Simultaneous stabilisation of power systems using geneticalgorithms by Abdel-Magid, Y.L.

“…The problem of selecting the parameters of a power system stabiliser which simultaneously stabilises this set of plants is converted to a simple optimisation problem which is solved by a genetic algorithm and an eigenvalue-based objective function. …”
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Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization by De Rochechouart, Maxence

“…The work demonstrates how machine learning techniques can capture resource allocation policy and help avoid the complexity of having to re-calculate cost function at every time step, especially when we have many radars and many cameras.…”
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Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information by M. Ghoniem, Rania

“…For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. …”
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Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers by Ali Sharida (17947847)

“…The OSF detection and localization algorithm is implemented based on the dynamic response difference between the physical system and its DT. …”

Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network by Monzure-Khoda Kazi (17191207)

“…<p dir="ltr">This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study to figure out the complicated relationship between the crashworthiness parameters of the hexagonal composite ring specimens under lateral compressive, energy absorption, and failure modes. …”

Fixed set search applied to the multi-objective minimum weighted vertex cover problem by Raka Jovanovic (17947838)

“…One important characteristic of the proposed GRASP is that it avoids the use of weighted sums of objective functions in the local search and the greedy algorithm. …”

Intelligent route to design efficient CO₂ reduction electrocatalysts using ANFIS optimized by GA and PSO by Majedeh Gheytanzadeh (17541927)

“…Considering the high cost and time-consuming experimental investigations, computational methods, particularly machine learning algorithms, can be the appropriate approach for efficiently screening the metal alloys as the electrocatalysts. …”

Software defect prediction. (c2019) by Moussa, Rebecca

“…One that focuses on predicting defect in software modules using a hybrid heuristic - a combination of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). We compare our approach to 9 well known machine learning techniques and results show the advantages of our model over the other techniques. …”
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Exploring the Dynamic Interplay of Deleterious Variants on the RAF1–RAP1A Binding in Cancer: Conformational Analysis, Binding Free Energy, and Essential Dynamics by Abbas Khan (5141000)

“…Survival analysis results revealed a strong association between <i>RAF1</i> and <i>RAP1A</i> expression levels and diminished survival rates in cancer patients across different cancer types. Integrated machine learning algorithms showed that among the 134 mutations reported for these 2 proteins, only 13 and 35 were classified as deleterious mutations in <i>RAF1</i> and <i>RAP1P</i>, respectively. …”

Wiener-Hammerstein Model Identification-Recursive lgorithms by Emara-Shabaik, Husam

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FoGMatch by Arisdakessian, Sarhad

“…Our solution consists of (1) two optimization problems, one for the IoT devices and one for the fog nodes, (2) preference functions for both the IoT and fog layers to help them rank each other on the basis of several criteria such latency and resource utilization, and (3) centralized and distributed intelligent scheduling algorithms that consider the preferences of both the fog and IoT layers to improve the performance of the overall IoT ecosystem. …”
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