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modeling algorithm » scheduling algorithm (Expand Search)
data modeling » data models (Expand Search), spatial modeling (Expand Search)
develop » developed (Expand Search)
element » elements (Expand Search)
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61
Using Machine Learning Algorithms to Forecast Solar Energy Power Output
Published 2025“…The data forecasting horizon used was a 24-h window in steps of 30 min. …”
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Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms
Published 2023“…Eighteen classification models were built using different classification algorithms such as Gaussian Naive Bais, Logistic Regression, k-nearest neighbors, Random Forest, and Decision Tree. …”
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63
Time-varying volatility model equipped with regime switching factor: valuation of option price written on energy futures
Published 2025“…To determine the parameters of the regime switching model and identify when economic states change, we employ the EM algorithm, utilizing real gas futures price data. …”
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A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008)
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masterThesis -
66
Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems
Published 2023“…First, a feature selection tool using PSO Algorithm is developed. Then, in order to maximize the diversity between data samples and improve the effectiveness of using PSO algorithm for feature selection, the Euclidean distance metric is used in order to reduce the data and maximize the diversity between data samples. …”
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Auto-indexing Arabic texts based on association rule data mining. (c2015)
Published 2015“…In this work, we propose a new model to enhance auto-indexing Arabic texts. Our model denotes extracting new relevant words by relating those chosen by the previous classical methods, to new words using data mining rules. …”
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masterThesis -
68
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Published 2022“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”
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An Effective Hash Based Assessment and Recovery Algorithm for Healthcare Systems
Published 2019“…Finally, the experimental results prove the improvements provided by our hash based algorithm over previously suggested models.…”
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masterThesis -
72
Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
Published 2022“…However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Lévy flight (LF) distribution. …”
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73
Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?
Published 2021“…The resulting model achieves an 89% predictive accuracy using historical data. …”
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Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark
Published 2021“…However, with the huge increase in data size, sophisticated models have to be created which require big data platforms. …”
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Deploying model obfuscation: towards the privacy of decision-making models on shared platforms
Published 2024“…The implementation nuances involve data and model sharing among allies and partners working on the same domain. …”
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76
AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques
Published 2023“…This paper proposes a novel automated algorithm for the health monitoring of concrete column base cover degradation based on IoT and the state-of-the-art deep learning framework, Convolutional Neural Network (CNN). …”
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77
Predicting Dropouts among a Homogeneous Population using a Data Mining Approach
Published 2019“…Our research relies solely on pre-college and college performance data available in the institutional database. Our research reveals that the Gradient Boosted Trees is a robust algorithm that predicts dropouts with an accuracy of 79.31% and AUC of 88.4% using only pre-enrollment data. …”
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78
Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment
Published 2023“…First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. …”
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