Tebyan: Fake News Detection System (Preprint)

2021 ◽  
Author(s):  
Lamya Alderywsh ◽  
Aseel Aldawood ◽  
Ashwag Alasmari ◽  
Farah Aldeijy ◽  
Ghadah Alqubisy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arab World ◽  
Detection System ◽  
Learning Algorithms ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Fake News ◽  
Typical Type ◽  
Performance Results

BACKGROUND There is a serious threat from fake news spreading in technologically advanced societies, including those in the Arab world, via deceptive machine-generated text. In the last decade, Arabic fake news identification has gained increased attention, and numerous detection approaches have revealed some ability to find fake news throughout various data sources. Nevertheless, many existing approaches overlook recent advancements in fake news detection, explicitly to incorporate machine learning algorithms system. OBJECTIVE Tebyan project aims to address the problem of fake news by developing a fake news detection system that employs machine learning algorithms to detect whether the news is fake or real in the context of Arab world. METHODS The project went through numerous phases using an iterative methodology to develop the system. This study analysis incorporated numerous stages using an iterative method to develop the system of misinformation and contextualize fake news regarding society's information. It consists of implementing the machine learning algorithms system using Python to collect genuine and fake news datasets. The study also assesses how information-exchanging behaviors can minimize and find the optimal source of authentication of the emergent news through system testing approaches. RESULTS The study revealed that the main deliverable of this project is the Tebyan system in the community, which allows the user to ensure the credibility of news in Arabic newspapers. It showed that the SVM classifier, on average, exhibited the highest performance results, resulting in 90% in every performance measure of sources. Moreover, the results indicate the second-best algorithm is the linear SVC since it resulted in 90% in performance measure with the societies' typical type of fake information. CONCLUSIONS The study concludes that conducting a system with machine learning algorithms using Python programming language allows the rapid measures of the users' perception to comment and rate the credibility result and subscribing to news email services.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals Towards scaling Twitter for digital epidemiology of birth defects

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ari Z. Klein ◽  
Abeed Sarker ◽  
Davy Weissenbacher ◽  
Graciela Gonzalez-Hernandez
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Birth Defects ◽  
Birth Defect ◽  
Learning Algorithms ◽  
Class Imbalance ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Svm Classifier

Abstract Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes—the leading cause of infant mortality—could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms—feature-engineered and deep learning-based classifiers—that automatically distinguish tweets referring to the user’s pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F1-score of 0.65 for the “defect” class and 0.51 for the “possible defect” class. We deployed the classifier on 20,457 unlabeled tweets that mention birth defects, which helped identify 542 additional users for potential inclusion in our cohort. Contributions of this study include (1) NLP methods for automatically detecting tweets by users reporting their birth defect outcomes, (2) findings that an SVM classifier can outperform a deep neural network-based classifier for highly imbalanced social media data, (3) evidence that automatic classification can be used to identify additional users for potential inclusion in our cohort, and (4) a publicly available corpus for training and evaluating supervised machine learning algorithms.

scholarly journals A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1248
Author(s):  
Eleana Hatzidaki ◽  
Aggelos Iliopoulos ◽  
Ioannis Papasotiriou
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Cancer Screening ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Significant Information

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.

scholarly journals Computational classification of animals for a highway detection system

2021 ◽  
Vol 58 ◽  
pp. e174951
Author(s):  
Denis Sato ◽  
Adroaldo José Zanella ◽  
Ernane Xavier Costa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Road Safety ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.

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