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Leakage-Resistant and Uncertainty-Aware Machine Learning for Carrier-Grade Router Power Prediction Using Real Traffic Telemetry

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Machine Learning in Mineral Exploration:A Review of Methods, Applications, and Open Challenges

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Machine learning-based solar radiation prediction across heterogeneous climatic zones using NASA POWER data: a case study in Nigeria

Accurate prediction of solar radiation is essential for renewable energy planning, climate analysis, and agricultural productivity. Solar radiation data are …

Article scientifique

Routing Layer Attacks in Low-Power IoT Networks: A Topology- and Mobility-Aware Dataset Generation and Machine Learning Benchmarking Study

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Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets

Classification is a core machine learning task, and ensemble voting methods are widely used to improve predictive accuracy in domains …

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Using machine learning to enhance pandemic modeling: case study of COVID-19 dynamics in Ghana

Abstract In this study, five distinct COVID-19 models developed in different countries, each designed to reflect the prevailing epidemiological condition …

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Indexing meets machine learning: a systematic literature review of learned indexes

Abstract Learned index structures use machine learning models, rather than traditional algorithmic data structures, to optimize database indexing performance. This …

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Improving Credit Card Fraud Detection Using Ensemble Machine Learning Techniques

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DESIGN AND IMPLEMENTATION OF A MACHINE LEARNING MODEL FOR NETWORK INTRUSION DETECTION

Abstract Network intrusion detection has become difficult because enterprise traffic is high-volume, heterogeneous and continuously altered by encryption, cloud adoption, …

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Forecasting food price inflation in Nigeria and identifying its drivers using machine learning models

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Leveraging generative models to assist Monte Carlo sampling

Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics …

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A Hybrid Machine Learning Framework for Robust Detection of Malicious DNS-over-HTTPS Tunneling Traffic

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Machine learning-based prediction of SARS-CoV-2 bioactivity: integrating IC50 regression and activity classification using multi-task neural networks

Abstract Accurate prediction of compound bioactivity is essential for accelerating antiviral drug discovery and reducing experimental costs. Machine learning (ML) …

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Machine Learning-Based Forecasting of Headline Inflation in Ethiopia: an Empirical Comparative Study

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Human capital in a fragile state: an econometric and machine learning analysis of development aid, humanitarian assistance, conflict, and economic conditions in Somalia

Introduction Human capital is central to economic development, particularly in fragile and conflict-affected states where instability, aid dependence, and weak …

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Explainable machine learning for early classification of middle school student performance

Identifying students at risk of academic underperformance early is a longstanding difficulty for school systems, and the difficulty is sharpest …

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A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30

This study concentrates on predicting stock prices in the Egyptian market, focusing on the EGX30, an influential financial hub in …

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A robust binary secretary bird optimization method for high-dimensional data classification

Feature selection is a key step in machine learning-based decision systems, especially in medical and biomedical applications, where datasets often …

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Reversed degree-based graph invariants in machine learning-driven QSPR analysis of antiviral drugs

The prediction of physicochemical properties of antiviral drugs is an important challenge in pharmaceutical research due to the high cost …

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Hybrid multi-disease SEIR and transformer models for infectious disease forecasting in Zambia

Abstract Background Traditional infectious disease models like SIR and SEIR have long served epidemiologists in understanding outbreak dynamics. However, these …

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