Economic load dispatch using memetic sine cosine algorithm حسب Abu Zitar, Raed

"…In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. …"
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High-Accurate Parameter Identification of PEMFC Using Advanced Multi-Trial Vector-Based Sine Cosine Meta-Heuristic Algorithm حسب Badreddine Kanouni (23073244)

"…<p dir="ltr">Development and modeling of proton exchange membrane fuel cells (PEMFCs) need accurate identification of unknown factors affecting mathematical models. The trigonometric function-based sine cosine algorithm (SCA) may solve such problems, but it traps in local optima, making it inappropriate for larger optimization tasks. …"

Logarithmic spiral search based arithmetic optimization algorithm with selective mechanism and its application to functional electrical stimulation system control حسب Abu Zitar, Raed

"…The proposed algorithm (Ls-AOA) was tested against unimodal and multimodal benchmark functions and demonstrated better capability comparatively using other efficient metaheuristic algorithms reported in the literature. …"

Evolution Of Activation Functions for Neural Architecture Search حسب Nader, Andrew

"…However, to the best of our knowledge, the design of new activation functions has mostly been done by hand. In this work, we propose the use of a self-adaptive evolutionary algorithm that searches for new activation functions using a genetic programming approach, and we compare the performance of the obtained activation functions to ReLU. …"
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Modeling of magnetization curves for computer-aided design حسب Abuelma’atti, M.T.

"…A simple algorithm is presented for obtaining the coefficients of the sine-series function for modelling the magnetization curves of magnetic materials. …"
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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. …"