The indications of acupoints are thought to be highly associated with the lines of the meridian systems. The present study used data mining methods to analyze the characteristics of the indications of each acupoint and to visualize the relationships between the acupoints and disease sites in the classic Korean medical text Chimgoogyeongheombang. Using a term frequency-inverse document frequency...

This thesis endeavors to solve a text classification (TC) problem of a real-world system, New Brunswick Opportunities Network (NBON), an online tendering system that helps the vendors and the purchasing agents to provide and obtain information about business opportunities. The solution mainly involves techniques in the areas of machine learning and natural language processing (NLP). We use a Nä...

This study collects abstracts of SSCI tourism journal papers between 2010 and 2019 from the WoS (Web Science) database uses a novel method topic classification to explore vocabulary characteristics classified articles. The corpora are given quantitative Term Frequency–Inverse Document Frequency (TF–IDF) weights. A hierarchical K-means cluster analysis is then performed automatically classify ar...

Abstract Undertaking a multistage qualitative approach, this study explores the accounting profession's demands for information communication technology (ICT) skills as well opportunities, challenges and influential factors that academics encounter in embedding ICT data analytics curriculum. We employ content analysis of course syllabi all major Australian New Zealand universities using term fr...

Among the many social media platforms that have emerged, TikTok is a platform has most significant number of subscribers compared to other platforms. However, not all reviews given by users are good and often found with slang real meaning, therefore sentiment analysis needed for these problems. These will later be analyzed according predetermined aspects, namely feature business content aspects...

Automatic discovery of how members of social media are discussing different thoughts on particular topics would provide a unique insight into how people perceive different topics. However, identifying trending terms / words within a topical conversation is a difficult task. We take an information retrieval approach and use tf-idf (term frequency-inverse document frequency) to identify words tha...

The rapid development of the internet has made information flow rapidly wich an impact on world commerce. Some people who have bought a product will write their opinion social media or other online site. Long-text buyer reviews need machine to recognize opinions. Sentiment analysis applies text mining method. One methods applied in sentiment is classification. classification algorithms naïve ba...

This manuscript introduces a new concept of statistical depth function: the compositional D-depth. It is first data developed exclusively for text data, in particular, those vectorized according to frequency-based criterion, such as tf-idf (term frequency–inverse document frequency) statistic, which results most vector entries taking value zero. The proposed consists considering inverse discret...

The main functions of an Intrusion Detection System (IDS) are to protect computer networks by analyzing and predicting the actions of processes. Though IDS has been developed for many years, the large number of alerts makes the system inefficient. In this paper, we proposed a classification method based on Support Vector Machines (SVM) with a weighted voting schema to detect intrusions. First, ...

Internet use has become increasingly widespread nowadays. In addition, there is a significant increase in the amount of text content produced digital media. However, accuracy and inaccuracy news we read large number are also unknown. this study, classification analysis whether real or not were done by using Deep Learning methods. For English news, data set created Katharine Jarmul was used. The...