The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

The rise of the Internet of Things (IoT) has driven significant advancements across sectors such as urbanization, manufacturing, and healthcare, all of which are focused on enhancing quality of life and stimulating the global economy. This survey offers an in-depth analysis of the integration of blockchain technology with IoT, addressing aspects such as architectural alignment, applications, security, limitations, scalability, and latency. Moreover, this survey focuses on security, integration techniques, and future research directions. The primary contributions of this review include a taxonomy of security concerns specific to IoT, an analysis of integration methods, and insights into consensus mechanisms suitable for resource-constrained environments. These findings highlight the unique challenges and opportunities in IoT–blockchain integration, providing a foundation for advancing secure and scalable IoT applications. By exploring consensus mechanisms and resource-constrained deployments, this paper provides a framework for developing secure and efficient IoT applications utilizing blockchain technology and providing a basis for future research and practical applications. In addition, this survey investigates innovative trends, including AI-driven blockchain for IoT.

One of the serious security breaches that threatens today's smart technologies is the broadcasting of false alarms. These alarms may severely affect road management systems. Vehicular networks suffer from this type of threat because of the opacity and the on-purpose broadcasting of false contents that occur frequently. Modern cloud-fog communication architectures force the organization of hierarchical communications, resulting in more robust governance of disseminated contents. This paper proposes a prediction technique that is based on a binary classification using hidden Markov model (HMM). Our contributions in this research area are as follows: 1) modeling fog data features to enforce organization of communication, 2) proposing a technique for the automatic modeling of HMM parameters, which facilitate the precise prediction of false contents, and 3) proposing a dissemination algorithm for fast communication. We conducted several experiments to measure and compare the performance of the proposed model with that of state-of-the-art classification algorithms. We also performed a misuse analysis to measure the ability of our proposed model to detect false alarms in synthesis and real-world datasets. In addition, we conducted a further analysis to demonstrate the sensitivity of our methodology to cutoff points. The results showed that our HMM-based technique provides promising prediction accuracy and significantly outperforms existing classification techniques in detecting false-alarm contents

A significant percentage of security research that is conducted suffers from common issues that prevent wide-scale adoption. Common snags of such proposed methods tend to include (i) introduction of additional nodes within the communication architecture, breaking the simplicity of the typical client–server model, or fundamental restructuring of the Internet ecosystem; (ii) significant inflation of responsibilities or duties for the user and/or server operator; and (iii) adding increased risks surrounding sensitive data during the authentication process. Many schemes seek to prevent brute-forcing attacks; they often ignore either partially or holistically the dangers of other cyber-attacks such as MiTM or replay attacks. Therefore, there is no incentive to implement such proposals, and it has become the norm instead to inflate current username/password authentication systems. These have remained standard within client–server authentication paradigms, despite insecurities stemming from poor user and server operator practices, and vulnerabilities to interception and masquerades. Besides these vulnerabilities, systems which revolve around secure authentication typically present exploits of two categories; either pitfalls which allow MiTM or replay attacks due to transmitting data for authentication constantly, or the storage of sensitive information leading to highly specific methods of data storage or facilitation, increasing chances of human error. This paper proposes a more secure method of authentication that retains the current structure of accepted paradigms, but minimizes vulnerabilities which result from the process, and does not inflate responsibilities for users or server operators. The proposed scheme uses a hybrid, layered encryption technique alongside a two-part verification process, and provides dynamic protection against interception-based cyber-attacks such as replay or MiTM attacks, without creating additional vulnerabilities for other attacks such as bruteforcing. Results show the proposed mechanism outperforms not only standardized methods, but also other schemes in terms of deployability, exploit resilience, and speed.