This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution.
Due to the vast and fast growth of the internet, privacy is becoming a concern. Web browser data privacy is a part of the overall users' privacy. Since the attackers took social engineering to the next level, achieving the web browser history data secured from privacy breaches becomes difficult. In this work, we implemented three types of web fingerprinting attack using ML algorithms (DT_Attack, RF_Attack and AdaB_Attack) with the best performance results (Accuracy 99.9%, precision 100%, recall 100% and execution time 339 seconds) achieved using the Decision Tree classifier (DT_Attack). Then we proposed a defense mechanism using the forward feature selection algorithm named Reverse Forward Feature selection, which contributes to reducing the efficiency of the previously implemented web fingerprinting attacks. More specifically, accuracy, precision, and recall of the implemented attacks were reduced on all attack types between 44.8-54.3%, 24.3-38.6%, 53.6-70.2% respectively, without adding any overhead in terms of bandwidth and execution time.
Brain anatomy presents a unique pedagogical challenge in medical education due to its complex structures and the detailed intricacies inherent to various brain regions. Advanced imaging techniques such as computed tomography (CT), T1/T2 magnetic resonance imaging (MRI), and diffusion imaging provide 3D representations of these anatomical details, which require suitable tools and visualization setups to be taught efficiently to medical students. Machine learning and virtual reality have emerged as promising avenues for refining the visualization and reconstruction processes of these details. This paper delves into the integration of cutting-edge deep learning (DL) algorithms designed for the reconstruction of brain vascular trees and neural fiber tracts within an immersive virtual reality (VR) framework. Our proposed solution has the potential to improve brain anatomy education by serving as tool for illustrating subject-specific complex structures. The ensuing results underscore the viability and efficacy of our solution.
TIn an age dominated by internet usage, the threat of phishing attacks continues to plague users and organizations alike. Traditional cybersecurity mechanisms often fail to cope with the evolving tactics of cybercriminals, leading to a growing interest in machine learning-based solutions. This research evaluates the performance of four different machine learning classifiers-K-Nearest Neighbors (KNN), Naïve Bayes (NB), Decision Tree, and Artificial Neural Network (ANN)-in identifying phishing activities. A Python code framework is utilized for this evaluation, employing stratified k-fold cross-validation for reliable results. Multiple performance metrics indicate that machine learning provides an adaptable and effective tool against phishing. This research serves as a stepping stone for future development in this crucial area, urging continuous advancements to stay ahead of ever-evolving cybersecurity threats.
The detection of missing security operations is a complex task in software engineering, mainly due to the semantic and contextual understanding required. Prior research efforts have employed similar path differential analysis to detect missing security operations, but these approaches have been limited in their ability to simultaneously compare the similarity of intra- and inter-procedural paths. To address this limitation, this paper proposes a novel approach called SSD that can detect multiple missing security operation bugs both intra- and inter-procedurally. Our approach collects slices with similar semantics and contexts based on four program slicing criteria, providing more versatile construction of similar slices and more comprehensive detection than previous works. In our experiments, we have identified 65 real bugs in the Linux kernel, of which we have verified 27 as fixed bugs and submitted the remaining 38 for patching. The Linux maintainers have accepted 19 of these patches, confirming the effectiveness and availability of SSD.
