Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm by Nasser, Youssef

“…Hundreds of such labeled graphs were used to train the model to predict the location of centroids. The objective is to produce a model capable of predicting centroids with greater accuracy than the traditional random initialization used in K-means. …”
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Parallel physical optimization algorithms for allocating data to multicomputer nodes by Mansour, Nashat

“…The parallel genetic algorithm (PGA) is based on a natural model of evolution. …”
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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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Physical optimization algorithms for mapping data to distributed-memory multiprocessors by Mansour, Nashat

“…PGA has excellent speed-ups by virtue of the natural evolution model on which it is based. PSA and PNN include communication schemes adapted to the properties of the mapping problem and of the algorithms themselves for reducing the communication overhead. …”
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Use of Data Mining Techniques to Detect Fraud in Procurement Sector by AL HAMMADI, SUMAYYA ABDULLA

“…The method used in this research is a classification of models and algorithms used in data mining. All techniques also will be studied; they include clustering, tracking patterns, classifications and outlier detection. …”
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Type 2 Diabetes Mellitus Automated Risk Detection Based on UAE National Health Survey Data: A Framework for the Construction and Optimization of Binary Classification Machine Learn... by Mohamed, AlShuweihi

“…The first is a comprehensive ML framework for the construction of diagnostic binary classification high accuracy models to predict T2DM in the United Arab Emirates based on STEPS style National Health Survey. …”
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Assessment of static pile design methods and non-linear analysis of pile driving by Abou-Jaoude, Grace G.

“…The pile/soil interaction system is described by a mass/spring/dashpot system where the properties of each component are derived from rigorous analytical solutions or finite element analysis. The outcome of this research is an algorithm that can be used to predict pile displacement and driving stresses. …”
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Logistics Optimization Using Hybrid Genetic Algorithm (HGA): A Solution to the Vehicle Routing Problem With Time Windows (VRPTW) by Ayesha Maroof (17984053)

A method for optimizing test bus assignment and sizing for system-on-a-chip by Harmanani, Haidar M.

“…Test access mechanism (TAM) is an important element of test access architectures for embedded cores and is responsible for on-chip test patterns transport from the source to the core under test to the sink. …”
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A stochastic iterative learning control algorithm with application to an induction motor by Saab, Samer S.

“…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. …”
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Estimating Construction Project Duration Using a Machine Learning Algorithm by Nasr, Joshua

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Metaheuristic algorithm for testing web 2.0 applications. (c2012) by Zeitunlian, Hratch Michel

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Evolutionary algorithms for state justification in sequential automatic test pattern generation by El-Maleh, Aiman H.

“…A common search operation in sequential Automatic Test Pattern Generation 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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Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms by Humna Khan (17541972)

“…Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²), were used to assess the prediction accuracy of the models. The results of the correlation matrices showed that the blueberry yield and losses (leaf loss, blower loss) had medium to strong correlations accessed based on the correlation coefficient (r) range 0.37–0.79. …”

An image processing and genetic algorithm-based approach for the detection of melanoma in patients by Tokajian, Sima

“…The second phase classifies lesions using a Genetic Algorithm. Our technique shows a significant improvement over other well-known algorithms and proves to be more stable on both training and testing data.…”
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Approximation and heuristic algorithms for computing backbones in asymmetric ad-hoc networks by Abu-Khzam, Faisal N.

“…We consider the problem of dominating set-based virtual backbone used for routing in asymmetric wireless ad-hoc networks. …”
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Correlation Clustering via s-Club Cluster Edge Deletion by Makarem, Norma

“…In certain situations, the requirement for clusters to be cliques was deemed excessively stringent, leading to the proposal of alternative relaxed clique models for dense subgraphs, such as s-club. In this work, we implement three approaches to tackle the 2-club clustering via edge deletion: a heuristic approach based on the influence of the edge to resolve maximum conflicts, a parameterized algorithm in which by deleting a maximum of k edges, the graph can be transformed into a 2-club cluster based on a branching algorithm, and the approach in Integer Linear Programming to find the optimized solution in an integer formulation. …”
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I Will Survive: An Event-driven Conformance Checking Approach Over Process Streams by Raun, Kristo

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Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model by Ramesh Sundar (19326046)

“…In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. …”

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

“…It concentrates on deriving a variable-metric family of minimum curvature algorithms for unconstrained optimization. The derivation is based on considering a rational model, with a certain tuning parameter, where the aim is to develop a general framework that encompasses all possible two-step minimum curvature algorithms generated by appropriate parameter choices. …”
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