scholarly journals Review Study of Hoax Email Characteristic

2018 ◽  
Vol 7 (3.2) ◽  
pp. 778 ◽  
Author(s):  
SY. Yuliani ◽  
Shahrin Sahib ◽  
Mohd Faizal Abdollah ◽  
Mohammed Nasser Al-Mhiqani ◽  
Aldy Rialdy Atmadja
Keyword(s):  
Machine Learning ◽  
Fake News ◽  
Learning Models ◽  
Detection Systems ◽  
Review Study ◽  
Machine Learning Models

Hoax on email is one form of attack in the cyber world where an email account will be sent with fake news that has many goals to take advantage or raise the rating of sales of a product. A Hoax can affect many people by damaging the credibility of the image of a person or group. The phenomenon of this hoax would cause anxiety in the community and even more bad effects because of the potential for the wrong power of the news or information. In this paper we review the Hoax detection systems, Types of Hoax, and machine learning models that has been used to detect the Hoax. This work serves as a basis for further studies on Hoax detection systems.  

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

An Analysis of Segmentation Approaches and Window Sizes in Wearable-Based Critical Fall Detection Systems With Machine Learning Models

2020 ◽  
Vol 20 (6) ◽  
pp. 3303-3313 ◽  
Author(s):  
Kai-Chun Liu ◽  
Chia-Yeh Hsieh ◽  
Hsiang-Yun Huang ◽  
Steen Jun-Ping Hsu ◽  
Chia-Tai Chan
Keyword(s):  
Machine Learning ◽  
Fall Detection ◽  
Learning Models ◽  
Detection Systems ◽  
Machine Learning Models

scholarly journals Detection of Online Fake News Using Blending Ensemble Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Arvin Hansrajh ◽  
Timothy T. Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Fake News ◽  
Learning Models ◽  
Linear Discriminant ◽  
Proposed Model ◽  
Machine Learning Models

The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efficacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

Prediction and Classification of Biased and Fake News Using NLP and Machine Learning Models

2021 ◽  
pp. 13-20
Author(s):  
Premanand Ghadekar ◽  
Mohit Tilokchandani ◽  
Anuj Jevrani ◽  
Sanjana Dumpala ◽  
Sanchit Dass ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Models

Impact of Sampling Rate on Wearable-Based Fall Detection Systems Based on Machine Learning Models

2018 ◽  
Vol 18 (23) ◽  
pp. 9882-9890 ◽  
Author(s):  
Kai-Chun Liu ◽  
Chia-Yeh Hsieh ◽  
Steen Jun-Ping Hsu ◽  
Chia-Tai Chan
Keyword(s):  
Machine Learning ◽  
Sampling Rate ◽  
Fall Detection ◽  
Learning Models ◽  
Detection Systems ◽  
Machine Learning Models

scholarly journals Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 203 ◽  
Author(s):  
Martin Sarnovsky ◽  
Jan Paralic
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Learning Models ◽  
Knowledge Model ◽  
Detection Systems ◽  
Knowledge Based ◽  
Knowledge Models ◽  
Multi Stage ◽  
Machine Learning Approach ◽  
Machine Learning Models

Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches.

scholarly journals Fake News Data Exploration and Analytics

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2326
Author(s):  
Mazhar Javed Awan ◽  
Awais Yasin ◽  
Haitham Nobanee ◽  
Ahmed Abid Ali ◽  
Zain Shahzad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Random Forest Classifier ◽  
The Internet ◽  
Fake News ◽  
Learning Models ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Machine Learning Models

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.

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