Nino E. Merencilla
10 Publications
Scopus ID: 105030147183
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-5
Conference Paper | Published: December 3, 2025
Abstract
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.
Scopus ID: 85199920699
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Conference Paper | Published: January 1, 2023
Abstract
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.
Scopus ID: 85199907654
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Conference Paper | Published: January 1, 2023
Abstract
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.
Scopus ID: 85130206509
2022 International Conference on Decision Aid Sciences and Applications (DASA), (2022), pp. 1469-1473
Jhon Michael C. Manalo, Alvin Sarraga Alon, ... Ricky C. Sandil
Conference Paper | Published: January 1, 2022
Abstract
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.
Scopus ID: 85105390853
2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), (2021), pp. 384-388
Conference Paper | Published: March 17, 2021
Abstract
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.
Scopus ID: 85127599472
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Conference Paper | Published: January 1, 2021
Abstract
Blindness is an impairment in which the patient requires constant assistance with even the most basic of everyday tasks particularly in travelling alone without occurring accident. The project was designed to improve the level of independence of a visually impaired individual in travelling, utilizing the Smart Stick for the Visually Impaired Person will help them travel in flat and rugged terrain with high level of confidence as not to have accidents or injuries. The smart stick provides an obstacle detector and a speech synthesizer mechanism to guide the individual to certain obstacle and a change in the terrain elevation, it also helps the individual to locate the smart stick easily if they accidentally dropped or misplaced it, the device will create a sound through the buzz module. The following modules were also included; Obstacle detection, Terrain detection, Hand detection, Speech Synthesizer, and Sound module, all of which are connected to the Arduino Nano microcontroller. The prototype was tested considering all modules as mentioned above, having a 95 to 100 percent success rate for 20 testing trials in every module of the system. The study had presented an alternative way for the visually impaired individual to travel safely. The researcher recommended for future enhancement of the device to be paired with smart phones or an application based-GPS for tracking and monitoring.
Scopus ID: 85113487106
2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS), (2020), pp. 1-4
Conference Paper | Published: December 18, 2020
Abstract
The most common cause of injuries in the construction site was caused by falls, slips, and trips. As a response to the Occupational Safety and Health Administration (OSHA), this agency conducted training such as fall prevention. Despite these initiatives, there are still incidents and accidents that happened on the site. According to the study conducted by previous researchers, those fatalities can be reduced by wearing a hard hat. That is why OSHA requires all construction sites to strictly implemented the wearing of hard-hat within the vicinity of the construction site. This study developed a hard hat detection system to determine if the worker is wearing a hard-hat properly. Image processing was used in this study. The proponents used the public datasets with hard hat-wearing images to evaluate the performance by using the mean average precision (mAp) where the proponents obtained an average accuracy of 79.246. The proponents of the detection system of hardhats concluded that regardless of their size, color, types, and angles with an average Training and Validation accuracy of 97.29 and 92.55, average evaluation accuracy of 79.24% with the highest model accuracy of 86.89%, and testing accuracy of 86.67%. The system works properly.
Scopus ID: 85113543634
2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS), (2020)
Conference Paper | Published: December 18, 2020
Abstract
In the Philippines, at least 16 million Filipinos reported smoking cigarettes amid the campaign against tobacco products due to various concerns about their adverse health effects. Due to health, environmental, and safety concerns, the President of the Philippines issued Executive Order 26 s. 2017, imposing a nationwide ban on smoking (use of tobacco including e-cigarettes) in all public places in the Philippines. Despite the implementation of this order, many are still seen smoking in prohibited smoking areas. A smoke detector can be helpful in this situation. This study proposed a smoker detection system that uses a deep learning algorithm that can detect people that are smoking cigarettes. The study used the Pascal VOC format and LabelImg tool for annotating the datasets. Training, validation, and evaluation of the system is done by presenting images, videos, and live detection using the webcam of a camera. Overall, the system produced 90% testing accuracy.
Scopus ID: 85098263057
2020 IEEE 10th International Conference on System Engineering and Technology (ICSET), (2020), pp. 22-25
Aimee G. Acoba, Christopher Franco Cunanan, ... Michael Angelo D. Ligayo
Conference Paper | Published: November 9, 2020
Abstract
The area of information system (IS) has created a whole different environment for information and communication technology (ICT), and the Internet plays a vital role in this work. This paper introduces an IS web-based implementation structure for the judicial system in the Philippines' Regional Trial Court (RTC). The original structure centers on helping the Judicial Officers and the Lawyer workflow. In the other hand, the administrator (authorized person) views other files: status reports, list of cases and list of clients. The research methodology includes an extensive study of information systems in the judiciary system. Two important concepts, Case Information Management System (CIMS) and Electronic Court Record-Keeping (ECRK), form the basis for the initial design. Software and the system architectures are presented.
Scopus ID: 85098261698
2020 IEEE 10th International Conference on System Engineering and Technology (ICSET), (2020), pp. 17-21
Cherry D. Casuat, Julius C. Castro, ... Christina P. Atal
Conference Paper | Published: November 9, 2020
Abstract
Predicting students' employability prior to graduation can be a great tool for every HEI's career center to intervene timely and to take steps on how to improve the weaknesses of the students to become more employable. At present, there is no tool that can be used to determine undergraduate students who are at risk of unemployment or becoming disadvantaged because vulnerabilities are not detected early. In this study the principal component analysis (PCA) and logistic regression were used to determine the most predictive features in the students' employability prediction system (STEPS). The Dataset used consist of 1000 information of engineering students who took their on-the Job training from School year 2017-2018 to School year 2019. The features used were professionalism and branding, confidence, comprehension, communication skills, growth potential, student performance rating. Upon using PCA, the experiments resulted to communication skills growth potential and student performance rating obtained the most predictive attributes that affects the employability prediction.