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RDNN: Rumor Detection Neural Network for Veracity Analysis in Social Media Text
SuthanthiraDevi P,Karthika S 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.12
A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.
Growth of rGO nanostructures via facile wick and oil flame synthesis for environmental remediation
Lekshmi G. S.,Tamilselvi R.,Prasad Karthika,Bazaka Olha,Levchenko Igor,Bazaka Kateryna,Mohandas Mandhakini 한국탄소학회 2021 Carbon Letters Vol.31 No.4
Oil spills into ocean or coastal waters can result in signifcant damage to the environment via pollution of aquatic ecosystems. Absorbents based on reduced graphene oxide (rGO) foams have the capacity to remove minor or major oil spills. However, conventional chemical synthesis of rGO often uses petrochemical precursors, potentially harmful chemicals, and requires special processing conditions that are expensive to maintain. In this work, an alternative cost-efective and environmentally friendly approach suitable for large-scale production of high-quality rGO directly from used cooking sunfower oil is discussed. Thus, produced faky graphene structures are efective in absorbing used commercial sunfower oil and engine oil, via monolayer physisorption in the case of used sunfower and engine oils facilitated by van der Waals forces, π–π stacking and hydrophobic interactions, π-cation (H+) stacking and radical scavenging activities. From adsorption kinetic models, frst-order kinetics provides a better ft for used sunfower oil adsorption (R2=0.9919) and second-order kinetics provides a better ft for engine oil adsorption (R2=0.9823). From intra-particle difusion model, R2 for USO is 0.9788 and EO is 0.9851, which indicates that both used sunfower and engine oils adsorption processes follow an intra-particle difusion mechanism. This study confrms that waste-derived rGO could be used for environmental remediation.