Blockchain-enabled hybrid CNN–Transformer framework with Integrated Gradients for Parkinson’s disease detection
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor impairments such as tremor, rigidity, bradykinesia, and gait instability. Accurate and early detection remains challenging due to the complex temporal nature of wearable sensor signals, limited…
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Artikel Jurnal
Multi-label lung disorder diagnosis via hybrid handcrafted and Swin transformer-based global–local feature fusion
Background: Cancer is a major health issue caused by various chemical imbalances and genetic problems, ranking as the second leading cause of death worldwide. Approximately one in six individuals succumb to this disease. Lung cancer is among the most prevalent and lethal types, significantly…
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Artikel Jurnal
Calibration-free grid-based pedestrian tracking with shortcut edge for video surveillance
Multi-object tracking (MOT) has drawn considerable interest owing to its many applications, such as analyzing pedestrian and obstacle behavior for autonomous driving perception. Conventional grid-based tracking methods are typically vulnerable to detection failures, since connections between…
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Artikel Jurnal
Risk-aware cyberattack detection in healthcare networks using GraphSAGE embeddings and conformal Gaussian mixture modeling
Modern healthcare networks increasingly rely on interconnected medical devices, electronic health records, and real-time monitoring systems, making them highly vulnerable to sophisticated cyberattacks that can directly compromise patient safety and clinical operations. Ensuring secure and reliable…
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Artikel Jurnal
Automated security-centric refactoring of PowerShell commands using large language models: A Python-based framework for cross-language automation
The integration of Large Language Models (LLMs) into software engineering workflows has demonstrated significant potential for automating code refactoring across programming languages. However, cross-language refactoring—particularly between high-level orchestration languages (Python) and shell…
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Artikel Jurnal
JSATRA: topology based logic mining using discrete hopfield neural network
Obtaining optimal logical rules and maintaining the explainability of the logical rules are two crucial issues in developing a logic mining model. To address these challenges, a novel symbolic logical rule namely J-type Random 2,3 Satisfiability was proposed to represent the attribute in the…
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Artikel Jurnal
A hybrid RoBERTa–BiLSTM framework for aspect-based sentiment analysis of Monkeypox tweets
The Monkeypox and COVID-19 outbreaks have brought into focus the significance of social media as a real-time source of public health information, where people share their opinions and concerns regarding different health issues. However, the task of reliable aspect-level sentiment extraction is…
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Artikel Jurnal
Model-based and data-driven control of heterogeneous multi-agent systems under deception attacks
This paper investigates the consensus control of heterogeneous multi-agent systems suffering from unknown dynamics and deception attacks. To circumvent the reliance on precise parametric models, a data-driven distributed control framework is developed using offline open-loop experimental data. A…