scholarly journals Construction of Music Teaching Evaluation Model Based on Weighted Naïve Bayes

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiongjun Xia ◽  
Jin Yan
Keyword(s):  
Evaluation System ◽  
Naive Bayes ◽  
Evaluation Model ◽  
Audio Signal ◽  
Naïve Bayes ◽  
Teaching Evaluation ◽  
Machine Learning Algorithms ◽  
Music Teaching ◽  
Model Based ◽  
Bayes Algorithm

Evaluation of music teaching is a highly subjective task often depending upon experts to assess both the technical and artistic characteristics of performance from the audio signal. This article explores the task of building computational models for evaluating music teaching using machine learning algorithms. As one of the widely used methods to build classifiers, the Naïve Bayes algorithm has become one of the most popular music teaching evaluation methods because of its strong prior knowledge, learning features, and high classification performance. In this article, we propose a music teaching evaluation model based on the weighted Naïve Bayes algorithm. Moreover, a weighted Bayesian classification incremental learning approach is employed to improve the efficiency of the music teaching evaluation system. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in the context of music teaching evaluation.

scholarly journals Prediction of Heart Disease Using Machine Learning Algorithms

2018 ◽  
Vol 7 (2.32) ◽  
pp. 363 ◽  
Author(s):  
N Rajesh ◽  
Maneesha T ◽  
Shaik Hafeez ◽  
Hari Krishna
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Decision Trees ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Bayes Algorithm ◽  
The One

Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.  

scholarly journals Effectiveness of Classification Methods on the Diabetes System

2021 ◽  
pp. 33-43
Author(s):  
Ahmed T. Shawky ◽  
Ismail M. Hagag
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Early Stage ◽  
Research Paper ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Bayes Algorithm

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

Smart teaching evaluation model using weighted naive bayes algorithm

2020 ◽  
pp. 1-11
Author(s):  
Lin Liu
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Evaluation Model ◽  
Evaluation Process ◽  
Teaching Quality ◽  
Teaching Evaluation ◽  
Current Status ◽  
Basic Algorithm ◽  
Model Training ◽  
Bayes Algorithm

There is a certain subjectivity in the teaching evaluation process, which leads to a low accuracy of the intelligent scoring system. In order to promote the intelligent development of teaching evaluation, based on machine learning, this study briefly introduces the background and current status of teaching evaluation, and describes in detail the relevant algorithm principles of data analysis and modeling using data mining technology and machine learning methods. Moreover, this study describes the establishment process of the traditional classroom teaching evaluation system and uses the classification algorithm in machine learning in the construction of evaluation models to further improve the scientificity and feasibility of teaching evaluation. In addition, in this study, empirical algorithm is used as the basic algorithm to evaluate teaching quality, and the topic word distribution obtained by joint model training is used as the original knowledge. Finally, this research analyzes the performance of this research system through a control experiment. The research results show that the scores of the research model are close to the standard manual scores and can provide a theoretical reference for subsequent related research.

scholarly journals Hadoop based Parallel Machine Learning Algorithms for Intrusion Detection System

2019 ◽  
Vol 9 (1) ◽  
pp. 1152-1156
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Naive Bayes ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
True Positive ◽  
Positive Rate ◽  
Bayes Algorithm

Web use and digitized information are getting expanded each day. The measure of information created is likewise getting expanded. On the opposite side, the security assaults cause numerous security dangers in the system, sites and Internet. Interruption discovery in a fast system is extremely a hard undertaking. The Hadoop Implementation is utilized to address the previously mentioned test that is distinguishing interruption in a major information condition at constant. To characterize the strange bundle stream, AI methodologies are used. Innocent Bayes does grouping by a vector of highlight esteems produced using some limited set. Choice Tree is another Machine Learning classifier which is likewise an administered learning model. Choice tree is the stream diagram like tree structure. J48 and Naïve Bayes Algorithm are actualized in Hadoop MapReduce Framework for parallel preparing by utilizing the KDDCup Data Corrected Benchmark dataset records. The outcome acquired is 89.9% True Positive rate and 0.04% False Positive rate for Naive Bayes Algorithm and 98.06% True Positive rate and 0.001% False Positive rate for Decision Tree Algorithm.

scholarly journals Penerapan Metode Machine Learning Dalam Memprediksi Keberhasilan Panggilan Telemarketing Menjual Produk Bank

2020 ◽  
Vol 6 (2) ◽  
pp. 213-222
Author(s):  
Ahmad Fauzi ◽  
Fanny Fatma Wati ◽  
Indah Sulistyowati ◽  
Muhammad Faittullah Akbar ◽  
Eka Rahmawati ◽  
...  
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Long Distance ◽  
Customer Data ◽  
Backward Elimination ◽  
Marketing Activities ◽  
Distance Communication ◽  
Bayes Algorithm

Abstract: Competition between banks can be seen from the various attempts by banks to find customers through various marketing activities in order to get as many customers as possible. In the past, business actors offered goods or services to consumers in a face-to-face manner, now by utilizing existing and sophisticated technology, they can use long-distance communication tools such as telephone and fax, as well as other electronic media. To make it easier to manage customer data, a data calcification is needed. Machine Learning Algorithms can be used to predict or classify data. One of the algorithms in Machine Learning is the Naive Bayes method. Naive Bayes is a simple probabilistic classification that calculates a set of probabilities by summing the frequency and value combinations from a given dataset. This research will predict a successful Telemarketing call in selling Bank products to customers. The Naive Bayes algorithm and the Backward Elimination feature selection can increase the accuracy value in predicting the success of telemarketing in selling bank products well, as evidenced by the accuracy value generated by Naive Bayes of 83.04%, then after being applied with the selection of the backward elimination feature it increases by 6.41. % to 89.45%. Keywords: Telemarketing, Machine Learning, Naive Bayes Abstrak: Persaingan antar bank dapat dilihat dari berbagai upaya bank dalam mencari nasabah dengan berbagai kegiatan pemasaran agar mendapat nasabah sebanyak-banyaknya. Dahulu para pelaku usaha menawarkan barang atau jasa kepada konsumen dengan cara bertatap muka langsung, sekarang dengan memanfaatkan teknologi yang ada dan canggih bisa menggunakan alat komunikasi jarak jauh seperti telepon dan fax, serta media elektronik lainnya. Untuk mempermudah mengelola data nasabah maka dibutuhkan sebuah pengkalsifikasian data. Algoritma Machine Learning dapat digunakan dalam memprediksi atau mengklasifikasikan sebuah data. Salah satu algoritma dalam Machine Learning adalah metode Naive Bayes. Naive Bayes merupakan sebuah pengklasifikasian probabilistik sederhana yang menghitung sekumpulan probabilitas dengan menjumlahkan frekuensi dan kombinasi nilai dari dataset yang diberikan. Pada penelitian ini akan memprediksi sebuah keberhasilan panggilan Telemarketing dalam menjula produk Bank kepada para nasabah. Algoritma Naive Bayes dan seleksi fitur Backward Elimination mampu meningkatkan nilai akurasi dalam memprediksi keberhasilan telemarketing dalam menjual produk bank dengan baik, dibuktikan dengan nilai akurasi yang dihasilkan naive bayes sebesar 83,04 %, kemudian setelah diterapkan dengan seleksi fitur backward elimination meningkat sebesa 6,41% menjadi 89,45%. Kata kunci: Telemarketing, Machine Learning, Naive Bayes

scholarly journals Identifying the User As Genuine/Malign Based on Search Logs and Search History

2020 ◽  
Vol 9 (1) ◽  
pp. 2046-2048
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Main Research ◽  
Security Issues ◽  
Specific Category ◽  
System Logs ◽  
Bayes Algorithm ◽  
Search Logs

-One of the major challenges a developer may face is security issues/threats on the labelled data. The labelled data comprises of system logs, network traffic or any other enriched data with threat/not threat classification. . There were few studies which categorized the URLs to a specific category like Arts, Technology, etc. In this paper the main research is on the classification of users based on the search logs(URLs). Manually it is difficult to differentiate the user based on search logs. So, we train a machine learning model that takes raw data as input and classifies the user to genuine or malign. This model helps in intrusion detection/suspicious activity detection. For this first we gather data of past malicious URLS as training set for Naïve Bayes algorithm to detect the malicious users. By implementing KNN algorithm effectively we can detect the malign users up to an accuracy of 94.28%. With the help of Machine Learning algorithms like Naïve Bayes, KNN, Random Forest classifiers we can classify the malign and genuine users.

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