[PDF] Top 20 IMPLEMENTATION AND ANALYSIS OF MACHINE LEARNING APPROACHES AND TECHNIQUES FOR STUDENT DROPOUT PREDICTION
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IMPLEMENTATION AND ANALYSIS OF MACHINE LEARNING APPROACHES AND TECHNIQUES FOR STUDENT DROPOUT PREDICTION
... Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future ...the analysis of a known training dataset, the ... See full document
7
Comparative Analysis of Machine Learning Techniques for Rain Prediction
... various approaches and algorithms employed by researches for rain prediction is shown in an exceedingly tabular ...the techniques and approaches utilized in the sector of rain ... See full document
7
An Analysis On Breast Disease Prediction Using Machine Learning Approaches
... supervised machine learning techniques. They applied multiple machine learning algorithms, including LR, RF, DT, and Multi-layer ...two machine learning classifiers NB and ... See full document
6
A Comparative Analysis Of Parkinson Disease Prediction Using Machine Learning Approaches
... Machine learning that aims to solve a diverse medicinal and clinical issue [8] ...that machine learning methods have picked up genuinely superior in classification based medical ...supervised ... See full document
5
Analysis of Student Performance using Machine Learning Techniques
... with student performance, and show that they can be used to enhance CFA ...CFA prediction in the context of ...users. Student performance prediction is an intriguing research area, and ... See full document
9
Prediction Model for Student Dropout Analysis using Data Mining Algorithms
... different Learning Management ...separation learning for dropout ...Instance-Based Learning Algorithms, Logistic Regression and Support Vector ... See full document
6
Rainfall prediction using Machine Learning Techniques
... Component Analysis (PCA), which was introduced by ...selection approaches. A few approaches used in feature selection are RELIEF, CMIM, BW-ration, Correlation Coefficient, GA, SVM-REF, Non-Linear ... See full document
7
Intervention Prediction and Progressive Learning Using Machine Learning Techniques
... Abstract-Machine learning algorithms have many applications in supporting target intervention ...progressive learning, and to establish a perfect prediction of intervention assessment using ... See full document
11
Prediction Of Rainfall Using Machine Learning Techniques
... This prediction mainly helps farmers and also water resources can be utilized ...Rainfall prediction is a challenging task and the results should be ...using machine learning techniques ... See full document
5
Automatic Student Analysis and Placement Prediction using Advanced Machine Learning Algorithms
... OnHot Encoding is a process that converts the categorial values in the collected data to the numerical or other ordinal values. These converted values are provided to machine learning algorithms for the ... See full document
6
Computational Methods in Linear B cell Epitope Prediction
... scales, prediction accuracy could not be improved to a great ...sophisticated machine learning approaches for predicting linear B-cell epitopes need to be ... See full document
5
Cancer Prediction and Prognosis Using Machine Learning Techniques
... the prediction of cancer disease by means of SSL learning ...of machine learning algorithms will become common in many clinical and hospital settings to prevent the delay of treatment ... See full document
5
1. Comparative study of deep learning based sentimental analysis with other existence techniques
... various machine learning algorithms have been proposed in literatures that are used to classify the ...These machine learning algorithms such as Support Vector Machine (SVM), Naive ... See full document
12
A Study of Classification Techniques of Data Mining Techniques in Health Related Research
... E. SVM(Support Vector Machine) classification: SVM was invented by Boser, Guyon and Vapnik. The Support vector machine deals with pattern classification [3]. There are two types of patterns linear and ... See full document
8
Product Aspect Ranking
... sentiment analysis approaches are ...sentiment analysis so far has mainly focused on two things: identifying whether a given textual entity is subjective or objective, and identifying polarity of ... See full document
5
Agro Genius: Crop Prediction using Machine Learning
... implemented techniques in usage, but agriculture department keeps so many raw data and using few in their website for public access, but it is not helpful to ... See full document
7
A Big Data Methodology for Sentiment Analysis of Twitter Data
... sentimental analysis using machine learning techniques and provide some ...the analysis will be trend analysis with different sections that is positive, negative and ... See full document
7
Spatial Prediction of Landslides using Time Series Analysis and Support Vector Machine
... GA is a family of population-based search algorithms for optimization problems. They maintain a set of solutions known as population. In each generation, it generates a new population from the current population using a ... See full document
6
Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
... Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical meth- ods, including support vector machine, ... See full document
11
A Survey on Graph based Approaches in Sentiment Analysis
... and machine learning which is used to find a linear combination of features that characterizes two or more class of ...of machine learning and artificial ... See full document
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