machine learning models
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2022 ◽  
Vol 9 (3) ◽  
pp. 1-22
Mohammad Daradkeh

This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet Allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.

Subhra Swetanisha ◽  
Amiya Ranjan Panda ◽  
Dayal Kumar Behera

<p>An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.</p>

2022 ◽  
Vol 8 ◽  
pp. 612-618
Pavel Matrenin ◽  
Murodbek Safaraliev ◽  
Stepan Dmitriev ◽  
Sergey Kokin ◽  
Anvari Ghulomzoda ◽  

2022 ◽  
Vol 211 ◽  
pp. 105957
Seoro Lee ◽  
Joo Hyun Bae ◽  
Jiyeong Hong ◽  
Dongseok Yang ◽  
Panos Panagos ◽  

2022 ◽  
Vol 54 (9) ◽  
pp. 1-36
Dylan Chou ◽  
Meng Jiang

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

2022 ◽  
Vol 96 ◽  
pp. 80-84
Stephen Bacchi ◽  
Luke Oakden-Rayner ◽  
David K Menon ◽  
Andrew Moey ◽  
Jim Jannes ◽  

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