Estimating Bridge Deterioration Age Using Artificial Neural Networks حسب AL HUSSEIN, ASEEL

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Online Control and Optimization of Directional Drilling حسب unknown

"…Secondly, the autonomous quad-rotor DSS which has 4 downhole motors, is considered. In this work, a novel feedback linearization controller to cancel the nonlinear dynamics of a DSS is proposed. …"
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Blood Glucose Regulation Modelling and Intelligent Control حسب Nasar, Nabeel

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Software defect prediction. (c2019) حسب Moussa, Rebecca

"…This is normally measured at the level of a module. A module is a class in the object-oriented design or a function in the procedural design. …"
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Particle swarm optimization approach for protein structure prediction in the 3D HP model حسب Mansour, Nashat

"…In this paper, we present a particle swarm optimization (PSO) based algorithm for predicting protein structures in the 3D hydrophobic polar model. …"
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Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia حسب Hanan Ehtewish (17149825)

"…We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). …"

Reinforcement Learning-Based School Energy Management System حسب Yassine Chemingui (18891757)

"…In recent years, the Deep Reinforcement Learning algorithm, applying neural networks for function approximation, shows promising results in handling such complex problems. …"

Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers حسب Ali Sharida (17947847)

"…First, the OSF is detected and localized based on the grid current dynamics, where each switch fault generates a specific pattern in the current dynamics. OSF is tolerated by changing the switching function based on the location of the fault. …"

Enhanced Microgrid Reliability Through Optimal Battery Energy Storage System Type and Sizing حسب Mohammadreza Gholami (17032317)

"…To determine the optimized size, a firefly optimization algorithm is used as an efficient meta-heuristic approach. …"

Oversampling techniques for imbalanced data in regression حسب Samir Brahim Belhaouari (9427347)

"…The outcomes revealed a positive impact of data augmentation, with a success rate of 83.75% for Light Gradient Boosting Method (LightGBM) and 71.57% for the 18 other regressors employed in the study. …"

Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network حسب Fares Almomani (12585685)

"…The developed model can be used to forecast the CMP as a function of operating temperature, the substrate composition, and chemical dose, and can be used for scaling-up and cost analysis purposes.…"

Improving the Resilience of Smart Distribution Networks against Cyber Attacks حسب ElYamani, Mohamed ElHusseiny Mohamed

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Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment حسب Farhat Mahmood (15468854)

"…A robust model predictive control strategy, based on the minimax objective function and particle swarm optimisation algorithm, is developed to handle the uncertainties in the system. …"

Label dependency modeling in Multi-Label Naïve Bayes through input space expansion حسب PKA Chitra (21749216)

"…The performance of our approach is gauged against the traditional multi-label Naïve Bayes (MLNB) algorithm and is quantified through a suite of evaluation metrics. …"

Defense against adversarial attacks: robust and efficient compressed optimized neural networks حسب Insaf Kraidia (19198012)

"…A cumulative updating loss function was employed for overall optimization, demonstrating remarkable superiority over traditional optimization techniques. …"

Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System حسب Alaa Altawallbeh (22565837)

"…Through systematic eigenvalue analysis and parameter sensitivity studies, complemented by time-domain verification in MATLAB/SIMULINK, we demonstrate the decisive influence of VSG control parameters on low-frequency oscillation (LFO) damping characteristics, transient frequency stability metrics, including the rate of change of frequency (ROCOF), maximum frequency deviation (<i>fnadir</i>), overshoot, and settling time. We further propose a hybrid Particle Swarm Optimization (PSO) algorithm with a multi-objective cost function to optimize VSG controller gains. …"

Design and performance analysis of hybrid MPPT controllers for fuel cell fed DC-DC converter systems حسب Shaik Rafikiran (15838929)

"…The studied MPPT controllers are Adaptive Adjustable Step-based Perturb and Observe (AASP&O) controllers, Variable Step Value-Radial Basis Function Controller (VSV-RBFC), Adaptive Step Hill Climb (ASHC) based fuzzy technique, Variable P&O with Particle Swarm Optimization (VP&O-PSO), and Variable Step Grey Wolf Algorithm (VSGWA) based fuzzy logic controller. …"

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces حسب Uzair Sajjad (19646296)

"…In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. …"

Design and performance analysis of hybrid MPPT controllers for fuel cell fed DC-DC converter systems حسب Shaik, Rafikiran

"…The studied MPPT controllers are Adaptive Adjustable Step-based Perturb and Observe (AAS-P&O) controllers, Variable Step Value-Radial Basis Function Controller (VSV-RBFC), Adaptive Step Hill Climb (ASHC) based fuzzy technique, Variable P&O with Particle Swarm Optimization (VP&O-PSO), and Variable Step Grey Wolf Algorithm (VSGWA) based fuzzy logic controller. …"
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R-CONV++: uncovering privacy vulnerabilities through analytical gradient inversion attacks حسب Tamer Ahmed Eltaras (22565414)

"…The first algorithm presents a novel data leakage method that efficiently exploits convolutional layer gradients, demonstrating that even with non-fully invertible activation functions, such as ReLU, training samples can be analytically reconstructed directly from gradients without the need to reconstruct intermediate layer outputs. …"