Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

2020 ◽  
pp. 073563312096731
Author(s):  
Bowen Liu ◽  
Wanli Xing ◽  
Yifang Zeng ◽  
Yonghe Wu
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Online Courses ◽  
Learning Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Achievement Emotions ◽  
Integrative Framework ◽  
Emotional Interaction ◽  
Performance Results

Massive Open Online Courses (MOOCs) have become a popular tool for worldwide learners. However, a lack of emotional interaction and support is an important reason for learners to abandon their learning and eventually results in poor learning performance. This study applied an integrative framework of achievement emotions to uncover their holistic influence on students’ learning by analyzing more than 400,000 forum posts from 13 MOOCs. Six machine-learning models were first built to automatically identify achievement emotions, including K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines. Results showed that Random Forest performed the best with a kappa of 0.83 and an ROC_AUC of 0.97. Then, multilevel modeling with the “Stepwise Build-up” strategy was used to quantify the effect of achievement emotions on students’ academic performance. Results showed that different achievement emotions influenced students’ learning differently. These findings allow MOOC platforms and instructors to provide relevant emotional feedback to students automatically or manually, thereby improving their learning in MOOCs.

scholarly journals Klasifikasi Citra Alat Musik Tradisional dengan Metode k-Nearest Neighbor, Random Forest, dan Support Vector Machine

2019 ◽  
Vol 9 (2) ◽  
pp. 185
Author(s):  
Herry Sujaini
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor

Dalam dekade terakhir, metode non-parametrik (algoritma berbasis pembelajaran mesin) semakin banyak dipergunakan dari berbagai aplikasi berbasis pengolahan citra digital. Penelitian ini bertujuan untuk membandingkan tiga metode non-parametrik yaitu Metode k-Nearest Neighbor (kNN), Random Forest (RF), dan Support Vector Machine (SVM) terhadap klasifikasi citra alat musik tradisional di Indonesia yang populer di kalangan masyarakat yaitu : angklung, djembe, gamelan, gong, gordang, kendang, kolintang, rebana, sasando, dan serunai. Dari hasil eksperimen pengklasifikasian dengan metode kNN, RF dan SVM, metode kNN memiliki akurasi yang paling baik. Rata-rata nilai precision ketiga metode tersebut berturut-turut adalah 92,1% untuk kNN, 85,4% untuk SVM, dan 69,4% untuk RF

scholarly journals Execution Assessment of Machine Learning Algorithms for Spam Profile Detection on Instagram

2021 ◽  
Vol 10 (3) ◽  
pp. 1889-1894
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Models ◽  
K Nearest Neighbor

Witheverypassingsecondsocialnetworkcommunityisgrowingrapidly,becauseofthat,attackershaveshownkeeninterestinthesekindsofplatformsandwanttodistributemischievouscontentsontheseplatforms.Withthefocus on introducing new set of characteristics and features forcounteractivemeasures,agreatdealofstudieshasresearchedthe possibility of lessening the malicious activities on social medianetworks. This research was to highlight features for identifyingspammers on Instagram and additional features were presentedto improve the performance of different machine learning algorithms. Performance of different machine learning algorithmsnamely, Multilayer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)were evaluated on machine learning tools named, RapidMinerand WEKA. The results from this research tells us that RandomForest (RF) outperformed all other selected machine learningalgorithmsonbothselectedmachinelearningtools.OverallRandom Forest (RF) provided best results on RapidMiner. Theseresultsareusefulfortheresearcherswhoarekeentobuildmachine learning models to find out the spamming activities onsocialnetworkcommunities.

scholarly journals IDENTIFIKASI JENIS IKAN MENGGUNAKAN MODEL HYBRID DEEP LEARNING DAN ALGORITMA KLASIFIKASI

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
Author(s):  
Anifuddin Azis
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Deep Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Output

Indonesia merupakan negara dengan keanekaragaman hayati terbesar kedua di dunia setelah Brazil. Indonesia memiliki sekitar 25.000 spesies tumbuhan dan 400.000 jenis hewan dan ikan. Diperkirakan 8.500 spesies ikan hidup di perairan Indonesia atau merupakan 45% dari jumlah spesies yang ada di dunia, dengan sekitar 7.000an adalah spesies ikan laut. Untuk menentukan berapa jumlah spesies tersebut dibutuhkan suatu keahlian di bidang taksonomi. Dalam pelaksanaannya mengidentifikasi suatu jenis ikan bukanlah hal yang mudah karena memerlukan suatu metode dan peralatan tertentu, juga pustaka mengenai taksonomi. Pemrosesan video atau citra pada data ekosistem perairan yang dilakukan secara otomatis mulai dikembangkan. Dalam pengembangannya, proses deteksi dan identifikasi spesies ikan menjadi suatu tantangan dibandingkan dengan deteksi dan identifikasi pada objek yang lain. Metode deep learning yang berhasil dalam melakukan klasifikasi objek pada citra mampu untuk menganalisa data secara langsung tanpa adanya ekstraksi fitur pada data secara khusus. Sistem tersebut memiliki parameter atau bobot yang berfungsi sebagai ektraksi fitur maupun sebagai pengklasifikasi. Data yang diproses menghasilkan output yang diharapkan semirip mungkin dengan data output yang sesungguhnya.  CNN merupakan arsitektur deep learning yang mampu mereduksi dimensi pada data tanpa menghilangkan ciri atau fitur pada data tersebut. Pada penelitian ini akan dikembangkan model hybrid CNN (Convolutional Neural Networks) untuk mengekstraksi fitur dan beberapa algoritma klasifikasi untuk mengidentifikasi spesies ikan. Algoritma klasifikasi yang digunakan pada penelitian ini adalah : Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN),  Random Forest, Backpropagation.

scholarly journals Prediction Of Plastic Degrading Microbes

2021 ◽  
Author(s):  
Hemalatha N ◽  
Akhil Wilson ◽  
Akhil Thankachan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Random Forest Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Plastic Pollution ◽  
Forest Model

Plastic pollution is one of the challenging problems in the environment. But a life without plastic we cannot imagine. This paper deals with the prediction of plastic degrading microbes using Machine Learning. Here we have used Decision Tree, Random Forest, Support vector Machine and K Nearest Neighbor algorithms in order to predict the plastic degrading microbes. Among the four classifiers, Random Forest model gave the best accuracy of 99.1%.

scholarly journals Predicting Stock Exchange using Supervised Learning Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 4081-4090
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Stock Market ◽  
Supervised Learning ◽  
Nearest Neighbor ◽  
Market Price ◽  
Real Life ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Future Value

The stock market price trend is one of the brightest areas in the field of computer science, economics, finance, administration, etc. The stock market forecast is an attempt to determine the future value of the equity traded on a financial transaction with another financial system. The current work clearly describes the prediction of a stock using Machine Learning. The adoption of machine learning and artificial intelligence techniques to predict the prices of the stock is a growing trend. More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model. This paper is mainly concerned with the best model to predict the stock market value. During the mechanism of contemplating the various techniques and variables that can be taken into consideration, we discovered five models Which are based on supervised learning techniques i.e.., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Bernoulli Naïve Bayes.The empirical results show that SVC performs the best for large datasets and Random Forest, Naïve Bayes is the best for small datasets. The successful prediction for the stock will be a great asset for the stock The stock market price trend is one of the brightest areas in the field of computer science, economics, finance, administration, etc. The stock market forecast is an attempt to determine the future value of the equity traded on a financial transaction with another financial system. The current work clearly describes the prediction of a stock using Machine Learning. The adoption of machine learning and artificial intelligence techniques to predict the prices of the stock is a growing trend. More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model. This paper is mainly concerned with the best model to predict the stock market value. During the mechanism of contemplating the various techniques and variables that can be taken into consideration, we discovered five models Which are based on supervised learning techniques i.e.., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Bernoulli Naïve Bayes.The empirical results show that SVC performs the best for large datasets and Random Forest, Naïve Bayes is the best for small datasets. The successful prediction for the stock will be a great asset for the stock market institutions and will provide real-life solutions to the problems that stock investors face.market institutions and will provide real-life solutions to the problems that stock investors face.

scholarly journals Klasifikasi Jenis Pemeliharaan dan Perawatan Container Crane menggunakan Algoritma Machine Learning

MATICS ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 21-27
Author(s):  
Via Ardianto Nugroho ◽  
Derry Pramono Adi ◽  
Achmad Teguh Wibowo ◽  
MY Teguh Sulistyono ◽  
Agustinus Bimo Gumelar
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Container Crane ◽  
Model Tree

Pada industri jasa pelayanan peti kemas, Terminal Nilam merupakan pelanggan dari PT. BIMA, yang secara khusus bergerak dibidang jasa perbaikan dan perawatan alat berat. Terminal ini menjadi sentral tempat untuk melakukan aktifitas bongkar muat peti kemas domestik yang memiliki empat buah container crane untuk melayani dua kapal. Proses perawatan alat berat seperti container crane yang selama ini beroperasi, agaknya kurang memperhatikan data pengelompokkan atau klasifikasi jenis perawatan yang dibutuhkan oleh alat berat tersebut. Di kemudian hari, alat berat dapat menunjukkan kinerja yang tidak maksimal bahkan dapat berujung pada kecelakaan kerja. Selain itu, kelalaian perawatan container crane juga dapat menyebabkan pembengkakan biaya perawatan lanjut. Target produksi bongkar muat dapat berkurang dan juga keterlambatan jadwal kapal sandar sangat mungkin terjadi. Metode pembelajaran menggunakan mesin atau biasa disebut dengan Machine Learning (ML), dengan mudah dapat melenyapkan kemungkinan-kemungkinan tersebut. ML dalam penelitian ini, kami rancang agar bekerja dengan mengidentifikasi lalu mengelompokkan jenis perawatan container crane yang sesuai, yaitu ringan atau berat. Metode ML yang pilih untuk digunakan dalam penelitian ini yaitu Random Forest, Support Vector Machine, k-Nearest Neighbor, Naïve Bayes, Logistic Regression, J48, dan Decision Tree. Penelitian ini menunjukkan keberhasilan ML model tree dalam melakukan pembelajaran jenis data perawatan container crane (numerik dan kategoris), dengan J48 menunjukkan performa terbaik dengan nilai akurasi dan nilai ROC-AUC mencapai 99,1%. Pertimbangan klasifikasi kami lakukan dengan mengacu kepada tanggal terakhir perawatan, hour meter, breakdown, shutdown, dan sparepart.

scholarly journals A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform

2021 ◽  
Vol 11 (21) ◽  
pp. 9927
Author(s):  
Qiuying Chen ◽  
SangJoon Lee
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Important Variable ◽  
Receiver Operating Curve ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Machine Learning Approach ◽  
Supervised Learning Algorithms

Health authorities have recommended the use of digital tools for home workouts to stay active and healthy during the COVID-19 pandemic. In this paper, a machine learning approach is proposed to assess the activity of users on a home workout platform. Keep is a home workout application dedicated to providing one-stop exercise solutions such as fitness teaching, cycling, running, yoga, and fitness diet guidance. We used a data crawler to collect the total training set data of 7734 Keep users and compared four supervised learning algorithms: support vector machine, k-nearest neighbor, random forest, and logistic regression. The receiver operating curve analysis indicated that the overall discrimination verification power of random forest was better than that of the other three models. The random forest model was used to classify 850 test samples, and a correct rate of 88% was obtained. This approach can predict the continuous usage of users after installing the home workout application. We considered 18 variables on Keep that were expected to affect the determination of continuous participation. Keep certification is the most important variable that affected the results of this study. Keep certification refers to someone who has verified their identity information and can, therefore, obtain the Keep certification logo. The results show that the platform still needs to be improved in terms of real identity privacy information and other aspects.

scholarly journals Leveraging Machine Learning Algorithms For Zero-Day Ransomware Attack

2019 ◽  
Vol 8 (6) ◽  
pp. 4104-4107
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Supervised Learning Algorithms ◽  
Microsoft Windows

Current global huge cyber protection attacks resulting from Infected Encryption ransomware structures over all international locations and businesses with millions of greenbacks lost in paying compulsion abundance. This type of malware encrypts consumer files, extracts consumer files, and charges higher ransoms to be paid for decryption of keys. An attacker could use different types of ransomware approach to steal a victim's files. Some of ransomware attacks like Scareware, Mobile ransomware, WannaCry, CryptoLocker, Zero-Day ransomware attack etc. A zero-day vulnerability is a software program security flaw this is regarded to the software seller however doesn’t have patch in vicinity to restore a flaw. Despite the fact that machine learning algorithms are already used to find encryption Ransomware. This is based on the analysis of a large number of PE file data Samples (benign software and ransomware utility) makes use of supervised machine learning algorithms for ascertain Zero-day attacks. This work was done on a Microsoft Windows operating system (the most attacked os through encryption ransomware) and estimated it. We have used four Supervised learning Algorithms, Random Forest Classifier , K-Nearest Neighbor, Support Vector Machine and Logistic Regression. Tests using machine learning algorithms evaluate almost null false positives with a 99.5% accuracy with a random forest algorithm.

scholarly journals Automatic Vowel Recognition from Assamese Spoken words

2019 ◽  
Vol 8 (10) ◽  
pp. 2297-2304
Keyword(s):  
Random Forest ◽  
Speech Processing ◽  
Nearest Neighbor ◽  
Research Work ◽  
Recognition System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
North East ◽  
The North ◽  
Vowel Recognition

Vowel plays the most important role in any speech processing work. In this research work, recognition of Assamese vowel from spoken Assamese words is explored. Assamese is a language which is spoken by major people in Brahmaputra Valley of Assam, Assam is a state which is situated in the North-East part of India. This automatic vowel recognition system is implemented by using three efficient techniques Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) classifier. The database used in the experiments is specially designed for this purpose. A list of phonetically vowel rich Assamese words is prepared for the experiment. As an initial effort, twenty different (20) words uttered by fifty-five (55) speakers are taken. Utterances from both male and female speakers are collected. Each utterance was repeated two times by every speaker. A database of the total of 2200 samples is prepared. After experimenting on different samples it is seen that Random Forest (RF) is giving the best performance compared to the other two classifiers. The performance of the system is shown with testing dataset and comparison is done. Outcome of this research work will enhance the Machine Translation from Assamese to any other language.

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