scholarly journals Detection of Anxiety Expression From EEG Analysis Using Support Vector Machine

2019 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Kou Yamada ◽  
Wan Junaidee bin Wan Hamat ◽  
Harris Majdi bin Ishak ◽  
Kotaro Hashikura ◽  
Takaaki Suzuki
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Learning Communities ◽  
Ranking Function ◽  
Support Vector ◽  
Eeg Analysis ◽  
Nonlinear Functions ◽  
Kernel Trick ◽  
Efficient Learning

Support Vector Machine (SVMs) have been extensively researched in data mining and machine learning communities for the last decade and actively applied to application in various domains. SVMs are typically used for learning classification, regression and ranking function. Two specials properties of SVMs are that SVMs achieve high generalization by maximizing the margin and support an efficient learning of nonlinear functions by kernel trick. In this paper, we present how to clarify when we feel anxiety by using SVM technique to estimate the condition of user.

scholarly journals Integration of synthetic minority oversampling technique for imbalanced class

2019 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Noviyanti Santoso ◽  
Wahyu Wibowo ◽  
Hilda Hikmawati
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Class Imbalance ◽  
Original Data ◽  
Support Vector ◽  
Classification Methods ◽  
Problematic Issue ◽  
Imbalanced Class ◽  
F Measure

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.

scholarly journals Design and analysis of an efficient machine learning based hybrid recommendation system with enhanced density-based spatial clustering for digital e-learning applications

2021 ◽  
Author(s):  
S. Bhaskaran ◽  
Raja Marappan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Support Vector Machine ◽  
Absolute Error ◽  
Support Vector ◽  
E Learning ◽  
Public Datasets ◽  
Hybrid Recommender ◽  
New Strategies

AbstractA decision-making system is one of the most important tools in data mining. The data mining field has become a forum where it is necessary to utilize users' interactions, decision-making processes and overall experience. Nowadays, e-learning is indeed a progressive method to provide online education in long-lasting terms, contrasting to the customary head-to-head process of educating with culture. Through e-learning, an ever-increasing number of learners have profited from different programs. Notwithstanding, the highly assorted variety of the students on the internet presents new difficulties to the conservative one-estimate fit-all learning systems, in which a solitary arrangement of learning assets is specified to the learners. The problems and limitations in well-known recommender systems are much variations in the expected absolute error, consuming more query processing time, and providing less accuracy in the final recommendation. The main objectives of this research are the design and analysis of a new transductive support vector machine-based hybrid personalized hybrid recommender for the machine learning public data sets. The learning experience has been achieved through the habits of the learners. This research designs some of the new strategies that are experimented with to improve the performance of a hybrid recommender. The modified one-source denoising approach is designed to preprocess the learner dataset. The modified anarchic society optimization strategy is designed to improve the performance measurements. The enhanced and generalized sequential pattern strategy is proposed to mine the sequential pattern of learners. The enhanced transductive support vector machine is developed to evaluate the extracted habits and interests. These new strategies analyze the confidential rate of learners and provide the best recommendation to the learners. The proposed generalized model is simulated on public datasets for machine learning such as movies, music, books, food, merchandise, healthcare, dating, scholarly paper, and open university learning recommendation. The experimental analysis concludes that the enhanced clustering strategy discovers clusters that are based on random size. The proposed recommendation strategies achieve better significant performance over the methods in terms of expected absolute error, accuracy, ranking score, recall, and precision measurements. The accuracy of the proposed datasets lies between 82 and 98%. The MAE metric lies between 5 and 19.2% for the simulated public datasets. The simulation results prove the proposed generalized recommender has a great strength to improve the quality and performance.

scholarly journals Machine Learning in Apache Spark Environment for Diagnosis of Diabetes

2021 ◽  
Author(s):  
Farshid Bagheri Saravi ◽  
Shadi Moghanian ◽  
Giti Javidi ◽  
Ehsan O Sheybani
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Big Data ◽  
Random Forest ◽  
Apache Spark ◽  
Support Vector ◽  
Computing Environment ◽  
Large Dataset ◽  
Related Data

Disease-related data and information collected by physicians, patients, and researchers seem insignificant at first glance. Still, the same unorganized data contain valuable information that is often hidden. The task of data mining techniques is to extract patterns to classify the data accurately. One of the various Data mining and its methods have been used often to diagnose various diseases. In this study, a machine learning (ML) technique based on distributed computing in the Apache Spark computing space is used to diagnose diabetics or hidden pattern of the illness to detect the disease using a large dataset in real-time. Implementation results of three ML techniques of Decision Tree (DT) technique or Random Forest (RF) or Support Vector Machine (SVM) in the Apache Spark computing environment using the Scala programming language and WEKA show that RF is more efficient and faster to diagnose diabetes in big data.

scholarly journals Klasifikasi Pengembalian Radar dari Ionosfer Menggunakan SVM, Naïve Bayes dan Random Forest

2021 ◽  
Vol 10 (2) ◽  
pp. 111-117
Author(s):  
Yulia Aryani ◽  
Arie Wahyu Wijayanto
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Random Forest ◽  
Support Vector ◽  
Ve Bayes

ABSTRAK – Klasifikasi merupakan salah satu topik utama dalam data mining atau machine learning. Klasifikasi adalah suatu pengelompokan data dimana data yang digunakan tersebut mempunyai kelas label atau target. Klasifikasi digunakan untuk mengambil data dan ditempatkan kedalam kelompok tertentu.  Studi tentang ionosfer penting untuk penelitian di berbagai domain, khususnya dalam sistem komunikasi.  Dalam penelitian ionosfer, perlu dilakukan klasifikasi radar yang berguna dan tidak berguna dari ionosfer. Pada makalah ini, akan dilakukan klasifikasi  terhadap data inosphere yang diambil dari UCI machine learning repository.  Klasifikasi dilakukan dengan menggunakan tiga metode klasifikasi, yakni  SVM ( Support Vector Machine ) , Naïve Bayes, dan Random Forest. Hasil dari percobaan ini bisa menunjukkan prediksi dari setiap percobaan dengan tingkat akurasi dan prediksi yang berbeda-beda di setiap metode yang digunakan. Hasil akurasi, presisi, dan recall terbaik didapatkan pada metode Random Forest dengan rasio data latih dan data uji sebesar 85% didapat akurasi dari data uji sebesar 90,57% dengan presisi sebesar 94,12%. Kata Kunci – Ionosfer; Klasifikasi; SVM; Naïve Bayes; Random Forest.

A Business Classifier to Detect Readability Metrics on Software Games and Their Types

2013 ◽  
Vol 4 (4) ◽  
pp. 47-57
Author(s):  
Yahya M. Tashtoush ◽  
Derar Darwish ◽  
Motasim Albdarneh ◽  
Izzat M. Alsmadi ◽  
Khalid Alkhatib
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Software Metrics ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mobile Environments ◽  
New Approach ◽  
Data Mining Tool ◽  
Mining Tool

Readability metric is considered to be one of the most important factors that may affect games business in terms of evaluating games' quality in general and usability in particular. As games may go through many evolutions and developed by many developers, code readability can significantly impact the time and resources required to build, update or maintain such games. This paper introduces a new approach to detect readability for games built in Java or C++ for desktop and mobile environments. Based on data mining techniques, an approach for predicting the type of the game is proposed based on readability and some other software metrics or attributes. Another classifier is built to predict software readability in games applications based on several collected features. These classifiers are built using machine learning algorithms (J48 decision tree, support vector machine, SVM and Naive Bayes, NB) that are available in WEKA data mining tool.

Penerapan Teknik Data Mining dengan Metode Support Vector Machine (SVM) untuk Memprediksi Siswa yang Berpeluang Drop Out (Studi Kasus di SMKN 1 Sutera)

2017 ◽  
Vol 3 (1) ◽  
pp. 15
Author(s):  
Ryci Rahmatil Fiska
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Drop Out ◽  
Support Vector ◽  
Kernel Trick

Drop out adalah suatu keadaan di mana siswa diberhentikan dari sekolah karena beberapa alasan yang mengharuskan siswa untuk di drop out . faktor drop out seperti absen yang telah melebihi batas maksimal, nilai yang kurang dari batas bawah yang telah ditentukan pihak sekolah dan prilaku siswa yang sudah melanggar nilai attitude yang telah diterapkan oleh pihak sekolah. Dalam penelitian ini penulis ingin memprediksi siswa yang berpeluang drop out dengan menggunakan metode SVM dan diharapkan mampu memprediksi masalah drop out apakah siswa akan lanjut ke semester selanjutnya atau di drop out karena beberapa alasan. Dan hasil yang disimpulkan adalah metode SVM mampu memprediksi siswa yang berpeluang drop out.Kata kunci : Support Vector Machine, Kernel Trick, Drop Out.

scholarly journals Simple quantum circuit for pattern recognition based on nearest mean classifier

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Gharib M Subhi ◽  
Azeddine Messikh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Quantum Circuit ◽  
Support Vector ◽  
Bloch Sphere ◽  
Optimal Hyperplane ◽  
Simple Quantum ◽  
Quantum Matrix

Machine learning plays a key role in many applications such as data mining and image recognition.Classification is one subcategory under machine learning. In this paper we propose a simple quantum circuitbased on the nearest mean classifier to classified handwriting characters. Our circuit is a simplified circuit fromthe quantum support vector machine [Phys. Rev. Lett. 114, 140504 (2015)] which uses quantum matrix inversealgorithm to find optimal hyperplane that separated two different classes. In our case the hyperplane is foundusing projections and rotations on the Bloch sphere.

Support Vector Machines Illuminated

2011 ◽  
pp. 1071-1076 ◽  
Author(s):  
David R. Musicant
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Data Storage ◽  
Research Data ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Vector Machines ◽  
A Current

In recent years, massive quantities of business and research data have been collected and stored, partly due to the plummeting cost of data storage. Much interest has therefore arisen in how to mine this data to provide useful information. Data mining as a discipline shares much in common with machine learning and statistics, as all of these endeavors aim to make predictions about data as well as to better understand the patterns that can be found in a particular dataset. The support vector machine (SVM) is a current machine learning technique that performs quite well in solving common data mining problems.

scholarly journals Threshold based Support Vector Machine Learning Algorithm for Sequential Patterns

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
S Imavathy ◽  
M. Chinnadurai
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Pattern Mining ◽  
Research Work ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Support Vector ◽  
Positive Rate

Now a days the pattern recognition is the major challenge in the field of data mining. The researchers focus on using data mining for wide variety of applications like market basket analysis, advertisement, and medical field etc., Here the transcriptional database is used for all the conventional algorithms, which is based on daily usage of object and/or performance of patients. Here the proposed research work uses sequential pattern mining approach using classification technique of Threshold based Support Vector Machine learning (T-SVM) algorithm. The pattern mining is to give the variable according to the user’s interest by statistical model. Here this proposed research work is used to analysis the gene sequence datasets. Further, the T-SVM technique is used to classify the dataset based on sequential pattern mining approach. Especially, the threshold-based model is used for predicting the upcoming state of interest by sequential patterns. Because this makes deeper understanding about sequential input data and classify the result by providing threshold values. Therefore, the proposed method is efficient than the conventional method by getting the value of achievable classification accuracy, precision, False Positive rate, True Positive rate and it also reduces operating time. This proposed model is performed in MATLAB in the adaptation of 2018a.

Analysis of Heart Disorder by Using Machine Learning Methods and Data Mining Techniques

2021 ◽  
pp. 212-221
Author(s):  
Sarangam Kodati ◽  
Jeeva Selvaraj
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Heart Diseases ◽  
Support Vector ◽  
Primary Reason ◽  
The World ◽  
Mining Methods ◽  
Using Data

Data mining is the most famous knowledge extraction approach for knowledge discovery from data (KDD). Machine learning is used to enable a program to analyze data, recognize correlations, and make usage on insights to solve issues and/or enrich data and because of prediction. The chapter highlights the need for more research within the usage of robust data mining methods in imitation of help healthcare specialists between the diagnosis regarding heart diseases and other debilitating disease conditions. Heart disease is the primary reason of death of people in the world. Nearly 47% of death is caused by heart disease. The authors use algorithms including random forest, naïve Bayes, support vector machine to analyze heart disease. Accuracy on the prediction stage is high when using a greater number of attributes. The goal is to function predictive evaluation using data mining, using data mining to analyze heart disease, and show which methods are effective and efficient.

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