Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.

Machine learning can be used to automate a wide range of tasks. Smart buildings, which use the Internet of Things (IoT) to connect building operations, enable activities, such as monitoring temperature, safety, and maintenance, for easier controlling via mobile devices and computers. Smart buildings are becoming core aspects in larger system integrations as the IoT is becoming increasingly widespread. The IoT plays an important role in smart buildings and provides facilities that improve human security by using effective technology-based life-saving strategies. This review highlights the role of IoT devices in smart buildings. The IoT devices platform and its components are highlighted in this review. Furthermore, this review provides security challenges regarding IoT and smart buildings. The main factors pertaining to smart buildings are described and the different methods of machine learning in combination with IoT technologies are also described to improve the effectiveness of smart buildings to make them energy efficient.

Deep learning has transformed image classification tasks across many domains, including medical diagnostics. Medicine wrappers, boxes, and strips often contain valuable but complex information that can be difficult to read and comprehend manually. This complexity drives users to seek additional knowledge online. However, traditional search engines often present a large volume of results, requiring users to manually filter through multiple links to find relevant information, which can be time-consuming. To address this issue, we propose a lightweight pipeline that leverages MobileNetV2 for image classification and Optical Character Recognition (OCR) for extracting text content from medicine packaging. The extracted text is processed using the RAKE algorithm to identify significant keywords, which are then matched with relevant URLs through a Google Search API. To ensure relevance, retrieved links are ranked using ROUGE scores. Performance metrics demonstrate the model's efficiency, with ROUGE-1 achieving 90% Recall, 95% F1-Score, and 90% Accuracy, and ROUGE-L achieving 83% Recall, 91% F1-Score, and 83% Accuracy. The pipeline was trained and validated on a curated dataset of 3,000 real-world medicine packaging images, publicly available on GitHub. These results highlight the novelty and practicality of our solution for automating medical information retrieval from packaging, using an interpretable and scalable deep learning-driven approach.