Learning continuous functions using decision tree learning algorithms by Ibrahim, Ahmed Esmat Mahmoud

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Ensemble Deep Random Vector Functional Link Neural Network for Regression by Minghui Hu (2457952)

“…<p dir="ltr">Inspired by the ensemble strategy of machine learning, deep random vector functional link (dRVFL), and ensemble dRVFL (edRVFL) has shown state-of-the-art results on different datasets. …”

Iterative Least Squares Functional Networks Classifier by Faisal, Kanaan A

“…Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. …”
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A fuzzy basis function network for generator excitation control by Abido, M.A.

“…The orthogonal least squares learning algorithm is developed for designing an adequate and parsimonious FBFN model. …”
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Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach by Sarif, Bambang

“…The results obtained show that the introduced algorithm outperforms the deterministic technique based on the direct cover approach [3] in terms of the average number of product terms required to realize a given MVL function.…”
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Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures by Iryna Haponchyk (19691701)

“…Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. …”

Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems by Abualigah, Laith

“…In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. …”
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Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise by Saab, Samer S.

“…This work also provides a recursive algorithm that generates the appropriate learning gain functions that meet the arbitrary high precision output tracking objective. …”
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Evolution Of Activation Functions for Neural Architecture Search by Nader, Andrew

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Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm by De Rochechouart, Maxence

“…The target dynamics, the sensor selection, and the measurements are simulated by way of an agent-based modeling framework called MESA. The results show an optimum resource allocation with an information-based cost function that is optimized by an Auction Algorithm.…”
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Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms by Humna Khan (17541972)

“…The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. …”

Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm by Noor Habib Khan (22224775)

“…To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. …”

Online dynamic ensemble deep random vector functional link neural network for forecasting by Ruobin Gao (16003195)

“…<p>This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). …”

Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization by Abu Zitar, Raed

“…Nevertheless, the GOA is unsuitable for addressing multimodal, hybrid functions, and data mining problems. Therefore, the current paper proposes the orthogonal learning (OL) method with Rosenbrock’s direct rotation strategy to improve the GOA and sustain the solution variety (IGOA). …”
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Using machine learning algorithm for detection of cyber-attacks in cyber physical systems by Almajed, Rasha

“…In the event of a violation in internet security, an attacker was able to interfere with the system's functions, which might result in catastrophic consequences. …”
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Random vector functional link network: Recent developments, applications, and future directions by A.K. Malik (16003193)

“…Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. …”

Logarithmic spiral search based arithmetic optimization algorithm with selective mechanism and its application to functional electrical stimulation system control by Abu Zitar, Raed

“…The proposed algorithm (Ls-AOA) was tested against unimodal and multimodal benchmark functions and demonstrated better capability comparatively using other efficient metaheuristic algorithms reported in the literature. …”

Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology by Senyuk, Mihail

“…In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. …”
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Salp swarm algorithm: survey, analysis, and new applications by Abualigah, Laith

“…The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. …”
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Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering by Abu Zitar, Raed

“…To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. …”