Search Results - (( based visualization mining algorithm ) OR ( evolution classification _ algorithm ))
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1
A performance analysis of association rule mining algorithms
Published 2016“…The results of the CPU profiler of Java VisualVM showed that Binary-Based algorithm performs better than Eclat algorithm in-terms of both execution times and memory usage. …”
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2
DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH
Published 2005“…The project's objective is identifying the available data mining algorithms in data classification and applying new data mining algorithm to perform classification tasks. …”
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Final Year Project -
3
Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization
Published 2024thesis::doctoral thesis -
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Data mining based damage identification using imperialist competitive algorithm and artificial neural network
Published 2018“…In this study, to predict the damage severity of sin-gle-point damage scenarios of I-beam structures a data mining based damage identification framework and a hybrid algorithm combining Artificial Neural Network (ANN) and Imperial Competitive Algorithm (ICA), called ICA-ANN method, is proposed. …”
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5
Using text mining algorithm to detect gender deception based on Malaysian chat room lingo / Dianne L. M. Cheong and Nur Atiqah Sia Abdullah @ Sia Sze Yieng
Published 2006“…Based on this algorithm. a prototype in Visual Basic is developed It was tested with /6 documents; each consists of 5 e-mails exchanges of respective individuals. …”
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6
Using text mining algorithm to detect gender deception based on Malaysian chatroom lingo / Dianne L.M. Cheong and Nur Atiqah Sia Abdullah@Sia Sze Yieng
Published 2006“…Based on this designed text algorithm, a prototype in Visual Basic is developed. …”
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Research Reports -
7
Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…The proposed algorithm is grounded on the two famous metaheuristic algorithms: cuckoo search (CS) and covariance matrix adaptation evolution strategy (CMA-es). …”
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Thesis -
8
Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. …”
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9
Differential evolution for neural networks learning enhancement
Published 2008“…Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. …”
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Thesis -
10
Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…In this regard, this thesis proposes five FS methods for efficient EMG signals classification. The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
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11
Development of a new algorithm for segmentation of flotation froth images
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12
Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm
Published 2019“…The spectral features were performed by employing the relative spectral powers of delta (δRP), theta (θRP), alpha (αRP), beta (βRP), and gamma (γRP). The differential evolution-based channel selection algorithm (DEFS_Ch) was computed to find the most suitable EEG channels that have the greatest efficacy for identifying the various emotional states of the brain regions. …”
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13
Fuzzy Soft Set Clustering for Categorical Data
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14
A New Quadratic Binary Harris Hawk Optimization For Feature Selection
Published 2019“…In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). …”
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15
Intersection Features For Android Botnet Classification
Published 2019“…This paper proposed an enhancement approach for Android botnet classification based on features selection and classification algorithms. …”
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16
Stock price monitoring system
Published 2024“…Consequently, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the performance of the prediction algorithms. The methodology used in this project is Cross-Industry Standard Process for Data Mining (CRISP-DM), which is a common standard for data mining projects. …”
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Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea
Published 2018“…This research develops a knowledge-based system by using computational intelligent approaches based on Boosting algorithms on decision trees augmented by pruning techniques and Association Rule Mining. …”
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18
Feature selection optimization using hybrid relief-f with self-adaptive differential evolution
Published 2017“…Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. …”
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19
Decision Tree: Customer churn analysis for a loyalty program using data mining algorithm
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20
Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation
Published 2011“…Unlike a conventional PSOIACO algorithm, this hybrid algorithm shows improvement of the classification accuracy in its generated rough reducts to solve NP-Hard problem. …”
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