Multiclass feature selection with metaheuristic optimization algorithms: a review by Abu Zitar, Raed

“…Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. …”
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A Simulated Annealing Algorithm For Fuzzy Unit Commitment Problem by Mantawy, A. H.

“…This paper presents a new algorithm based on integrating simulated annealing and fuzzy logic methods to solve the unit commitment problem. …”
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A simulated annealing algorithm for fuzzy unit commitment problem by Mantawy, A.H.

“…This paper presents a new algorithm based on integrating simulated annealing and fuzzy logic methods to solve the unit commitment problem. …”
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Economic load dispatch using memetic sine cosine algorithm by Abu Zitar, Raed

“…SCA is a recent population based optimizer turned towards the optimal solution using a mathematical-based model based on sine and cosine trigonometric functions. …”
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A Survey of cuckoo search algorithm: optimizer and new applications by Abualigah, Laith

“…Cuckoo search (CS) is an efficient swarm intelligence-based algorithm that has come a long way since its start in 2009. …”
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Parameter Estimation Of Wiener-Hammerstein Models Via Genetic Algorithms by Emara-Shabaik, Husam

“…Numerical simulations are presented to illustrate the effectiveness of the proposed algorithm based on different input signals, and different noise-to-signal ratios of the output. …”
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An efficient method for the open-shop scheduling problem using simulated annealing by Harmanani, Haidar M.

“…The method is based on a simulated annealing algorithm that efficiently explores the solution space. …”
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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm by Nasser, Youssef

“…This work aims to enhance the performance of the K-means algorithm by introducing a novel method for selecting the initial centroids, thereby minimizing randomness and reducing the number of iterations needed to reach optimal results. …”
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A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems by Devinder Kaur (264278)

A new minimum curvator multi-step method for unconstrained optimization by Obeid, Samir

“…Our derivation of the new algorithm is based on determining some value of the parameter that minimizes the curvature in some chosen metric. …”
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An incremental approach for test scheduling and synthesis using genetic algorithms by Harmanani, H.

“…The method is based on a genetic algorithm that efficiently explores the testable design space. …”
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Concurrent BIST Synthesis and Test Scheduling Using Genetic Algorithms by Harmanani, H. M.

“…The method is based on a genetic algorithm that efficiently explores the testable design space and finds a sub-optimal test registers assignment for each k-test session. …”
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Power Control Algorithms for Media Transmission in Remote Healthcare Systems by Ali Hassan Sodhro (18629392)

“…Thus, this paper first proposes a transmission power control (TPC)-based energy-efficient algorithm (EEA) for when a subject is in different postures, i.e., standing, walking, and running, in wireless body sensor networks. …”

Methods for system-on-chip test design, scheduling and optimization. (c2006) by Farah, Rana

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Dynamic Layout Planning Using a Hybrid Incremental Solution Method by Zouein, Pierrette

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Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage by Yousra Regaya (17788964)

“…A robust brain aneurysm segmentation has the potential to prevent the blood leakage, also known as sentinel hemorrhage. Here, we present a method combining a multiresolution and a statistical approach in two dimensional domain to segment cerebral aneurysm in which the Contourlet transform (CT) extracts the image features, while the Hidden Markov Random Field with Expectation Maximization (HMRF-EM) segments the image, based on the spatial contextual constraints. …”

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects by Majdi Mansouri (16869885)

“…Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. …”

Combinatorial method for bandwidth selection in wind speed kernel density estimation by El Dakkak, Omar

“…In this study, a non-parametric combinatorial method is implemented for obtaining an accurate non-parametric kernel density estimation (KDE)-based statistical model of wind speed, in which the selection of the bandwidth parameter is optimised concerning mean integrated absolute error (L 1 error ) between the true and hypothesised densities. …”
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Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems by Ahmad K. Sleiti (14778229)

“…Furthermore, this paper demonstrates the advantages of the developed ADL algorithm approach and DSM prediction of the DT using vector autoregressive model for anomaly detection in utility gas turbines with data from an operational power plant.…”

Cyberbullying Detection Model for Arabic Text Using Deep Learning by Albayari, Reem

“…Data-driven approaches, such as machine learning (ML), par ticularly deep learning (DL), have shown promising results. …”
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