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processing algorithm » processing algorithms (Expand Search)
data finding » data mining (Expand Search), data hiding (Expand Search)
element » elements (Expand Search)
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Nonlinear analysis of shell structures using image processing and machine learning
Published 2023“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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103
The Effects of Data Mining on Small Businesses in Dubai
Published 2011“…This study was conducted with the aim of finding the benefits of data mining on small businesses and specifically on the real estate sector in Dubai, and the criteria to be used when evaluating and selecting the best tools to consider for such businesses. …”
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104
A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM
Published 2020“…We also find optimal parameters values for a specific class of data sets and give recommendations on the choice of parameters for general data sets. …”
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105
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106
Machine Learning Model for a Sustainable Drilling Process
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doctoralThesis -
107
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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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
Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information
Published 2019“…For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. …”
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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
Social spider optimization algorithm: survey and new applications
Published 2024“…This algorithm has been developed over time, resulting in many versions besides theories and findings. …”
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112
Application of Data Mining to Predict and Diagnose Diabetic Retinopathy
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doctoralThesis -
113
A Scatter search algorithm for exam scheduling. (c2006)
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masterThesis -
114
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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115
Indexing Arabic texts using association rule data mining
Published 2019“…The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. Design/methodology/approach The proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. …”
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116
Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
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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. …”