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1
Clustering of rainfall data using k-means algorithm
Published 2019“…Clustering algorithms in data mining is the method for extracting useful information for a given data. …”
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2
A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat
Published 2021“…The combined method is called Hybrid K-MeansCGA. Modifications of K-Means structures were done by inserting genetic algorithm operators and tuning the population. …”
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Thesis -
3
Spatial Clustering Algorithm for Time Series Rainfall Data Using X-Means Data Splitting
Published 2017“…Therefore, a clustering algorithm by introducing data transformation using X-means data splitting is proposed to investigate the spatial homogeneity of time series rainfall data. …”
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4
Web based clustering tool using K-MEAN++ algorithm / Muhammad Nur Syazwanie Aznan
Published 2019“…Cluster analysis is one of the data mining task that are widely used in many area to extracting, grouping data with similar attribute in order to uncover the hidden pattern and meaning in the data. …”
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5
Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm
Published 2015“…The self organizing map (SOM) combined with the K-means algorithm arranged the data based on the relationships of 25 variables. …”
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6
Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…In cluster labelling process, a cluster labelling algorithm based on calculation of minimum-distance (MD) between cluster mean and class mean was developed to label the clusters. …”
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7
Big Data Mining Using K-Means and DBSCAN Clustering Techniques
Published 2022“…The density-based spatial clustering of applications with noise (DBSCAN) and the K-means algorithm were used to develop clustering algorithms. …”
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8
HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC
Published 2014“…The algorithm gives a mean accuracy of 84% out of 125 test images.…”
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Final Year Project -
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Performance of Levenberg-Marquardt neural network algorithm in power quality disturbances classification / Adibah I’zzah Mohamad Kasim
Published 2025“…This study investigates the application of the Levenberg-Marquardt (LM) neural network algorithm for classifying PQDs such as voltage sags, swells, and transients. …”
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10
Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
Published 2014“…Texture features involved the middle energy and the middle entropy and shape features involved statistical characterizations including variance, median, standard deviation and mean. Next, the classification was carried out. First, the best set of structures for feed forward neural network were found by multi objective parallel genetic algorithm. …”
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11
Development Of Fall Risk Clustering Algorithm In Older People
Published 2020“…Therefore, the aim of this study is to develop a clustering-based fall risk algorithm which can provide assistances for clinician in management of falls. …”
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Final Year Project / Dissertation / Thesis -
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Robust remote heart rate estimation from multiple asynchronous noisy channels using autoregressive model with Kalman filter
Published 2019“…Also, it indicates that our algorithm can be used for the online environment. Finally, the application of our algorithm in search and rescue scenarios using drones is considered and an experiment is conducted to investigate the algorithm's potential to be embedded in drones.…”
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13
Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data
Published 2023“…The MIRR were computed based on the data extracted from the companies’ financial reports from 2010 to 2018. …”
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14
Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”
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Improved measurement of blood pressure by extraction of characteristic features from the cuff oscillometric waveform
Published 2015“…An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. …”
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The classification of wink-based eeg signals by means of transfer learning models
Published 2021“…EEG signals which contains information on the activities carried out by a human that is widely used in many applications of BCI technologies which offers a means of controlling exoskeletons or automated orthosis to facilitate their ADL. …”
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17
Rule-based filtering algorithm for textual document
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Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients
Published 2017“…The proposed method is based on a hybrid method integrating random walkers algorithm with integrated priors and particle swarm optimized spatial fuzzy c-means (FCM) algorithm with level set method and AdaBoost classifier. …”
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19
Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment
Published 2013“…In conclusion, hybrid classification as a combination of k-means and support vector machine algorithms and post-classification comparison change detection technique can be used to monitor land cover changes in Halabja city, Iraq. …”
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20
Pemetaan Pm10 Dan Aot Menggunakan Teknik Penderiaan Jauh Di Semenanjung Malaysia
Published 2006“…The two-band model, terma linear and modified algorithms were selected based on the highe.st correlation coefficient (R) value {> 80%) and the lowest root-mean-square (RMS) error value. …”
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