Search Results - (( java implementation path algorithm ) OR ( missing values machine algorithm ))
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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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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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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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Enhanced mechanism to handle missing data of Hadith classifier
Published 2011“…The correct branch to take is unknown if a feature tested is missing, and the algorithm must employed enhanced mechanisms to handle missing values. …”
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Proceeding Paper -
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Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer
Published 2017“…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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Intelligent imputation method for mix data-type missing values to improve data quality
Published 2024“…A common challenge is the occurrence of missing data during the data input process. Numerous studies have proposed methods to impute missing values for data across multiple fields. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment
Published 2021“…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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Tangible interaction learning model to enhance learning activity processes among children with dyslexia
Published 2024“…A common challenge is the occurrence of missing data during the data input process. Numerous studies have proposed methods to impute missing values for data across multiple fields. …”
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Imputation Analysis of Time-Series Data Using a Random Forest Algorithm
Published 2024“…To address the issue, this paper compared and evaluated four imputation methods: MissForest, MICE, Simplefill, and Softimpute which utilized Random Forest Algorithm. …”
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Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset
Published 2014“…Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. …”
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Smart appointment organizer for mobile application / Mohd Syafiq Adam
Published 2009“…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid
Published 2022“…One of the major challenges when applying machine learning is to identify the best algorithm from a variety of algorithms to solve a problem. …”
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An Apriori-based Data Analysis on Suspicious Network Event Recognition
Published 2019“…Then, each missing value in the test data set is decided by using the obtained rules. …”
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Conference or Workshop Item -
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Predicting uniaxial compressive strength using Support Vector Machine algorithm
Published 2019“…From the result, it was found that SVM is capable of predicting the missing values with a prediction trend accuracy of 75%. …”
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Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease
Published 2024“…The baseline LightGBM model with dropped missing values had an accuracy of 0.8333, sensitivity of 0.1081, precision of 0.3429, F1 score of 0.1644, and AUC of 0.6875. …”
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Data Mining Analysis Of Chronic Kidney Disease (CKD) Level
Published 2022“…Data classifications are performed at a 10-fold-cross-validation mode using Naïve Bayes (NB), Support Vector Machine (SVM), and J48 Trees. The ZeroR algorithm was set as the baseline There are three levels of classification analyses: before and after handling the missing values, before and after the outliers’ treatment, and adding uncertain classes. …”
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