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All Journal Journal of Information Systems and Informatics Innovation in Research of Informatics (INNOVATICS) Journal of Technology and Informatics (JoTI) Internet of Things and Artificial Intelligence Journal Journal of Technology Informatics and Engineering International Journal of Artificial Intelligence and Science
Umoren, Imeh
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Journal : Journal of Information Systems and Informatics

 1 
Towards Sustainable Smart Living: Cloud-Based IoT Solutions for Home Automation Etuk, Ubong E; Omenaru, Gabriel; Inyang, Saviour Joshua; Umoren, Imeh
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.621

Abstract

In recent times, the realm of home automation systems has garnered significant attention, thanks to the ever-evolving landscape of communication technology. The concept of a smart home, essentially an application of the Internet of Things (IoT), leverages the power of the internet to oversee and employ household appliances through a sophisticated automation infrastructure. Nevertheless, challenges persist within the existing home automation systems, such as constrained wireless transmission reach, a deficiency in backup power management, and the substantial financial outlay involved. Addressing these limitations, our study introduces an economical and resilient solution that combines cloud based IoT with an uninterrupted power management system, making a cutting-edge home automation prototype. This system relies on a microcontroller unit, specifically the ESP-32, which functions as a Wi-Fi-enabled gateway for connecting a variety of sensors and transmitting their data to the Blynk IoT cloud server. The data assembled from a multitude of sensors, including vibration sensors and voltage detectors, becomes readily accessible on users' devices, be it smartphones or laptops, irrespective of their geographical location. The system is further strengthened by a set of relays that link the ESP-32 with household appliances, allowing for centralized control. Structurally, the design uses a control box that can be seamlessly integrated into a real home environment, offering the means to both monitor and govern an array of household devices. This IoT-based home automation solution not only efficiently manages internet-connected appliances but also provides an effective emergency power management system, enabling remote initiation and deactivation of backup generators. It represents a innovative leap in the evolution of home automation systems, steering in convenience, efficiency, and cost-effectiveness.
Realtime-Based System for Facemask Detection Using PCA, with CNN and COCO Model Etuk, Ubong; Umoren, Imeh; Umoren, Odudu; Inyang, Saviour
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.759

Abstract

The instant spread of COVID-19 has underscored the need for effective measures such as wearing face masks to control transmission. As a response, facemask detection systems using advanced machine learning techniques have become essential for ensuring compliance and public safety. This research focused on developing a system for detecting facemask usage using a hybridized approach comprising of Convolutional Neural Networks (CNN), Principal Component Analysis (PCA), and the Common Objects in Context (COCO) model. A hybridized detection model is often explored to enhance the precision and efficiency of previous methods that leveraged traditional machine learning or deep learning for the same task. Hence, this system effectively identifies whether individuals are properly wearing masks, not wearing masks at all, or wearing masks improperly from images and real-time video streams using bounding boxes. The results demonstrate that the hybrid approach achieves high accuracy in detecting various facemask conditions across different scenarios. Evaluation metrics such as Average Precision (AP) and Average Recall (AR) indicate the model's robustness, with a reported AP value of 70% and an AR value of 81%, primarily evaluated on larger objects within images. Further evaluations involving different individuals and types of facemasks revealed variability in detection accuracy, highlighting the model's effectiveness and areas for improvement. Nevertheless, the development and deployment of facemask detection systems are crucial for managing public health and ensuring safety in the face of ongoing and future pandemics.
Co-Authors Afolorunso, Adenrele Akpanobong , Aloysius Akwang, Nse Ansa, Godwin E. Etuk, Ubong Edet, Anthony Essien, Daniel Etuk, Ubong Etuk, Ubong E Etuk, Ubong E. Inyang, Saviour Inyang, Saviour Joshua James, Gabriel Omenaru, Gabriel Udonna, Uduakobong Umoren, Odudu