scholarly journals Analyzing Navigational Data and Predicting Student Grades Using Support Vector Machine

2020 ◽  
Vol 4 (4) ◽  
pp. 243-252
Author(s):  
SriUdaya Damuluri ◽  
Khondkar Islam ◽  
Pouyan Ahmadi ◽  
Namra Shafiq Qureshi
Keyword(s):  
Support Vector Machine ◽  
Information Science ◽  
Research Study ◽  
Educational Institutions ◽  
Support Vector ◽  
Classification Algorithms ◽  
Student Access ◽  
Final Grades ◽  
Student Grades ◽  
Access Patterns

The advent of Learning Management System (LMS) has unfolded a unique opportunity to predict student grades well in advance which benefits both students and educational institutions. The objective of this study is to investigate student access patterns and navigational data of Blackboard (Bb), a form of LMS, to forecast final grades. This research study consists of students who are pursuing a Networking course in Information Science and Technology Department (IST) at George Mason University (GMU). The gathered data consists of a wide variety of attributes, such as the amount of time spent on lecture slides and other learning materials, number of times course contents are accessed, time and days of the week study material is reviewed, and student grades in various assessments. By analyzing these predictors using Support Vector Machine, one of the most efficient classification algorithms available, we are able to project final grades of students and identify those individuals who are at risk for failing the course so that they can receive proper guidance from instructors. After comparing actual grades with predicted grades, it is concluded that our developed model is able to accurately predict grades of 70% of the students. This study stands unique as it is the first to employ solely online LMS data to successfully deduce academic outcomes of students.

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

2020 ◽  
Vol 123 (4) ◽  
pp. 573-586
Author(s):  
M. Twala ◽  
R. J. Roberts ◽  
C. Munghemezulu
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Bushveld Complex ◽  
Support Vector ◽  
Classification Algorithms ◽  
Error Matrix ◽  
Geological Processes ◽  
Multispectral Remote Sensing ◽  
Eastern Limb

Abstract Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.

Simulation of Fluctuating Wind Speeds Based on Data-Driven Approaches

2014 ◽  
Vol 635-637 ◽  
pp. 1618-1623
Author(s):  
Yue Dan Wang ◽  
Chun Xiang Li
Keyword(s):  
Support Vector Machine ◽  
Bp Neural Network ◽  
Information Science ◽  
Prediction Models ◽  
Rapid Development ◽  
Data Driven ◽  
Support Vector ◽  
Wind Speeds ◽  
Fluctuating Wind ◽  
Simulation Time

With the rapid development of information science and technology, data-driven approaches are already being the research tide in many fields. BP neural network (BPNN), support vector machine (SVM) and least squares support vector machine (LS-SVM) are introduced and adopted to simulate fluctuating time-series wind speeds in this paper. The regression-prediction models developed by implementing machine interpolation learning are established respectively. And the original speeds used as learning and forecast samples for the simulation of the data-driven approaches are obtained through AR numerical modeling. Based on the comparison of evaluation index, the results show that the simulated fluctuating wind speeds through SVM and LS-SVM are more accurate than the simulated speeds through BPNN, but the simulation time of LS-SVM and BPNN are shorter than the SVM.

scholarly journals NAIVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE SEBAGAI ALTERNATIF SOLUSI UNTUK TEXT MINING

2019 ◽  
Vol 12 (2) ◽  
pp. 32-38
Author(s):  
Iin Ernawati
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
The Relationship

This study was conducted to text-based data mining or often called text mining, classification methods commonly used method Naïve bayes classifier (NBC) and support vector machine (SVM). This classification is emphasized for Indonesian language documents, while the relationship between documents is measured by the probability that can be proven with other classification algorithms. This evident from the conclusion that the probability result Naïve Bayes Classifier (NBC) word “party” at least in the economic document and political. Then the result of the algorithm support vector machine (svm) with the word “price” and “kpk” contains in both economic and politic document.  

scholarly journals Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

2021 ◽  
Vol 8 (2) ◽  
pp. 311
Author(s):  
Mohammad Farid Naufal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors

<p class="Abstrak">Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. <em>Computer vision</em> adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan <em>self autonomous machine</em> agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara <em>real time</em>. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari <em>Machine Learning</em> sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Weather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Ensemble swarm behaviour based feature selection and support vector machine classifier for chronic kidney disease prediction

2018 ◽  
Vol 7 (2.31) ◽  
pp. 190 ◽  
Author(s):  
S Belina V.J. Sara ◽  
K Kalaiselvi
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Kidney Disease ◽  
Information Gain ◽  
Clonal Selection ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Algorithms

Kidney Disease and kidney failure is the one of the complicated and challenging health issues regarding human health. Without having any symptoms few diseases are detected in later stages which results in dialysis. Advanced excavating technologies can always give various possibilities to deal with the situation by determining important realations and associations in drilling down health related data.   The prediction accuracy of classification algorithms depends upon appropriate Feature Selection (FS) algorithms decrease the number of features from collection of data. FS is the procedure of choosing the most relevant features, removing irrelevant features. To identify the Chronic Kidney Disease (CKD), Hybrid Wrapper and Filter based FS (HWFFS) algorithm is proposed to reduce the dimension of CKD dataset.   Filter based FS algorithm is performed based on the three major functions: Information Gain (IG), Correlation Based Feature Selection (CFS) and Consistency Based Subset Evaluation (CS) algorithms respectively. Wrapper based FS algorithm is performed based on the Enhanced Immune Clonal Selection (EICS) algorithm to choose most important features from the CKD dataset.  The results from these FS algorithms are combined with new HWFFS algorithm using classification threshold value.  Finally Support Vector Machine (SVM) based prediction algorithm be proposed in order to predict CKD and being evaluated on the MATLAB platform. The results demonstrated with the purpose of the SVM classifier by using HWFFS algorithm provides higher prediction rate in the diagnosis of CKD when compared to other classification algorithms.  

Prediction of Breast Cancer Using Machine Learning

2020 ◽  
Vol 13 (5) ◽  
pp. 901-908
Author(s):  
Somil Jain ◽  
Puneet Kumar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Breast Cancer Dataset

Background:: Breast cancer is one of the diseases which cause number of deaths ever year across the globe, early detection and diagnosis of such type of disease is a challenging task in order to reduce the number of deaths. Now a days various techniques of machine learning and data mining are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like cancer which can save the life’s of the patients suffering from such type of disease. The major concern of this study is to find the prediction accuracy of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest and to suggest the best algorithm. Objective:: The objective of this study is to assess the prediction accuracy of the classification algorithms in terms of efficiency and effectiveness. Methods: This paper provides a detailed analysis of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest in terms of their prediction accuracy by applying 10 fold cross validation technique on the Wisconsin Diagnostic Breast Cancer dataset using WEKA open source tool. Results:: The result of this study states that Support Vector Machine has achieved the highest prediction accuracy of 97.89 % with low error rate of 0.14%. Conclusion:: This paper provides a clear view over the performance of the classification algorithms in terms of their predicting ability which provides a helping hand to the medical practitioners to diagnose the chronic disease like breast cancer effectively.

scholarly journals Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines

2019 ◽  
Vol 8 (6) ◽  
pp. 3697-3706
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Kidney Disease ◽  
Support Vector ◽  
Final Conclusion ◽  
Classification Algorithms ◽  
Major Objective ◽  
Vector Machines ◽  
Improve Accuracy ◽  
Processing Techniques

Preventing Chronic Kidney Disease has become one of the most intriguing task to the healthcare society. The major objective of this paper is to deal mainly with different classification algorithms namely NaiveBayes, Multi Layer Perceptron and Support Vector Machine. The work analyzes the Chronic Kidney Disease dataset taken from the machine learning repository of UCI. Pre-processing techniques such as missing value replacement, unsupervised discretization and normalization are applied to the Chronic Kidney Disease dataset to improve accuracy. Accuracy and time are the taken as the experimental outcomes of the classification models. The final conclusion states that Support Vector Machine implements much superior than all the other classification methods.

scholarly journals Evaluación de cuatro algoritmos de clasificación de imágenes satelitales Landsat-8 y Sentinel-2 para la identificación de cobertura boscosa en paisajes altamente fragmentados en Costa Rica

2020 ◽  
pp. 37
Author(s):  
I.D. Ávila-Pérez ◽  
E. Ortiz-Malavassi ◽  
C. Soto-Montoya ◽  
Y. Vargas-Solano ◽  
H. Aguilar-Arias ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Costa Rica ◽  
Maximum Likelihood ◽  
Forest Cover ◽  
Support Vector ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Classification Methods ◽  
Global Accuracy ◽  
Sentinel 2

<p>Mapping of land use and forest cover and assessing their changes is essential in the design of strategies to manage and preserve the natural resources of a country, and remote sensing have been extensively used with this purpose. By comparing four classification algorithms and two types of satellite images, the objective of the research was to identify the type of algorithm and satellite image that allows higher global accuracy in the identification of forest cover in highly fragmented landscapes. The study included a treatment arrangement with three factors and six randomly selected blocks within the Huetar Norte Zone in Costa Rica. More accurate results were obtained for classifications based on Sentinel-2 images compared to Landsat-8 images. The best classification algorithms were Maximum Likelihood, Support Vector Machine or Neural Networks, and they yield better results than Minimum Distance Classification. There was no interaction among image type and classification methods, therefore, Sentinel-2 images can be used with any of the three best algorithms, but the best result was the combination of Sentinel-2 and Support Vector Machine. An additional factor included in the study was the image acquisition date. There were significant differences among months during which the image was acquired and an interaction between the classification algorithm and this factor was detected. The best results correspond to images obtained in April, and the lower to September, month that corresponds with the period of higher rainfall in the region studied. The higher global accuracy identifying forest cover is obtained with Sentinel-2 images from the dry season in combination with Maximum Likelihood, Support Vector Machine, and Neural Network image classification methods.</p>

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