Search Results - (( java implementation rsa algorithm ) OR ( missing values using algorithm ))
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RSA Encryption & Decryption using JAVA
Published 2006“…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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Final Year Project -
2
AUTOMATED MODEL GENERATION OF FSM AND NUSMV MODEL FROM RSA JAVA SOURCE CODE FOR MODEL CHECKING
Published 2021“…RSA is one of these encryption algorithms that have been implemented in security systems. …”
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Thesis -
3
An improved RSA cryptosystem based on thread and CRT / Saheed Yakub Kayode and Gbolagade Kazeem Alagbe
Published 2017“…Java programming language is used to implement the algorithm. …”
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Article -
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Secure Image Steganography Using Encryption Algorithm
Published 2016“…A system based on the proposed algorithm will be implemented using Java and it will be more secured due to double-layer of security mechanisms which are RSA and Diffie-Hellman.…”
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Conference or Workshop Item -
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Digitally signed electronic certificate for workshop / Azinuddin Baharum
Published 2017“…Digital Signature was encrypted by RSA Algorithm, a very powerful asymmetrical encryption. …”
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Thesis -
6
Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
Published 2020“…This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches. …”
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Article -
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An Evaluation of Machine Learning Algorithms for Missing Values Imputation
Published 2019“…It represents the research and imputation of missing values in gene expression data. By using the local or global correlation of the data we focus mostly on the contrast of the algorithms. …”
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Article -
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Missing-values imputation algorithms for microarray gene expression data
Published 2019“…Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371–385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. …”
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Book Chapter -
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An improved K-nearest neighbour with grasshopper optimization algorithm for imputation of missing data
Published 2021“…K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. …”
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Article -
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Systematic review on missing data imputation techniques with machine learning algorithms for healthcare
Published 2022“…Many machine learning algorithms have been applied to impute missing data with plausible values. …”
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Article -
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Detection Of Misplaced And Missing Regions In Image Using Neural Network
Published 2017“…The neural network uses the RGB value from the image processing phase and analyzes the regions to check whether there is misplaced or missing jigsaw puzzle. …”
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Thesis -
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Improving knowledge extraction of Hadith classifier using decision tree algorithm
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Proceeding Paper -
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New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Missing values lead to the difficulty of extracting useful information from that data set. …”
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Thesis -
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ExtraImpute: a novel machine learning method for missing data imputation
Published 2022“…This approach imputes each missing value that exists in features by predicting its value using other observed values in the dataset. …”
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Article -
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Enhanced mechanism to handle missing data of Hadith classifier
Published 2011“…Decision tree algorithms have the ability to deal with missing values or wrong data. …”
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Proceeding Paper -
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Missing values imputation tool using imputex algorithm
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Article -
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Missing value estimation methods for data in linear functional relationship model
Published 2017“…Missing value problem is common when analysing quantitative data. …”
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Article -
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Evaluating A New Adaptive Group Lasso Imputation Technique For Handling Missing Values In Compositional Data
Published 2024“…The complexity of compositional data with missing values renders traditional estimation methods inadequate. …”
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Thesis
