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processing algorithm » processing algorithms (Expand Search)
function algorithm » auction algorithm (Expand Search), detection algorithm (Expand Search)
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cost » most (Expand Search)
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Machine Learning Model for a Sustainable Drilling Process
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doctoralThesis -
103
Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV
Published 2013Get full text
doctoralThesis -
104
A new genetic algorithm approach for unit commitment
Published 1997“…A fitness function is constructed from the total operating cost of the generating units without penalty terms. …”
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105
Evolutionary algorithms for VLSI multi-objective netlist partitioning
Published 2006“…Fuzzy rules are incorporated in order to handle the multi-objective cost function. For SimE, fuzzy goodness functions are designed for delay and power, and proved efficient. …”
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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
An Evolutionary Algorithm for the Allocation Problem in High-Level Synthesis
Published 2005“…The method performs allocation of functional units, registers, and multiplexers in addition to controller synthesis with the objective of minimizing the cost of hardware resources. …”
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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
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 -
110
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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111
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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112
Simulated evolution algorithm for multiobjective VLSI netlist bi-partitioning
Published 2003“…Fuzzy rules are used in order to design the overall multi-objective cost function that integrates the costs of three objectives in a single overall cost value. …”
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113
A genetic-based algorithm for fuzzy unit commitment model
Published 2000“…In the implementation for the GA, coding of the UCP solutions is based on mixing binary and decimal representations. A fitness function is constructed from the total operating cost of the generating units plus a penalty term determined due to the fuzzy load and spinning reserve membership functions. …”
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114
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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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116
Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
Published 2022Get full text
doctoralThesis -
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)
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masterThesis -
119
Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
Published 2024“…The proposed method, named Eye-means, emulates the natural ocular process of estimating initial centroids. To achieve this goal, supervised machine learning was employed to train models on graphs with labeled data points, where each graph contains a set of points and a label indicating the centroid determined by K-means. …”
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masterThesis -
120
Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition
Published 2021“…Whilst different models are proposed for STLF, they are based on small historical datasets and are not scalable to process large amounts of big data as energy consumption data grow exponentially in large electric distribution networks. …”