There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.

There are very renowned social media platforms like Instagram, Twitter, Facebook, etc., with each of which being used by different shareholders across the world to communicate with each other. Social media is a pool of online communication platforms that are based on community input, content sharing, and collaborations. The way we communicate, share information, and connect with other people has been revolutionized by social media. This has led to a series of benefits but also posed many challenges, especially in cybersecurity. This paper investigates the varied influences of social media, examining both its good and negative consequences across a variety of industries. It focuses specifically on the cybersecurity concerns posed by the growing usage of social media, shedding light on the vulnerabilities encountered by individuals and organizations. This investigation includes a study of common cybercrimes like phishing, social engineering, burglary via social networking, virus attacks, cyberstalking, identity theft, and cybercasing. This study emphasizes the importance of a complete and targeted cybersecurity approach that includes preventive measures such as privacy enhancements, user training, sophisticated email filtering, robust authentication, and encryption technologies. Individuals and organizations can traverse the evolving social media ecosystem with greater cyber resilience by addressing these challenges and using proactive tactics.

The past year saw our world afflicted by COVID-19 undergo a digital transformation which led to a majority of people and organizations gravitate towards the internet. A remote working environment complicated the pre-existent crisis of phishing where the vulnerable population incurred huge losses at the hands of internet miscreants. A phishing attack comprises an attacker that creates fake websites to fool users and steal client-sensitive data which may be in form of login, password, or credit card details. Timely detection of phishing attacks has become more crucial than ever. Hence in this paper, we provide a thorough literature survey of the various machine learning methods used for phishing detection. This thesis will discuss in detail, different approaches used by various authors over the past few years. This survey aims to identify and narrow down the best machine learning algorithms that can be adopted to develop a hybrid model which can be implemented to detect whether a website is legitimate or phishing in nature.

There has been widespread interest in the concept of the metaverse in recent years. The aim of this comprehensive review paper is to provide an in-depth analysis of the taxonomy, technological foundations, and historical evolution of the metaverse. The study explores both the positive and negative dimensions of the metaverse, including ethical dilemmas, using a robust analytical framework. In addition to studying the metaverse’s impact on various sectors of society and the economy, this study offers an insight into how it will develop in the coming years. A notable highlight is the exploration of the estimated revenue forecast for metaverse money-making, projecting a substantial 40 billion USD by the 2030s. Further, the paper examines cyberbullying within the metaverse, shedding light on the unique challenges it poses. The hype surrounding the metaverse has also been analyzed, as well as its implications for the broader technological landscape.

Reliable real-time vehicle control is essential for intelligent transport systems where accurate perception and decision-making depend on fast sensor data processing. This study developed a hybrid edge–cloud method integrating deep learning with Internet of Things (IoT) sensor fusion for adaptive vehicle control. Ultrasonic range data were combined with convolutional neural networks (CNNs) to enable object detection, stopping-time prediction, and braking control under varying environmental conditions. The CNN-based model was trained and evaluated under normal and simulated adverse driving scenarios. Results indicated strong performance with R² = 0.99 under normal and 0.98 under adverse conditions, and a mean squared error (MSE) of 0.0085. Average inference latency is 110–116 ms on Jetson Nano and 210–230 ms on Raspberry Pi, confirming suitability for real-time deployment on edge hardware.