Identification of errors-in-variables model with observation outliers based on Minimum-Covariance-Determinant by ALMutawa, J.

“…In order to solve the MCD problem for the EIV model we propose a random search algorithm. …”
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Joint Planning of Smart EV Charging Stations and DGs in Eco-Friendly Remote Hybrid Microgrids by Shaaban, Mostafa

“…An outer sub-problem determines the locations and sizes of the DG units and charging stations using a non-dominated sorting Genetic algorithm (NSGA-II). …”
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Communications in electronic textile systems by Nakad, Z.

“…Abstract- Electronic textiles (e-textiles) are emerging as a novel method for constructing electronic systems in wearable and large area applications. …”
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High-order parametrization of the hypergeometric-Meijer approximants by Abouzeid M. Shalaby (16329062)

“…To solve this problem, we formulate an equivalent (order by order) linear set of equations which is easy to solve in an appropriate time using normal PCs. …”

Logic-based Benders decomposition combined with column generation for mobile 3D printer scheduling problem by Tao Li (86810)

“…After analyzing the characteristics and structure of the model, a logic-based Benders decomposition algorithm framework is designed for solving this problem. …”

Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas by Marwan Dhuheir (19170898)

“…We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. …”

Phased Array Technique for Brain Source Localization by Zarghami, Tahereh

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An Evolutionary Meta-Heuristic for State Justification in Sequential Automatic Test Pattern Generation by El-Maleh, Aiman H.

“…A common search optimization in sequential ATPG is to justify a desired state assignment on the sequential elements. State justification using deterministic algorithms is a difficult problem and is prone to many backtracks, which can lead to high execution times. …”
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An evolutionary meta-heuristic for state justification insequential automatic test pattern generation by El-Maleh, A.H.

“…A common search operation in sequential ATPG is to justify a desired state assignment on the sequential elements. State justification using deterministic algorithms is a difficult problem and is prone to many backtracks, which can lead to high execution times. …”
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Practical Multiple Node Failure Recovery in Distributed Storage Systems by Itani, M.

“…We allocate newcomers to nodes with minimal computations and without changing the original optimized plan. The problem is solved using genetic algorithms that search within the feasible solution space. …”
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Dynamic multiple node failure recovery in distributed storage systems by Itani, May

“…The contribution of this paper is four-fold: i. we generate an optimized block distribution scheme that minimizes the total system repair cost of all dependent and independent multiple node failure scenarios; ii. we address the practical scenario of having newly arriving blocks and allocate those blocks to existing nodes without any modification to the original on-node block distribution; iii. we consider new-comer nodes and generate an updated optimized block distribution; iv. we consider optimized storage and recovery of blocks with varying priority using variable fractional repetition codes. The four problems are modeled using incidence matrices and solved heuristically. …”
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On Indefinite Quadratic Optimization over the Intersection of Balls and Linear Constraints by Temadher A. Almaadeed (14151288)

“…To solve e-TRS, we use the alternating direction method of multipliers approach and a branch and bound algorithm. …”

Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management by Alhusin, Mohammed Omer Alamin

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Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation by Saqr Khalil Saeed Thabet (19517560)

“…In the power-distortion problem, to obtain a target distortion, the algorithm exhaustively solves the closed form of the power resource under a predefined upper-bound bandwidth. …”

An Improved Genghis Khan Optimizer based on Enhanced Solution Quality Strategy for Global Optimization and Feature Selection Problems by Abdel-Salam, Mahmoud

“…It utilizes Quasi-opposite-based learning (QOBL) to enhance the best solution obtained and, consequently, the entire population. The algorithm presented aims to solve the FS problem and has been assessed using benchmark optimization problems from the CEC’2017 and CEC’2022. …”
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Final exams scheduling for univeristies. (c2001) by Timany, Mazen Nuhad

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Tabu Search For A Class Of Single-Machine Scheduling Problems by Al-Turki, Umar

“…Problems from the literature are used to test the performance of the algorithm. This algorithm can be used for solving other problems such as minimizing completion time deviation from a common due date.…”
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Transformations for Variants of the Travelling Salesman Problem and Applications by Assaf, Mustafa Jamil

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Toward automatic motivator selection for autism behavior intervention therapy by Siyam, Nur

“…The states, actions and rewards design consider the factors that impact the efectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. …”
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Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose by Atiq Ur Rehman (8843024)

“…In this paper, these two problems of `sensors long term drift' and `delayed response' are solved simultaneously to propose a robust and fast electronic nose system, with following merits: (i) only initial transient state features are used in the proposed system without waiting for the sensors to reach a steady state, (ii) a modified boxplot approach is used to handle noisy/drifted data points as a preprocessing step before the classification setup, (iii) a heuristic tree classification approach with optimized transient features is proposed, (iv) the proposed approach only relies on adapted ML methods contrary to the traditional approaches like system recalibration or sensors replacement for handling sensors drift, and (v) the proposed ML model does not require any target domain data and uses only the source domain data for learning the classifier, opposed to the other ML solutions available in the existing literature. …”