Liar Benchmark
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2024 Real-Time Intelligent Systems
In the rapidly advancing field of Intelligent Computing, tracking, monitoring, synthesizing, and inferencing real-time data is becoming a crucial field of interest. This will impact several research themes, including all sub-domains of AI. The significance of real-time data processing lies in their ability to produce remarkably realistic implications, thereby mitigating challenges associated with intelligence capturing. This compendium reflects the recent progress in real-time intelligence, sensing the innovative approaches and addressing the challenges. It includes both core research and various applications in various disciplines. This publication enables young researchers and master’s students to understand the requirements for initiating research in real-time computing.
Data Intelligence and Cognitive Informatics
The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2021), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during July 16–17, 2021. This book discusses new cognitive informatics tools, algorithms, and methods that mimic the mechanisms of the human brain which leads to an impending revolution in understating a large amount of data generated by various smart applications. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning, and cognitive science to study and develop a deeper understanding of the information processing systems.
A Study of Tackling Fake News with Machine Learning Approaches
Author: Balamurugan Rengeswaran
language: en
Publisher: GRIN Verlag
Release Date: 2024-05-17
Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely ” fake news and real news”, ”ISOT” and ”LIAR”. We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset.