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Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods
Published 2022“…The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. …”
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103
Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV
Published 2013Get full text
doctoralThesis -
104
Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks
Published 2024“…Nowadays, deep learning algorithms are essential for ensuring the reliable, safe, and efficient operation of these complex energy systems. …”
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105
Machine Learning Model for a Sustainable Drilling Process
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doctoralThesis -
106
Recent Advances of Chimp Optimization Algorithm: Variants and Applications
Published 2023“…Chimp Optimization Algorithm (ChOA) is one of the recent metaheuristics swarm intelligence methods. …”
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107
Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…So far none addresses the use of Threat Intelligence (IT) data in Ensemble Learning algorithms to improve the detection process, nor does it work as a function of time, that is, taking into account what happens on the network in a limited time interval. …”
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108
Data redundancy management for leaf-edges in connected environments
Published 2022“…Major advances in the fields of Internet and Communication Technology (ICT), data modeling/processing, and sensing technology have rendered traditional environments (e.g., cities, buildings) more connected. …”
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109
Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments
Published 2025“…These processes begin with capturing speech in analog form and applying signal processing algorithms to ensure compatibility with devices like cochlear implants (CIs). …”
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110
A hybrid graph representation for recursive backtracking algorithms
Published 2017“…The performance of these algorithms often suffers from the increasing number of graph modifications, such as deletions, that reduce the problem instance and have to be “taken back” frequently during the search process. …”
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conferenceObject -
111
The effects of data balancing approaches: A case study
Published 2023“…Our results showed that the replacement method was effective, and LogisticRegression combined with the oversampling algorithms SMOTE or ADASYN, GaussianProcessClassifier with the oversampling algorithm SMOTE, and LinearDiscriminantAnalysis were the best performing models after log transformation of the dataset was followed by Recursive Feature Elimination.…”
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112
Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information
Published 2019“…In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. …”
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113
Big Data Energy Management, Analytics and Visualization for Residential Areas
Published 2020“…A high-speed distributed computing cluster based on commodity hardware with efficient big data mathematical algorithm is employed in this work. …”
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114
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Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
Published 2022Get full text
doctoralThesis -
116
Using genetic algorithms to optimize software quality estimation models
Published 2004“…This thesis explores the use of genetic algorithms for the problem of optimizing existing rule-based software quality estimation models. …”
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masterThesis -
117
An efficient approach for textual data classification using deep learning
Published 2022“…Textual data contains much useless information that must be pre-processed. …”
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118
Bee colony algorithm for assigning proctors to exams. (c2013)
Published 2013Get full text
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masterThesis -
119
Ensemble Deep Random Vector Functional Link Neural Network for Regression
Published 2022“…To address this problem, we propose a random skip connection-based edRVFL, which can keep the diversity in the latent space. esc-RVFL is an ensemble scheme that utilizes several edRVFL-RSC models trained on the different folds of the training dataset. …”
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120