Detecting COVID-19 From Lung Computed Tomography Images: A Swarm Optimized Artificial Neural Network Approach by S. Punitha (17945654)

A new family of multi-step quasi-Newton algorithms for unconstrained optimization by Obeid, Samir

“…One member of the family is tested against earlier developed algorithms of the multi-step type. Performance improvement is evident in our presented results, thus verifying the importance of the minimum curvature framework…”
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Multi-Objective Co-optimization of Power and Gas under Uncertainties with P2H embedded by Abdallah, Rawan Yousef Ali

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Developing a Cooperative Behavior for Multi Agents System Application to Robot Soccer by Idlan, Ayman Hashim

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Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

Subjects:

A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015) by Wehbe, Gioia Wahib

“…The challenge now is to develop fast and accurate computational methods to analyze this huge amount of data. …”
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Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”

Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain by Al Sadawi, Alia

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Meta-Heuristic Procedures for the Multi-Resource Leveling Problem with Activity Splitting by AlSayegh, Hadeel Yacoub

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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing by Al Sadawi, Alia

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Process Targeting Of Multi-Characteristic Product Using Fuzzy Logic And Genetic Algorithm With An Interval Based Taguchi Cost Function by Mujahid, S.N.

“…In this paper, a fuzzy based process targeting model is developed for a product with multi-characteristic. …”
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Process Targeting Of Multi-Characteristic Product Using Fuzzy Logic And Genetic Algorithm With An Interval Based Taguchi Cost Function by Duffuaa, S. O.

“…In this paper, a fuzzy based process targeting model is developed for a product with multi-characteristic. …”
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Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network by Mohammad Reza Chalak Qazani (13893261)

“…Its purpose was to estimate shear and residual stress levels. Additionally, the multi-objective genetic algorithm (MOGA) was utilised to extract the most optimal parameters for the injection moulding process, aiming to minimise shear and residual stress and thereby increase the resistance of the final product. …”

High-Accurate Parameter Identification of PEMFC Using Advanced Multi-Trial Vector-Based Sine Cosine Meta-Heuristic Algorithm by Badreddine Kanouni (23073244)

“…This paper introduces a novel multi-trial vector-based sine cosine algorithm (MTV-SCA) for the identification of seven unknown parameters of PEMFCs. …”

Consensus-Based Distributed Formation Control of Multi-Quadcopter Systems: Barrier Lyapunov Function Approach by Nargess Sadeghzadeh-Nokhodberiz (16904952)

Development of an Optimization Scheme for A Fixed-Wing UAV Long Endurance with PEMFC and Battery by Al Rayes, Zaid

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Experimental Investigation and Comparative Evaluation of Standard Level Shifted Multi-Carrier Modulation Schemes With a Constraint GA Based SHE Techniques for a Seven-Level PUC Inv... by Atif Iqbal (5504636)

Radar Pulse Interleaving For Multi-Target Tracking by Elshafei, M.

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Modelling And Analysis Of A Multi-Stage Order Quantity Model by Andijani, A. A.

“…An algorithm to determine the relationship between the lot sizes of the MSOQ model is also developed. …”
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A multi-pretraining U-Net architecture for semantic segmentation by Cagla Copurkaya (22502042)

“…Nucleus segmentation is one of the most challenging tasks because of the many hurdles involved such as masking operations, inaccurate and erroneous annotations, unclear boundaries, poor colours, and overlapping cells. New developments in the deep learning field contributed to the development of new application domains and this has made segmenting nuclei possible. …”