Identifying Efficient Road Safety Prediction Model Using Data Mining Classifiers: Recent Advancements |Chapter 5 | Emerging Trends in Engineering Research and Technology Vol. 5

 

Globally road safety is a major concern to prevent accidents. The aim of the traffic accident analysis for a region is to investigate the cause for accidents and to determine accident prone spots in a region. Multivariate analysis of traffic accidents data is critical to identify major causes for fatal accidents. In this work, accident dataset is analysed using algorithmic approach, as an attempt to address this problem. The reason for accident and with other attributes such as weather, surface, ambience, mobile usage and drunken driving were also considered. Prediction model was derived using various classifiers such as J48, Naive Bayes etc., to enhance safety measures for a accident prone region.

 

 

Author(s) Details

Durga Karthik

Department of Computer Science and Engineering, SRC – SASTRA Deemed University, Tamil Nadu, India.

 

K. Vijayarekha

Department of Electrical and Electronics Engineering, SASTRA Deemed University, Tamil Nadu, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/etert/v5

Supervised Text Classification Algorithms and Methods | Chapter 11 | Text Mining Techniques with Applications, Edition 1

Assigning a written document to one or more classes or groups is referred to as this task. We will go over a number of fundamental supervised text classification techniques, such as Decision Trees (DT), Naive Bayes, Support Vector Machines (SVM), Logistic Regression, and k Nearest Neighbor (kNN).

 

Author(s) Details

 

Adebola K. Ojo

Department of Computer Science, University of Ibadan, Ibadan, Nigeria.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-81-972870-5-3/CH11

A Comparative Study of Naive Bayes and Enhanced Random Forest Algorithms in Spam Detection | Chapter 4 | Science and Technology: Developments and Applications Vol. 3

Online Social Networks (OSNs) or simply social Media such as Facebook, X (Formerly known as Twitter), Instagram have recently emerged as one of the crucial platforms in human communication worldwide that allows individual users to send messages, build friendships, share perceptions and voice out their opinions, and get inspired. In this era of technology, online social media has become a rapidly growing phenomenon. The main social media platforms such as Instagram, Facebook, and X (formerly known as Twitter) connect and unite people globally, as quickly as any other communication medium. The growth of social media is expected to increase tremendously in the future. Online social media users generate and consume information independently. Many domains recognize the vital role of analyzing social media data, as this can improve operations and enable organizations to stay competitive. Nowadays, people spend a significant amount of time on social media platforms. However, the growing popularity of social media has also led to an increase in spamming and hacking activities. Cyber-criminals often spam and hack through external phishing sites or malware downloads, which pose significant security risks and poor user experience in social media networks. To combat the issue of spam, several methods have been proposed, but there is still no perfect, effective solution for detecting spam with high accuracy. In this chapter, we propose a spam detection approach using Naive Bayes (NB) and Enhanced Random Forest (ERF) classifiers. The Naive Bayes classifier applies Bayes' theorem with feature independence assumptions, while the Enhanced Random Forest improves on the traditional Random Forest with optimized feature handling. In order to assess the effectiveness of the proposed model, metrics such as accuracy, precision, recall and F1 scores are used to compare the model’s performance in recognizing spam messages. In conclusion, this study emphasizes that Enhanced Random Forest is more balanced in terms of efficiency and performance for social network spam detection and it can perform well with lower computational requirements.

 

Author (s) Details

 

M. Arunkrishna
PG & Research Department of Computer Science, Christhu Raj College (Affiliated to Bharathidhasan University), Tiruchirappalli - 620 012, Tamil Nadu, India.

 

B. Mukunthan
Department of Computer Science, Jairams Arts and Science College (Affiliated to Bharathidhasan University), Karur - 639003, Tamil Nadu, India.

B. Senthilkumaran
PG & Research Department of Computer Science, Christhu Raj College (Affiliated to Bharathidhasan University), Tiruchirappalli - 620 012, Tamil Nadu, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v3/3442

Study on Opinion Mining Framework Using Proposed RB-Bayes Model for Text Classification| Chapter 8 | New Approaches in Engineering Research Vol. 9

 Everyone is on social media, and their motivation is not only to be active but also to generate knowledge. We always read reviews on social media before making a purchase. Information mining is a capable concept with enormous potential for predicting future patterns and behaviour. It refers to the process of extracting hidden information from large data sets using techniques such as factual investigation, machine learning, grouping, neural systems, and genetics.algorithms. There is a problem of zero likelihood in naïve bayes. To solve the problem of zero likelihood, this work proposed the RB-Bayes technique, which is based on Baye's theorem. We also compare our strategy to a few other approaches, such as naive bayes and SVM. We show that this technique is superior to several existing strategies, and that it can analyse data sets more effectively. When the proposed approach is applied to real-world data sets, the results are generally more accurate. The precision of the RB-Bayes computation is 83,333.

Author (S) Details

Dr.Rajni Bhalla
Department of Computer Application, Lovely Professional University, India.

Dr. Amandeep Bagga
Department of Computer Application, Lovely Professional University, India.


View Book :- https://stm.bookpi.org/NAER-V9/article/view/2821

Identifying Efficient Road Safety Prediction Model Using Data Mining Classifiers: Recent Advancements | Chapter 5 | Emerging Trends in Engineering Research and Technology Vol. 5

Globally road safety is a major concern to prevent accidents. The aim of the traffic accident analysis for a region is to investigate the cause for accidents and to determine accident prone spots in a region. Multivariate analysis of traffic accidents data is critical to identify major causes for fatal accidents. In this work, accident dataset is analysed using algorithmic approach, as an attempt to address this problem. The reason for accident and with other attributes such as weather, surface, ambience, mobile usage and drunken driving were also considered. Prediction model was derived using various classifiers such as J48, Naive Bayes etc., to enhance safety measures for a accident prone region.

Author (s) Details

Dr. Durga Karthik
Department of Computer Science and Engineering, SRC – SASTRA Deemed University, Tamil Nadu, India.

Dr. K. Vijayarekha
Department of Electrical and Electronics Engineering, SASTRA Deemed University, Tamil Nadu, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/187