Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add filters

Database
Language
Document Type
Year range
1.
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1788670

ABSTRACT

Covid-19 has been a serious issue in the Philippines for the past two years. Its spread has taken a toll on the country's economy and society. Furthermore, the populous has been suffering throughout the pandemic as new cases and deaths are increasing. These massive problems warrant research on modelling and predicting this pandemic. Although there are numerous research with regards to using statistical modelling, Machine learning, deep learning, and artificial intelligence to model and understand the pandemic throughout the world, few pieces of researches focus on the Philippines alone. In addition to that, simple models are seen to fit the Covid-19 data more than complex ones. With these in mind, the authors fit and modelled Philippine new cases of Covid-19 using Sklearn Polynomial and MLP regressors. It was found out that Polynomial models fit the entire dataset from January 2020 to September 2021, but MLP model fits the recent September 2021 data better. Further research using different countries as case studies or different models is recommended. © 2021 IEEE.

2.
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1788666

ABSTRACT

Human-computer interaction (HCI) focuses on the interaction between humans and computers and it exists ubiquitously in our daily lives, especially in post COVID era where non-face-to-face interaction is common. Since HCI usually uses a physical controller such as a mouse or a keyboard, it hinders National User Interface, giving a middle ground between the user and the computer. This paper presents a vision-based hand tracking system development for non-face-to-face interaction, which aims to improve HCI by being able to track the hand which will act as the pen and functioning as a reusable writing surface for creating texts, drawings, and such as well as removing or erasing using the user's hand as the pen, and utilizing Open Computer Vision Library (OpenCV) and Mediapipe. Using the computer's camera the hand will be tracked as the pen for creating basic drawings and handwriting. The vision-based board where the user can draw on and the pen or marker will be the user's hand. The results indicate that this system is accurate enough to be a feasible application for handwriting ad basic drawings. © 2021 IEEE.

SELECTION OF CITATIONS
SEARCH DETAIL