Agriculture has observed significant advancements since smart farming technology has been introduced.The Green Movement played an essential role in the evolution of farming methods. The use of smart farming is accelerating at an unprecedented rate because it benefits both farmers and consumers by enabling more effective crop budgeting. The Smart Agriculture domain uses the Internet of Things, which helps farmers to monitor irrigation management, estimate crop yields, and manage plant diseases. Additionally, farmers can learn about environmental trends and, as a result, which crops to cultivate and how to apply fungicides and insecticides. This research article uses the primary and subsidiary keywords related to smart agriculture to query the Scopus database. The query returned 146 research articles related to the keywords inputted, and an analysis of 146 scientific publications, including journal articles, book chapters, and patents, was conducted. Node XL, Gephi, and VOSviewer are open-source tools for visualizing and exploring bibliometric networks. New facets of the data are revealed, facilitating intuitive exploration. The survey includes a bibliometric analysis as well as a word cloud analysis. This analysis focuses on publication types and publication regions, geographical locations, documents by year, subject area, association, and authorship. The research field of IoT in agricultural plant disease detection articles is found to frequently employ English as the language of publication.

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.

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.

Cervical cancer remains a significant global health issue, particularly in underdeveloped nations, where it contributes to high mortality rates. Early detection is critical for improving treatment outcomes and survival rates. This study employs machine learning (ML) algorithms to predict cervical cancer risk using a dataset from the University of California at Irvine (UCI), which includes demographic and clinical attributes such as age, sexual history, smoking habits, and medical history. After applying data preprocessing techniques, several classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree, adaptive boosting (AdaBoost), and artificial neural networks (ANN), were trained and evaluated. The models were assessed using classification metrics such as precision, recall, and F1 score. Among the models, the ANN demonstrated the highest accuracy, achieving a score of 0.95. In addition, correlation analysis revealed significant relationships between various risk factors, providing insights into cervical cancer mechanisms and potential preventive measures. The study highlights the potential of ML in improving cervical cancer detection and patient outcomes, suggesting that advanced ML techniques can be valuable tools in healthcare research and clinical applications.

Everyday technological advancement and expansion make safety more and more crucial across all fields. Everybody wants a private space that only they can access, so one must secure the room, office, locker, and other space that protect the priceless jewelry, documents, and accessories. To that end, the chapter has created a knocking sensor-based door lock system by using Arduino. A sensor-based door opening and closing system has been designed to enable the opening and closing of the door. A project on Arduino UNO has been made to detect the knocking at door. Depending on the number and time interval of knocks, the sensor detects, and a signal is generated which lights the LED and displays information on LCD. The system is designed to provide the safety of a person. The future scope of the design is to implement it with IoT-enabled devices.

The amount of data being produced and used every day is increasing rapidly. Different operations are performed on the data that is stored in database systems to generate meaningful information. The task of retrieving data from the database is difficult for inexperienced users who don’t have great knowledge of Database Management System (DBMS) languages such as Structured Query Language (SQL). Using Natural Language Processing, anyone can directly interact with the database system. To achieve this type of communication, we have developed a novel and improved Intelligent Search Engine (ISE) Tool using natural language processing. Using this search engine, the user can query the database system in natural language (human-understandable language). The intelligent search engine will convert the natural language query into the DBMS language query to retrieve the data from the database. The proposed system also has a query recommendation engine that recommends the possible queries in case of any error or if the result is not found. The intelligent search engine proposed in this work is a generic tool that is not dependent on the front or back end of the system, therefore it can be used for any database system.