scholarly journals Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

2020 ◽  
Author(s):  
Castro Mayleen Dorcas Bondoc ◽  
Tumibay Gilbert Malawit
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Video Tutorials ◽  
Learning Management ◽  
Face To Face Instruction

Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.

Support Vector Machine

2009 ◽  
pp. 501-522 ◽  
Author(s):  
A. B.M. Shawkat Ali
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
System Analysis ◽  
Learning Algorithm ◽  
Mathematical Formulation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Support Vector ◽  
Analysis And Design ◽  
Simple Demonstration

From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.

scholarly journals Prediction of dyslexia using support vector machine in distributed environment

2018 ◽  
Vol 7 (4) ◽  
pp. 2795
Author(s):  
Jothi Prabha A ◽  
Bhargavi R ◽  
Ramesh Ragala
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Research Work ◽  
Great Difficulty ◽  
Support Vector ◽  
Learning Disorder ◽  
Distributed Environment ◽  
Brain Images ◽  
Brain Behavior

Dyslexia is a learning disorder characterized by lack of reading and /or writing skills, difficulty in rapid word naming and also poor in spelling. Dyslexic individuals have great difficulty to read and interpret words or letters. Research work is carried out to classify dyslexic from non-dyslexics by various approaches such as machine learning, image processing, understanding the brain behavior through psychology, studying the differences in anatomy of brain. In addition to it several assistive tools are developed to support dyslexics. In this work, brain images are used for screening individuals who have high risk to dyslexia. This work also motivates the application of machine learning in distributed environment. The proposed predictive model uses the machine-learning algorithm Support Vector Machine (SVM). The model is designed in Apache SPARK framework to support voluminous data. The prediction accuracy of 92.5% is achieved using SVM. 

A Practical Fault Tolerance Approach in Cloud Computing Using Support Vector Machine

2020 ◽  
pp. 45-49
Author(s):  
Gajendra Sharma ◽  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cloud Computing ◽  
Fault Tolerance ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Tolerance Approach ◽  
Systematic Solution

Fault tolerance is an important issue in the field of cloud computing which is concerned with the techniques or mechanism needed to enable a system to tolerate the faults that may encounter during its functioning. Fault tolerance policy can be categorized into three categories viz. proactive, reactive and adaptive. Providing a systematic solution the loss can be minimized and guarantee the availability and reliability of the critical services. The purpose and scope of this study is to recommend Support Vector Machine, a supervised machine learning algorithm to proactively monitor the fault so as to increase the availability and reliability by combining the strength of machine learning algorithm with cloud computing.

scholarly journals The effect of gamma value on support vector machine performance with different kernels

2020 ◽  
Vol 10 (5) ◽  
pp. 5497
Author(s):  
Intisar Shadeed Al-Mejibli ◽  
Jwan K. Alwan ◽  
Dhafar Hamed Abd
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Kernel Functions ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Performance ◽  
Rbf Kernel ◽  
The Impact

Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.

scholarly journals Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

2012 ◽  
Vol 9 (73) ◽  
pp. 1934-1942 ◽  
Author(s):  
Philip J. Hepworth ◽  
Alexey V. Nefedov ◽  
Ilya B. Muchnik ◽  
Kenton L. Morgan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Important Indicator ◽  
Unseen Data

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

scholarly journals FoRex Trading Using Supervised Machine Learning

2018 ◽  
Vol 7 (4.15) ◽  
pp. 400 ◽  
Author(s):  
Thuy Nguyen Thi Thu ◽  
Vuong Dang Xuan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Exchange Rate ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Machine Learning Model

The exchange rate of each money pair can be predicted by using machine learning algorithm during classification process. With the help of supervised machine learning model, the predicted uptrend or downtrend of FoRex rate might help traders to have right decision on FoRex transactions. The installation of machine learning algorithms in the FoRex trading online market can automatically make the transactions of buying/selling. All the transactions in the experiment are performed by using scripts added-on in transaction application. The capital, profits results of use support vector machine (SVM) models are higher than the normal one (without use of SVM). 

scholarly journals A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Pradeepa H. Dakappa ◽  
Keerthana Prasad ◽  
Sathish B. Rao ◽  
Ganaraja Bolumbu ◽  
Gopalkrishna K. Bhat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Dengue Fever ◽  
Bacterial Infections ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Tympanic Temperature ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Temperature Recording

Diagnosis of undifferentiated fever is a major challenging task to the physician which often remains undiagnosed and delays the treatment. The aim of the study was to record and analyze a 24-hour continuous tympanic temperature and evaluate its utility in the diagnosis of undifferentiated fevers. This was an observational study conducted in the Kasturba Medical College and Hospitals, Mangaluru, India. A total of ninety-six (n=96) patients were presented with undifferentiated fever. Their tympanic temperature was recorded continuously for 24 hours. Temperature data were preprocessed and various signal characteristic features were extracted and trained in classification machine learning algorithms using MATLAB software. The quadratic support vector machine algorithm yielded an overall accuracy of 71.9% in differentiating the fevers into four major categories, namely, tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases. The area under ROC curve for tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases was found to be 0.961, 0.801, 0.815, and 0.818, respectively. Good agreement was observed [kappa = 0.618 (p<0.001, 95% CI (0.498–0.737))] between the actual diagnosis of cases and the quadratic support vector machine learning algorithm. The 24-hour continuous tympanic temperature recording with supervised machine learning algorithm appears to be a promising noninvasive and reliable diagnostic tool.

scholarly journals Kernel Parameter Tuning to Tweak the Performance of Classifiers for Identification of Heart Diseases

2021 ◽  
Vol 12 (4) ◽  
pp. 1-16
Author(s):  
Annu Dhankhar ◽  
Sapna Juneja ◽  
Abhinav Juneja ◽  
Vikram Bali
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Research Work ◽  
Main Idea ◽  
Parameter Tuning ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Initial Stage ◽  
Kernel Parameter

Medical data analysis is being recognized as a field of enormous research possibilities due to the fact there is a huge amount of data available and prediction in initial stage may save patient lives with timely intervention. With machine learning, a particular algorithm may be created through which any disease may be predicted well in advance on the basis of its feature sets or its symptoms can be detected. With respect to this research work, heart disease will be predicted with support vector machine that falls under the category of supervised machine learning algorithm. The main idea of this study is to focus on the significance of parameter tuning to elevate the performance of classifier. The results achieved were then compared with normal classifier SVM before tuning the parameters. Results depict that the hyperparameters tuning enhances the performance of the model. Finally, results were calculated by using various validation metrics.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

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