Search Results - (( process application means algorithm ) OR ( java implication based algorithm ))
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Complex-valued nonlinear adaptive filters for noncircular signals
Published 2017“…Their importance in real-world application is showed through case studies. The CC-CNGD algorithm rigorously takes advantage of the fast convergence rate of the CNGD algorithm and as well exploit the low Means Square Error (MSE) of the ACNGD algorithm in order to circumvent the problem of slow convergence rate and high Mean Square Error (MSE) seen in the family of complex signal. …”
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Thesis -
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Malaria parasites segmentation in red blood cells images using mean-shift and median-cut
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Book Section -
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Extracting feature from images by using K-Means clustering algorithm / Abdul Hakim Zainal Abidin
Published 2016“…This research purposed clustering algorithm to improve process extracting feature in images to get meaningful information because it can speed up the time to process of extracting meaningful information in images due to the efficient of the algorithm that has high performance to process the image. …”
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Thesis -
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Adaptive interference canceller using analog algorithm with offset voltage
Published 2015“…LMS and NLMS algorithms have been used in a wide range of signal processing applications because of their simplicity in computations compared to the RLS algorithm. …”
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Thesis -
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loformation Retrieval - using Porter Stemming Algorithm
Published 2006“…The scope of the project is to implement the original Porter Stemming Algorithm in the application to improved the precision and recall in the retrieving document process. …”
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Final Year Project -
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Tracking The Eyes Using Interdependence Mean Shift Tracking Algorithm With Appropriate Information Provided
Published 2016“…The human eye tracking algorithm is very important, especially in the facial analysis application. …”
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Pattern discovery using k-means algorithm
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Conference or Workshop Item -
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A novel quantum calculus-based complex least mean square algorithm (q-CLMS)
Published 2022“…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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A novel quantum calculus-based complex least mean square algorithm (q-CLMS)
Published 2022“…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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A novel quantum calculus-based complex least mean square algorithm (q-CLMS)
Published 2023“…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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Segmentation of MRI brain images using statistical approaches
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Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation
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Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh
Published 2019“…Moreover, various indicators were implemented to evaluate the ANN model’s productivity including relative root mean square error (RRMSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE) and relative error (RE). …”
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Thesis -
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Recursive least square and fuzzy modelling using genetic algorithm for process control application
Published 2007“…Results show that fuzzy model with genetic algorithm gives minimum mean squared error compare with recursive least square.…”
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Conference or Workshop Item -
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Support Vector Machines (SVM) in Test Extraction
Published 2006“…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. Each word in the particular document is processed by both algorithms to determine its actual occurrence in the document by which it will first be clustered or grouped into categories based on parts of speech (verb, noun, adjective) which is done by K-Means, then later processed by SVM to determine the actual occurrence of each word in each of the cluster, taking into account whether the words have similar meanings with otherwords in the subsequent cluster. …”
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Final Year Project -
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Support Vector Machines (SVM) in Test Extraction
Published 2006“…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. Each word in the particular document is processed by both algorithms to determine its actual occurrence in the document by which it will first be clustered or grouped into categories based on parts of speech (verb, noun, adjective) which is done by K-Means, then later processed by SVM to determine the actual occurrence of each word in each of the cluster, taking into account whether the words have similar meanings with otherwords in the subsequent cluster. …”
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Final Year Project
