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

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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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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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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. …”

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

“…We present experimental results that demonstrate the effectiveness of our method while outperforming reported techniques.…”
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On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach by Md. Sihab Uddin (17542488)

“…The design is carried out based on the selection of the optimal model parameters using a search optimization algorithm called GridSearchCV. …”

A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna by Nupur Chhaule (21224987)

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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Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle by Wadi, Ali H M

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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 Hamiltonian Semi-Analytical Approach to Vibration Analysis of Piezoelectric Multi-Layered Plates by Andrianarison, O.

“…The whole piezoelectric multilayered plate’s dynamic stiffness is then built, from which its circular frequencies are computed with the help of the Wittrick-Williams algorithm. A detailed discussion is provided on the implementation aspects, followed by some numerical examples to assess the robustness, accuracy and effectiveness of the proposed method. …”
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Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection by Alok Singh Chauhan (8264985)

A Framework for Predictive Modeling in Sustainable Projects by Labban, Tarek

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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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XBeGene: Scalable XML Documents Generator by Example Based on Real Data by Harazaki, Manami

“…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects by Majdi Mansouri (16869885)

Optimizing Energy Consumption In Cloud Datacenters by Khan, Mueez Ahmad

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A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems by Majdi Mansouri (16869885)

“…In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. …”