Performance of traditional ML algorithms for optimal offloading scheme. by Shehr Bano (20421706)

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Performance of different ensemble algorithms for optimal partitioning policy. by Shehr Bano (20421706)

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Performance of traditional ML algorithms for optimal partitioning scheme. by Shehr Bano (20421706)

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Performance of different ensemble algorithms for optimal offloading policy. by Shehr Bano (20421706)

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The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist by Xiaoqi Wang (164823)

Measures of predictive accuracy in four machine learning algorithms. by Ryan P. Strum (12411239)

Optimal ovarian cancer BNs learned by CausNet and CausNet-partial. by Nand Sharma (21519845)

Algorithm of the PbGA search for the optimal PbF. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Optimized network structure. by Menaa Nawaz (14304920)

Multiobjective Tuning and Performance Assessment of PID Using Teaching–Learning-Based Optimization by Wei Zhang (405)

“…The CPA-related non-convex problem and tuning-related multiobjective problem are solved by teaching–learning-based optimization, which guarantees a tighter lower bound for MOV due to the excellent capability of local optima avoidance and has higher computational efficiency due to the low complexity. …”

Hyperparameter search space used in the optimization of the Basic DeepInsight-CNN. by Leonides Medeiros Neto (10429179)

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SVR and MLPR algorithm flow optimized by GridSearchCV. by Jintao Li (448681)

Flow chart of particle swarm algorithm. by Nour Eldeen Mahmoud Khalifa (19259450)

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Table_1_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX by Maruf A. Tamal (18947776)

“…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …”

Table_2_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX by Maruf A. Tamal (18947776)

“…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …”

Overview of mobile-edge-cloud server-based system. by Shehr Bano (20421706)

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Optimal control for quadrotors UAV based on deep neural network approximations of stable manifold of HJB equation by Yuhuan Yue (824383)

The flow of the SP-DRL algorithm. by Jie Fang (306330)

“…Inspired by deep learning and reinforcement learning, combined with the characteristics of rectangular piece packing, a novel algorithm based on deep reinforcement learning is proposed in this work to solve the rectangular strip packing problem. …”

Mean relative importance for the top 10 patient characteristic predictors of hospital admission across all machine learning algorithms. by Ryan P. Strum (12411239)

A comparison among different optimizations and classifiers. by Nour Eldeen Mahmoud Khalifa (19259450)

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