A New Approach Using Hidden Markov Model and Bayesian Method for Estimate of Word Types in Text Mining

2017 ◽  
Vol 8 (4) ◽  
pp. 17-29 ◽  
Author(s):  
Adem Doganer ◽  
Sinan Calik
Keyword(s):  
Text Mining ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Information Extraction ◽  
Success Rate ◽  
Bayesian Method ◽  
Hidden Markov ◽  
Text Summarization ◽  
New Approach ◽  
New Model

Determining the structure of words in the text for the operations such as automated information extraction and text summarization of the text is essential. In computers, textual analysis to define the type of the word is considered as a vital advantage. Defining the types of words provides an estimate of the sequence of words in the sentence. In this article, estimating types of Turkish words is provided by developing a Hidden Markov Model and a Bayesian-based new model. In this model, an algorithm is developed which separates the suffixes of the words and grouping the words by counts of characters that suffixes of the words receive. A text composed of 584 Turkish words is used for the testing the dependability of the model. The model has achieved a high success rate in predicting the types of Turkish words.

scholarly journals Credit Card Fraud Detection Performance Improvement using Advanced Super Gradient Boosting Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 179-184
Keyword(s):  
Text Mining ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Training Data ◽  
Gradient Boosting ◽  
Data Set ◽  
Credit Card Fraud ◽  
Mining Algorithm

Credit card fraud introduces to the physical loss of a credit card or the destruction of sensitive credit card data. Several text mining procedures can be used for disclosure. This investigation reveals several algorithms that can be used to analyze transactions as a fraud or as a real background. This paper represents the possibility of fraudulent transactions in the prevalence and meaning of credit card usage also, Credit card fraud data collection was used in the investigation. Since the dataset was largely unbalanced, SMOTE (Synthetic Minority oversampling Technique) is applying for an overdose. In addition, jobs selected, and the data set divided into two parts, training data and test data. In this paper, The Advanced Super Gradient Boostingbased Text mining Algorithm (ASGB) suggested to detect the fraud transaction in Credit card transactions. ASGB is a Decision-Tree-Based Ensemble Text mining algorithm that utilizes a gradient boosting framework. In forecast difficulties, including unstructured data (Images, Text, etc.), artificial neural networks tend to exceed all other algorithms or structures. The proposed algorithms used in the experiment were the Hidden Markov Model, Random Forest, Gradient Boosting, and Enhanced Hidden Markov Model. The Experimental Results show that proposed algorithms, a welltuned ASGB classifier outperforms all of them. And it presents better Precision is 99.1%, and Recall is 99.8%, F-measure is 99.5%.

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