Showing 1 - 16 results of 16 for search '(((( algorithm python function ) OR ( algorithms mc function ))) OR ( algorithm co function ))', query time: 0.10s Refine Results
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    Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network by Fares Almomani (12585685)

    Published 2020
    “…The CMP was the highest for the substrate with moisture content (%MC) in the range of 34% to 48%, and it decreased for %MC > 50%. …”
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    Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO by Majedeh Gheytanzadeh (17541927)

    Published 2022
    “…The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. …”
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    Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures by Iryna Haponchyk (19691701)

    Published 2017
    “…The accurate model comparison on the standard CoNLL-2012 setting shows the benefit of more expressive loss functions.…”
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    Reinforcement Learning-Based School Energy Management System by Yassine Chemingui (18891757)

    Published 2020
    “…In recent years, the Deep Reinforcement Learning algorithm, applying neural networks for function approximation, shows promising results in handling such complex problems. …”
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    Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition by Abboud, Ralph

    Published 2019
    “…Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. …”
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