The widespread use of Liquefied Petroleum Gas (LPG) in households and industries presents a significant risk due to its highly flammable nature, making early detection of leaks for preventing accidents. Traditional LPG detection methods often rely on manual monitoring, which may not provide timely alerts or automatic responses to prevent accidents. The Internet of Things (IoT) refers to a network of physical objects, or “things,” embedded with sensors, software, and other technologies that enable them to connect and exchange data with other devices and systems over the internet. The advantage of the Internet of Things (IoT) offers new opportunities to safety systems through real-time monitoring and remote alerts. By integrating IoT technology with LPG leak detection, it is possible to create a responsive and reliable safety system. This device focuses on the development of an IoT-based LPG tank leakage detection system with alarm and auto-off system, designed to detect leaks, sound an alarm, and automatically shut off the gas supply or even remotely turn off the valve through an android-based application to prevent accidents. The system uses IoT for real-time data transmission and remote monitoring, allowing users to receive instant notifications and take immediate action, even when away from the premises. The system uses gas sensors to continuously monitor LPG levels, triggering an immediate response in the event of a leak. Integrated with IoT technology, the system provides real-time alerts to users via android-based application, ensuring prompt action.

This paper is introduced as an exploration of how to utilize advanced technology and computing can assist individuals who are mute and/or deaf-mute individuals. It recognizes the rapid progress in technology and computing, which opens up opportunities to address the unique needs of this particular group. The research question addressed in this paper is how we can use accurate time sign language recognition to assist people suffering from mutism and deafness to make the healthcare system more inclusive and accessible to them, especially when taking visual acuity assessments which is normally needed for acquiring eyeglasses. The system comprises a microprocessor unit and a Python-based program that is used to facilitate the visual acuity assessment properly and accurately. The keyfindings of this paper suggest that realtime sign language recognition technology can significantly improve the healthcare experience for individuals who are suffering from mutism and deafness by facilitating communication and reducing barriers to access facilities for eye health care to person with disability like person who are mute and deaf. The study also highlights the need for further research and development so as to improve the accuracy and efficiency of sign language recognition technology for people who are mute and deaf.

As the Philippines advances industrially, the nation's demand for electricity is soaring. In response to this challenge, the proponent seeks to devise an Internet-of-Things (IoT) solution that addresses the imbalance between electricity supply and demand through a cost-effective yet highly accurate energy management system for commercial and industrial sectors. The idea is to curtail energy consumption by enabling building managers to remotely control shared area resources, thereby resulting in a corresponding decrease in energy expenses. The designed prototype will also encompass a feature for power interruptions both blackout and brownout detection and notification, which will allow building supervisors to implement electrical outage procedures during necessary circumstances. When power is restored, the system will initiate a “rolling” procedure to prevent sudden load spikes on the power grid by first restoring power to areas known for lower current draw. This approach differs from the typical scenario where, after a blackout, all devices power on at once when service is restored, escalating the risk of another brownout or potentially even an electrical fire due to an overloaded grid. In the event of a power outage, the system will rely on an alternate power supply to maintain its operation. The envisioned product is designed to be portable and transferable, meaning it can be conveniently installed in any commercial building and relocated as required. Moreover, the design contemplates a scenario where several such prototypes can be placed within a single building and individually controlled remotely via web application.

Pothole detection is critical in defining optimal road management solutions and maintenance. The researcher used deep learning and yolov3 to create a pothole detection system in this study. A deep learning algorithm called YOLOv3 is used to develop a model that can successfully identify potholes. The detection model had an average precision of 95.43%, and identified potholes had accuracies ranging from 33% to 69%, which is to be anticipated given the numerous various forms and sizes of potholes.

People are anxious about the potential dangers of scuba diving and like in all sports, there are dangers involved in it. Typically, people think sharks and shark attacks are the dangers of scuba diving, as sharks are one of the ocean's biggest predators, and the great white shark, in particular, is one of the primary threats to divers. The study proposes a deep learning approach to shark detection for underwater diving surveillance. A large collection of great white sharks' datasets underwater is used by the system for training as sharks are hard to differentiate from other sharks like animals in an underwater environment. A YOLOv3 algorithm that uses convolutional neural networks for object detection, multiscale prediction, and bounding box prediction through the use of logistic regression is used in the study. And with this approach, the testing of the shark detection system generates a good result.