Network security is crucial in today’s digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.Intrusion detection systems (IDSs) are one of the significant aspect of cybersecurity that involve the monitoring and analysis, with the intention of identifying and reporting of dangerous activities that would help to prevent the attack.Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN) are the vector figures incorporated into the study through the results. These models are subjected to various test to established the best results on the identification and prevention of network violation. Based on the obtained results, it can be stated that all the tested models are capable of organizing data originating from network traffic. thus, recognizing the difference between normal and intrusive behaviors, models such as SVM, KNN, RF, and DT showed effective results. Deep learning models LSTM and ANN rapidly find long-term and complex pattern in network data. It is extremely effective when dealing with complex intrusions since it is characterised by high precision, accuracy and recall.Based on our study, SVM and Random Forest are considered promising solutions for real-world IDS applications because of their versatility and explainability. For the companies seeking IDS solutions which are reliable and at the same time more interpretable, these models can be promising. Additionally, LSTM and ANN, with their ability to catch successive conditions, are suitable for situations involving nuanced, advancing dangers.

A significant advancement in artificial intelligence is the development of large language models (LLMs). Despite opposition and explicit bans by some authorities, LLMs continue to play a transformative role, particularly in education, by improving language understanding and generation capabilities. This study explores LLMs’ types, history, and training processes, alongside their application in education, including digital and higher education settings. A novel theoretical framework is proposed to guide the integration of LLMs into education, addressing key challenges such as personalization, ethical concerns, and adaptability. Furthermore, the study presents practical case studies and solutions to barriers, such as data privacy and bias, offering insights into their role in enhancing the teaching–learning process. By providing a systematic analysis and proposing a structured framework, this study advances current knowledge and highlights the significant potential of LLMs in revolutionizing education.

Energy efficiency and safety are two essential factors that play a significant role in operating a wireless sensor network. However, it is claimed that these two factors are naturally conflicting. The level of electrical consumption required by a security system is directly proportional to its degree of complexity. Wireless sensor networks require additional security measures above the capabilities of conventional network security protocols, such as encryption and key management. The potential application of machine learning techniques to address network security concerns is frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them to understand their environment and respond. During the training phase, machine-learning systems may face challenges due to the large amount of data required and the complex nature of the training procedure. The main objective of the article is to know about different machine learning algorithms that are used to solve the security issues of wireless sensor networks. This study also focuses on the use of wireless sensor networks in different fields. Furthermore, this study also focuses on different Machine learning algorithms that are used to secure wireless sensor networks. Moreover, this study also addresses issues of adapting machine learning algorithms to accommodate the sensors’ functionalities in the network configuration. Furthermore, this article also focuses on open issues in this field that must be solved.

Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMT-based approach is considered. The google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0.

As higher education faces technological advancement and environmental imperatives, AI becomes a key instrument for revolutionizing instructional methods and institutional operations. AI can improve educational outcomes, resource management, and long-term sustainability in higher education, according to this study. The research uses case studies and best practices to show how AI-driven innovations can minimize environmental impact, enhance energy efficiency, and customize learning, creating a more sustainable and inclusive academic environment. The document discusses AI ethics, including data privacy, algorithmic prejudice, and the digital divide. It emphasizes the need for strong ethical frameworks to use AI ethically and make decisions with transparency and fairness. The study also emphasizes the need for robust institutional rules and infrastructure to promote ethical AI integration, protecting student privacy and supporting fair access to AI technologies. The research also shows how AI-driven curriculum-building tools can educate students for future sustainability concerns and stimulate research innovation. The prospects and difficulties of AI in higher education are critically examined, including its potential to change traditional educational roles, improve academic performance, and maintain institutional profitability. Actionable recommendations for educators, politicians, and institutional leaders contribute to the education sustainability conversation. Focusing on AI and sustainability creates the framework for a future where technology and environmental stewardship are intimately connected, ensuring that higher education institutions can prosper in a fast-changing world.

Patient-focused healthcare applications are important to patients because they offer a range of advantages that add value and improve the overall healthcare experience. The 5G networks, along with Mobile Edge Computing (MEC), can greatly transform healthcare applications, which in turn improves patient care. MEC plays an important role in the healthcare of patients by bringing computing resources to the edge of the network. It becomes part of an IoT system within healthcare that brings data closer to the core, speeds up decision-making, lowers latency, and improves the overall quality of care. While the usage of MEC and 5G networks is beneficial for healthcare purposes, there are some issues and difficulties that should be solved for the efficient introduction of this technological pair into healthcare. One of the critical issues that blockchain technology can help to overcome is the challenge faced by MEC in realizing the most potential applications involving IoT medical devices. This article presents a comprehensive literature review on IoT-based healthcare devices, which provide real-time solutions to patients, and discusses some major contributions made by MEC and 5G in the healthcare industry. The paper also discusses some of the limitations that 5G and MEC networks have in the IoT medical devices area, especially in the field of decentralized computing solutions. For this reason, the readership intended for this article is not only researchers but also graduate students.