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Learning continuous functions using decision tree learning algorithms
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Computational evluation of protein energy functions
Published 2014“…A protein is characterized by its 3D structure, which defines its biological function. …”
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Evolutionary algorithm for protein structure prediction
Published 2010“…A protein is characterized by its 3D structure, which defines its biological function. …”
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Scatter Search algorithm for Protein Structure Prediction
Published 2016“…Given the protein's sequence of Amino Acids (AAs), our algorithm produces a 3D structure that aims to minimise the energy function associated with the structure. …”
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Evolutionary algorithm for predicting all-atom protein structure
Published 2011“…This algorithm produces a 3D structure of the whole protein, including back-bone and side-chain atoms, by minimizing the energy function associated with the structure. …”
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Scatter search for protein structure prediction. (c2008)
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7
AGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics
Published 2022“…The term AGEomics is defined as multiplexed quantitation of spontaneous modification of proteins damage and other usually low-level modifications associated with a change of structure and function—for example, citrullination and transglutamination. …”
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Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise
Published 2005“…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
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Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm
Published 2024“…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. …”
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Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms
Published 2022“…The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. …”
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Online dynamic ensemble deep random vector functional link neural network for forecasting
Published 2023“…<p>This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). …”
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Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
Published 2022“…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
Published 2022“…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
Published 2023“…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. …”
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Published 2023“…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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Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures
Published 2017“…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. …”