Search Results - (( java implementation force algorithm ) OR ( using missing based algorithm ))
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Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
Published 2016“…Based on the analysis, the proposed singular imputation algorithms, which treated missing data by geometric means, harmonic means and medians are more superior compared to the other imputation algorithms, irrespective of missing rates and rainfall stations. …”
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Determination of the best single imputation algorithm for missing rainfall data treatment
Published 2016“…Based on the analysis, the proposed singular imputation algorithms, which treated missing data by geometric means, harmonic means and medians are more superior compared to the other imputation algorithms, irrespective of missing rates and rainfall stations.…”
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Missing tags detection algorithm for radio frequency identification (RFID) data stream
Published 2019“…Thus in this research, an AC complement algorithm with hashing algorithm and Detect False Negative Read algorithm (DFR) is used to developed the Missing Tags Detection Algorithm (MTDA). …”
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MISSING DAILY RAINFALL PREDICTION USING GREY WOLF OPTIMIZER-BASED NEURAL NETWORK
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SINE COSINE ALGORITHM BASED NEURAL NETWORK FOR RAINFALL DATA IMPUTATION
Published 2024“…This chapter presents the ability of the sine cosine algorithm-based neural network (SCANN) to predict and optimize missing rainfall at different percentages of missing rates. …”
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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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Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
Published 2020“…The novel optimization-based artificial intelligence algorithm proposed in this paper implies an improved way to overcome a real engineering challenge i.e. handling missing values for better RUL prediction, hence bringing great opportunities for the domain area. …”
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Detection Of Misplaced And Missing Regions In Image Using Neural Network
Published 2017“…Therefore, it is necessary to develop an algorithm that is able to detect both misplaced and missing jigsaw puzzles. …”
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Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method(NNM) and Expectation-Maximization (EM) Algorithm
Published 2020“…The missing data is imputed by using both NNM and EM algorithm. …”
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Missing-values imputation algorithms for microarray gene expression data
Published 2019“…We classified the algorithms as global, hybrid, local, or knowledge-based techniques. …”
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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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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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Deep-learning-based detection of missing road lane markings using YOLOv5 algorithm
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Proceeding Paper -
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A Rough Set-Based Approach for Identifying and Replacing Missing Concepts in Incomplete Sentences in Computer Domain Texts
Published 2026thesis::master thesis -
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Missing values imputation tool using imputex algorithm
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Development of an imputation technique - INI for software metric database with incomplete data
Published 2007“…In this paper, an imputation technique for imputing missing data based on global-local Modified Singular Value Decomposition (MSVD) algorithm, INI was proposed. …”
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Missing point on the corner by using extension cases of advancing front mesh method
Published 2020“…The holes focused was on the missing point on the corner of the object. In the algorithm, object was chosen and two procedure used to achieved the results. …”
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Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan
Published 2015“…Several methods in constructing the CI for the concentration parameter are proposed including CI based on circular population, CI based on the asymptotic distribution of ˆ , CI based on the distribution of 휃 and 푅 and also CI based on bootstrap-t method. …”
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INCENTIVE-SCHEDULING ALGORITHMS TO PROVIDE GREEN COMPUTATIONAL DATA CENTER
Published 2017“…To solve this issue, green incentive based scheduling algorithms were devised to significantly save cooling electricity consumption cost in DC and also be able to compensate users for their submitted jobs that missed the job completion deadline(s).…”
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