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Sentiment Analysis for COVID-19 Vaccine Popularity
( Muhammad Saeed ),( Naeem Ahmed ),( Abid Mehmood ),( Muhammad Aftab ),( Rashid Amin ),( Shahid Kamal ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.5
Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.
A Cross-Platform Malware Variant Classification based on Image Representation
( Hamad Naeem ),( Bing Guo ),( Farhan Ullah ),( Muhammad Rashid Naeem ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.7
Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.
Instability analysis of microfilaments with and without surface effects using Euler theory
Taj, Muhammad,Khadimallah, Mohamed A.,Hussain, Muzamal,Mahmood, Shaid,Safeer, Muhammad,Rashid, Yahya,Ahmad, Manzoor,Naeem, M. Nawaz,Asghar, Sehar,Ponnore, Joffin,Al Qahtani, Abdelaziz,Mahmoud, S.R.,Al Techno-Press 2021 Advances in nano research Vol.10 No.6
The study of cell components has been an active area of research since the last few decades. Cytoskeleton of the cell which gives shape and provides structure to the cell has three main components, microtubules, microfilaments and intermediate filaments. Each of the cytoskeletal components is surrounded by various filamentous or the other cytoskeletal components act as a surface layer on these filaments. The stability of these components affected when cell perform various functions in the body and as a result these filaments buckle, vibrate and bend. In the present study the buckling behavior of microfilament is discussed with the effects of surface by using Euler Bernoulli beam theory and the obtained results for free and surrounded microfilament are shown in the tables and figures.