scholarly journals Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers

2021 ◽  
Vol 6 (9) ◽  
pp. 132
Author(s):  
Faezeh Jafari ◽  
Sattar Dorafshan
Keyword(s):  
Statistical Analysis ◽  
Naive Bayes ◽  
Peak Frequency ◽  
Naïve Bayes ◽  
Frequency Method ◽  
Bridge Inspection ◽  
Impact Echo ◽  
Sensing Technology ◽  
Defect Area ◽  
Defect Recognition

Interpretation of IE data have been carried out by analyzing IE signals in frequency domain to determine the maximum frequency. However, the current peak frequency method can be inaccurate. The purpose of this research is to introduce features in IE signals that can be used for effective classification and interpretation for bridge deck evaluation through statistical analysis and Naive Bayes classifiers. The dataset contained IE data collected from eight slabs created at Advanced Sensing Technology FAST NDE laboratory (FHWA). A set of statistical features in time domain, normalized peak values, and length of preprocessed signals were used to classify the IE data, statistically. Then, Naive Bayes classifiers was employed to recognize defect area. Finally, the result of statistical classification was compared with frequency approach. The result shows that 19 and 21% of the IE signals collected from the defect area have multiple peaks, respectively. However, 85% of the IE signals collected from the sound set had only one peak. A probability classifier was used to find the relationship between the result of the frequency method and statistical analysis. The result shows that 10% of the IE signals were usable for estimating the thickness in the sound group.

scholarly journals Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes

2021 ◽  
Vol 8 (10) ◽  
pp. 138
Author(s):  
Giovanni D’Addio ◽  
Leandro Donisi ◽  
Giuseppe Cesarelli ◽  
Federica Amitrano ◽  
Armando Coccia ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Statistical Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Receiver Operating Curve ◽  
Poincaré Plot ◽  
Nyha Classification

Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF). Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. In this paper, we explored the possibility to feed machine-learning (ML) algorithms using unconventional quantitative parameters extracted from Poincaré plots (generated from 24-h electrocardiogram recordings) to classify patients with CHF belonging to different New York Heart Association (NYHA) classes. We performed in sequence the following investigations: first, a statistical analysis was carried out on 9 morphological parameters, automatically measured from Poincaré plots. Subsequently, a feature selection through a wrapper with a 10-fold cross-validation method was performed to find the best subset of features which maximized the classification accuracy for each considered ML algorithm. Finally, patient classification was assessed through a ML analysis using AdaBoost of Decision Tree, k-Nearest Neighbors and Naive Bayes algorithms. A univariate statistical analysis proved 5 out of 9 parameters presented statistically significant differences among patients of distinct NYHA classes; similarly, a multivariate logistic regression confirmed the importance of the parameter ρy in the separability between low-risk and high-risk classes. The ML analysis achieved promising results in terms of evaluation metrics (especially the Naive Bayes algorithm), with accuracies greater than 80% and Area Under the Receiver Operating Curve indices greater than 0.7 for the overall three algorithms. The study indicates the proposed features have a predictive power to discriminate the NYHA classes, to which the features seem evenly correlated. Despite the NYHA classification being subjective and easily recognized by cardiologists, the potential relevance in the clinical cardiology of the proposed features and the promising ML results implies the methodology could be a valuable approach to automatically classify CHF. Future investigations on enriched datasets may further confirm the presented evidence.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

2019 ◽  
Vol 64 (2) ◽  
pp. 53-71
Author(s):  
Botond Benedek ◽  
Ede László
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Naive Bayes ◽  
Insurance Industry ◽  
Naïve Bayes ◽  
Sql Server ◽  
Categorical Variables ◽  
Automobile Insurance ◽  
Price Determination ◽  
Mining Tool

Abstract Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.

PREDIKSI JURUSAN PADA SELEKSI NASIONAL MASUK PERGURUAN TINGGI NEGERI (SNMPTN) MENGGUNAKAN METODE KLASIFIKASI NAÏVE BAYES

2020 ◽  
Vol 4 (1) ◽  
pp. 55
Author(s):  
Herliyani Hasanah ◽  
Nugroho Arif Sudibyo ◽  
Edy Kurniawan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bahasa Indonesia

SNMPTN merupakan salah satu jalur masuk perguruan tinggi negeri yang banyak diminati siswa karena hanya menggunakan parameter nilai raport, prestasi siswa, dan prestasi sekolah. Setiap jurusan mempunyai nilai diterima minimal yang berbeda-beda, besaran kuota yang ditetapkan LTMP 2019 minimal hanya 20% dari daya tampung program studi di setiap perguruan tinggi negeri. Besarnya minat siswa dan kecilnya jumlah kuota tidak sebanding sehingga menyebabkan persaingan diterima pada jalur ini semakin ketat. Namun, masih banyak siswa yang belum mempertimbangkan parameter tersebut saat mendaftar sehingga kemungkinan diterima pada jalur SNMPTN semakin kecil. Oleh karena itu diperlukan sebuah sistem yang dapat memprediksi kemungkinan diterimanya siswa pada jurusan SNMPTN berdasarkan atribut yang sudah ditentukan. <em>Naïve Bayes</em> diterapkan untuk mencari nilai probabilitas terbesar dalam setiap variabel yang ada. Variabel yang digunakan meliputi nilai rata-rata matematika, bahasa indonesia, dan bahasa inggris semester 1 sampai 5 serta prestasi siswa yang dilampirkan saat mendaftar dan prestasi sekolah. Hasilnya dengan <em>naïve bayes</em> mampu menghasilkan akurasi sebesar 83,3%.

Otomatisasi Peringkasan Dokumen Sebagai Pendukung Sistem Manajemen Surat

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Ahmad Najibullah ◽  
Wang Mingyan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes

Peringkasan dokumen adalah proses penyajian kembali dokumen dalam bentuk yang lebih singkat tanpa membuang informasi penting yang terdapat dalam dokumen tersebut. Dalam penelitian ini, peneliti menggunakan metode Naive Bayes untuk menghasilkan ringkasan sebuah dokumen. Objek dalam penelitian ini berupa dokumen yang berbentuk surat. Dalam proses peringkasan dokumen, penghitungan probabilitas didasarkan pada fitur teks yang ada dalam surat, diantaranya adalah frekuensi kata, kata kunci, frase kunci, dan kata yang termasuk dalam kelas entitas atau numerik. Hasil uji coba menunjukkan bahwa tingkat kompresi adalah 53.67% dengan informasi penting yang tersedia dalam ringkasan mencapai 96.67% dari dokumen asli.

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