University of Sciences and Technology Houari Boumediene

Adaptive Noise-Aware Bearing Fault Diagnosis via FFT Windowing and Wavelet-Based SNR-Guided LSTM Model Selection with Real-Time FPGA Implementation

This paper presents an adaptive noise-aware bearing fault diagnosis framework that integrates Fast Fourier Transform (FFT)-windowed feature extraction, wavelet-based Signal-to-Noise Ratio (SNR) estimation, and Long Short-Term Memory (LSTM) classification to maintain high diagnost...

MODFLOW-informed machine learning for interpretable groundwater level prediction under sparse monitoring conditions in the Eastern Mitidja Aquifer, Northern Algeria

Predicting groundwater levels in semi-arid regions is challenging because groundwater systems are controlled by complex processes and monitoring data are often scarce. This study compares six machine learning models—Random Forest (RF), XGBoost, LightGBM, RBF-SVR, LSTM, and CNN—fo...

SAME: Similarity-Aware Monotonic Entropy for FWER-controlled association-rule mining without a minimum-support threshold

Every classical frequent-itemset miner asks the user for one number it cannot supply with confidence: the minimum support σ. Pick it too low and the output drowns in trivial associations;pick it too high and rare but meaningful patterns disappear; and whatever value is chosen, th...

DMPE: A Deterministic Multi-Criteria Proposer-Election State Machine for Permissioned Dis tributed Systems

Leader selection strongly affects performance and reliability in permissioned networks. Existing mechanisms rely on static schedules or protocol-specific reputation, limiting portable observation- driven selection across heterogeneous consensus engines. This paper presents DMPE,...

SAME: Similarity-Aware Monotonic Entropy for FWER-controlled association-rule mining without a minimum-support threshold

Every classical frequent-itemset miner asks the user for one number it cannot supply with confidence: the minimum support σ. Pick it too low and the output drowns in trivial associations;pick it too high and rare but meaningful patterns disappear; and whatever value is chosen, th...