Search Results - (( java implementation cell algorithm ) OR ( program effectiveness tree algorithm ))
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Classification of Google Play application using decision tree algorithm on sentiment analysis of text reviews / Aqil Khairy Hamsani, Ummu Fatihah Mohd Bahrin and Wan Dorishah Wan A...
Published 2023“…The project's objectives are to study the classifying approach of Google Play Store application reviews using the Decision Tree algorithm, develop a prototype of a classifying application program, and evaluate the accuracy model of the review classification program. …”
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Evaluation of data mining classification and clustering techniques for diabetes / Tuba Pala and Ali Yilmaz Camurcu
Published 2014“…Multilayer Perceptron algorithm has been the best algorithm with the highest success percentage in both of the programs; Decision Trees has been the algorithm which has the lowest success percentage again in both of the programs. …”
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Prime-based method for interactive mining of frequent patterns
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Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat
Published 2022“…This work investigates the suitability and effectiveness of machine learning algorithms such as Multinomial Naive Bayes, KNN, Logistic Regression, Decision Tree for predicting levels of arousal intensity among the programmers and LSTM deep learning algorithm to classify the programmers according to their performance. …”
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Correlation analysis and predictive performance based on KNN and decision tree with augmented reality for nuclear primary cooling process / Ahmad Azhari Mohamad Nor
Published 2024“…Subsequently, predictive models employing k-nearest neighbour and decision tree algorithms are constructed and evaluated based on accuracy, precision, and recall metrics. …”
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Enhancing understanding of programming concepts through physical games
Published 2017“…We produced in total 10 lesson games to illustrate variables, swapping, arrays, sorting algorithm particularly bubble sort, quicksort, selection sort, graph theory, dynamic programming, amortized analysis and trees. …”
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Conference or Workshop Item -
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Implementation of Health Monitoring System for Patients using Machine Learning Algorithms
Published 2024“…We employed the Decision Tree Algorithm to train and assess a model that produced a perfection of 66.66%.…”
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Enhanced dynamic security assessment for power system under normal and fake tripping contingencies.
Published 2019“…The hybrid logistic model tree (hybrid LMT) approach proposed in this study combines the symmetrical uncertainties (SU) algorithm and the logistic model tree (LMT) algorithm. …”
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Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines
Published 2018“…This paper presents a cost-effective approach based on a convolutional neural network (CNN) algorithm to compute the height (depth maps) of objects proximal to power poles and transmission lines. …”
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Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines
Published 2018“…This paper presents a cost-effective approach based on a convolutional neural network (CNN) algorithm to compute the height (depth maps) of objects proximal to power poles and transmission lines. …”
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Artificial Intelligence (AI) to predict dental student academic performance based on pre-university results
Published 2022“…Logistic Regression (LR) is the most effective algorithm for forecasting student success in Year 1 with accuracy 0.88 and Decision Tree (DT) in Year 3 with accuracy 0.9. …”
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Proceeding Paper -
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Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques
Published 2021“…Therefore, in this research an algorithm has been created which can achieve the following goals. 1) To generate DTM only with LiDAR data without the need for layers and other information from the area 2) To create a building footprint from the LiDAR data by removing the tree cover effect 3) To create an automatic system that can perform the production process of DTM and footprint without the intervention of an expert. …”
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Prediction of novel doping agent using an in silico model that integrates chemical, biological and phenotypic data
Published 2016“…In this study, two different training sets, termed as biological and phenotypic were compiled and three molecular descriptors (MACCS, ECFP4, FCFP4) and two machine learning algorithms (Naive Bayes and Decision Tree) were employed to build the predictive models. …”
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Student Project -
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Empirical study on intelligent android malware detection based on supervised machine learning
Published 2020“…More significantly, this paper empirically discusses and compares the performances of six supervised machine learning algorithms, known as K-Nearest Neighbors (K-NN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR), which are commonly used in the literature for detecting malware apps.…”
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