scholarly journals Design of a low cost and novel Naive Bayes classifier based NodeMCU web server for fever type detection

2018 ◽  
Vol 29 (12) ◽  
Author(s):  
Partha Pratim Ray ◽  
Dinesh Dash ◽  
Debashis De
Keyword(s):  
Naive Bayes ◽  
Low Cost ◽  
Web Server ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

IMPLEMENTASI LEXICON BASED DAN NAIVE BAYES PADA ANALISIS SENTIMEN PENGGUNA TWITTER TOPIK PEMILIHAN PRESIDEN 2019

2019 ◽  
Vol 24 (2) ◽  
pp. 140-153
Author(s):  
Gusti Nur Aulia ◽  
Eka Patriya
Keyword(s):  
Naive Bayes ◽  
Confusion Matrix ◽  
Web Server ◽  
Data Preprocessing ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Data Filtering ◽  
Naïve Bayes Classifier ◽  
Twitter Data

Pilpres saat ini cukup menyita perhatian, karena berbagai rumor yang beredar. Masyarakat juga menjadi sasaran elit politik, dimana suara mereka merupakan penentu keberlangsungan arah politik untuk lima tahun kedepan. Opini-opini positif, netral maupun negatif dapat menimbulkan ancaman munculnya berita bohong (hoax). Salah satu sarana yang digunakan masyarakat dalam mengekspresikan pilihan politiknya adalah melalui media sosial salah satunya twitter. Data seperti opini publik dapat diolah menjadi sebuah informasi yang bermanfaat, salah satunya melalui analisis sentimen. Pada penelitian ini, akan dilakukan analisis sentimen pada Twitter tentang pemilihan presiden 2019. Tahapan analisis sentimen pada penelitian ini terdiri dari akuisisi data, pre-processing, klasifikasi data, evaluasi data dan visualisasi data. Preprocessing dilakukan dengan case folding, normalisasi data, filtering, ubah kata baku, stopword dan stemming. Penelitian ini melakukan 2 metode yaitu dengan metode Lexicon Based dan Naïve Bayes Classifier. Hasil akhir dari analisis kemudian dihitung nilai akurasi menggunakan confusion matrix dan di visualisasikan menggunakan web server. Penentuan sentimen prediksi dilakukan menggunakan metode Lexicon Based dan Labelisasi dengan perhitungan secara manual. Data latih dan data uji akan digunakan dalam proses pelatihan dan pengujian menggunakan Naive Bayes Classifier. Hasil klasifikasi yang dilakukan oleh metode Naive Bayes Classifier disebut sentimen aktual. Perhitungan tingkat keakurasian antara sentimen prediksi terhadap sentimen aktual menggunakan pengujian confusion matrix. Hasil yang didapatkan adalah tingkat akurasi antara sentimen prediksi dan sentimen aktual dengan Lexicon Based sebesar 64,49% pada data uji dan pada data latih sebanyak 94,2% serta dengan menggunakan Labelisasi dan Naive Bayes Classifier sebesar 86,53% pada data uji dan data latih sebesar 94,08%. Hasil penelitian ini diharapkan dapat membantu melakukan riset atas opini masyarakat pada Twitter mengenai Pilpres 2019 yang mengandung sentimen positif, negatif atau netral.

scholarly journals Determination of near-fault impulsive signals with multivariate naïve Bayes method

2021 ◽  
Author(s):  
Deniz Ertuncay ◽  
Giovanni Costa
Keyword(s):  
Naive Bayes ◽  
Strong Motion ◽  
Naïve Bayes ◽  
Strike Slip ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Earthquake Physics ◽  
Near Fault ◽  
A Site

AbstractNear-fault ground motions may contain impulse behavior on velocity records. To calculate the probability of occurrence of the impulsive signals, a large dataset is collected from various national data providers and strong motion databases. The dataset has a large number of parameters which carry information on the earthquake physics, ruptured faults, ground motion parameters, distance between the station and several parts of the ruptured fault. Relation between the parameters and impulsive signals is calculated. It is found that fault type, moment magnitude, distance and azimuth between a site of interest and the surface projection of the ruptured fault are correlated with the impulsiveness of the signals. Separate models are created for strike-slip faults and non-strike-slip faults by using multivariate naïve Bayes classifier method. Naïve Bayes classifier allows us to have the probability of observing impulsive signals. The models have comparable accuracy rates, and they are more consistent on different fault types with respect to previous studies.

Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language

2021 ◽  
Vol 30 (1) ◽  
pp. 774-792
Author(s):  
Mazin Abed Mohammed ◽  
Dheyaa Ahmed Ibrahim ◽  
Akbal Omran Salman
Keyword(s):  
Natural Language ◽  
Naive Bayes ◽  
False Negative ◽  
Naïve Bayes ◽  
Final Decision ◽  
Learning Approach ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Wide Range

Abstract Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest.

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