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The Effects of Data Mining on Small Businesses in Dubai
Published 2011“…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …”
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104
Sentiment visualization of correlation of loneliness mapped through social intelligence analysis
Published 2024“…Social media platforms have become a valuable source of data to study this phenomenon.</p><h3>Objectives</h3><p dir="ltr">This paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. …”
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105
Mining airline data for CRM strategies. (c2006)
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masterThesis -
106
Evolutionary algorithm for predicting all-atom protein structure
Published 2011“…We present an improved version of a scatter search (SS) algorithm for predicting all-atoms protein structures using a recent energy model. …”
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conferenceObject -
107
Capturing outline of fonts using genetic algorithm and splines
Published 2001“…In order to obtain a good spline model from large measurement data, we frequently have to deal with knots as variables, which becomes a continuous, non-linear and multivariate optimization problem with many local optima. …”
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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
Application of Data Mining to Predict and Diagnose Diabetic Retinopathy
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doctoralThesis -
110
The effects of data balancing approaches: A case study
Published 2023“…In this research, classification algorithms, combined with resampling strategies and dimensionality reduction methods, were investigated to find a prediction model to correctly identify the samples of treated animals. …”
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111
Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review
Published 2021“…With the massive smart meter integration, DL takes advantage of the large-scale and multi-source data representations to achieve a spectacular performance and high PV forecastability potential compared to classical models. …”
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112
Scatter search for homology modeling
Published 2016“…Results obtained by our SS algorithm are compared with other approaches. The 3D models predicted by our algorithm show improved root mean standard deviations with respect to the native structures.…”
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Integration of Artificial Intelligence in E-Procurement of the Hospitality Industry: A Case Study in the UAE
Published 2020“…The data analytics maturity model is used as the conceptual model for evaluating both data analytics and data governance in this research. …”
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115
TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection
Published 2020“…TIDCS reduces the number of features in the input data based on a new algorithm for feature selection. …”
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A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models
Published 2024“…Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. …”
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117
A method for data path synthesis using neural networks
Published 2017“…Presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. …”
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conferenceObject -
118
Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
Published 2024“…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 -
119
Correlation Clustering with Overlaps
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masterThesis -
120
An efficient approach for textual data classification using deep learning
Published 2022“…<p dir="ltr">Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. …”