Wavelets Based Anomaly-Based Detection System or J48 and Naïve Bayes Based Signature-Based Detection System: A Comparison

2018 ◽  
pp. 213-224 ◽  
Author(s):  
Gagandeep Kaur ◽  
Amit Bansal ◽  
Arushi Agarwal
Keyword(s):  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
System A

scholarly journals An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm

2020 ◽  
Vol 20 (1) ◽  
pp. 82-94
Author(s):  
Badrus Zaman ◽  
Army Justitia ◽  
Kretawiweka Nuraga Sani ◽  
Endah Purwanti
Keyword(s):  
Performance Evaluation ◽  
System Performance ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Bayes Algorithm ◽  
F Measure ◽  
System Performance Evaluation

AbstractHoax news in Indonesia spread at an alarming rate. To reduce this, hoax news detection system needs to be created and put into practice. Such a system may use readers’ feedback and Naïve Bayes algorithm, which is used to verify news. Overtime, by using readers’ feedback, database corpus will continue to grow and could improve system performance. The current research aims to reach this. System performance evaluation is carried out under two conditions ‒ with and without sources (URL). The system is able to detect hoax news very well under both conditions. The highest precision, recall and f-measure values when including URL are 0.91, 1, and 0.95 respectively. Meanwhile, the highest value of precision, recall and f-measure without URL are 0.88, 1 and 0.94, respectively.

scholarly journals K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

2021 ◽  
Vol 9 (6) ◽  
pp. 2650-2657
Author(s):  
Mohd Hatta Jopri ◽  
Mohd Ruddin Ab Ghani ◽  
Abdul Rahim Abdullah ◽  
Mustafa Manap ◽  
Tole Sutikno ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Single Point ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
System A ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Source

This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.

scholarly journals Using Classification Techniques to SMS Spam Filter

2019 ◽  
Vol 8 (12) ◽  
pp. 1734-1739
Keyword(s):  
English Language ◽  
Naive Bayes ◽  
Detection System ◽  
Arabic Language ◽  
Naïve Bayes ◽  
The Body ◽  
Training Set ◽  
Spam Filter ◽  
Arabic And English ◽  
Textual Content

SMS is service that uses mobile phone that allows the users to exchange textual content. Spamming can be defined as sending unwanted content to a group of people for various purposes such as fraud. SMS spam is one form of spamming in which unwanted messages are delivered to many clients by spammers. Therefore, it has become necessary to develop SMS spam detection system to keep up with the current development of message services. Where the aim of this work is developing spam filter for Arabic and English languages by using two filter to be able to detect spam sms efficiently. Content based method was used to build spam filter for English and Arabic languages. based on this method, there are a number of steps should be taken which are Read English and Arabic dataset, Preprocessing phase, Feature Extraction and Classification. The first step after reading the dataset for Arabic and English languages is preprocessing phase which is important step to get more accurate results. The next step is extracting the features from the body of each message. Eight features have been extracted from English messages and six features from Arabic messages. Then features of messages for English and Arabic languages are splitted into two set: training set and testing set. Training set are used to train the algorithms while the test set are used evaluate the performance of proposed Spam filter for the English and Arabic language. In proposed system two classifiers are used. Naive Bayes is used as first classifier and neural network as second classifier. The incoming messages are passed through naive Bayes classifier. If it is classified as ham then passes to second classifier to make sure if it is spam, otherwise it doesn’t passes to second classifier. The results of the proposed system were acceptable with 97% accuracy is obtained for English language when using eight features and 80% from dataset for training .And 95% accuracy is obtained for Arabic language with six features and 70% from dataset for training.

Sign in / Sign up

Export Citation Format

Share Document