Ruzelita Ngadiran
Thumbnail Image
Preferred name
Ruzelita Ngadiran
Official Name
Ruzelita, Ngadiran
Alternative Name
Ngadiran, Ruzelita
Ngadiran, R.
Binti Ngadiran, Ruzelita
Main Affiliation
Scopus Author ID
36548898800
Researcher ID
ABF-3045-2020

Research Output

Filters

6
6
Reset filters

Settings
Now showing 1 - 6 of 6
  • Publication
    IOT Smart Guidance Parking Search System for Open Space Parking Area
    ( 2021-07-26)
    Iszaidy Ismail
    ;
    Ruzelita Ngadiran
    ;
    Nazren A.R.A.
    ;
    Wafi N.M.
    ;
    Ramli N.
    ;
    Vijayasarveswari Veeraperumal
    ;
    Leow W.Z.
    Open parking facilities can be automated and parking spaces can be easily operated by the implementation of IoT technology (Internet of Things). In this article, we present the evolution and prototyping of the open space smart guidance-parking search system, an IoT-based smart parking search system. The Smart Guidance Parking Searches System consists of i) An IOT module to monitor the availability of a parking slot and to update the parking lot status; and (ii) A web-based software allows users to view parking spaces available for a specific open space area. This paper addresses the existing system, device description, its functional specifications, the methods, and technologies used, the development/deployment of prototypes, along with the findings from the demonstration. This device serves as a guide for the user/driver to search for the parking slot occupancy in open/outdoor environments.
  • Publication
    Tomato Diseases Classification Using Extreme Learning Machine
    ( 2023-10-06)
    Xian T.S.
    ;
    Ruzelita Ngadiran
    ;
    Iszaidy Ismail
    ;
    Amiza Amir
    ;
    Siti Zuraidah Ibrahim
    ;
    Taha T.B.
    Plant disease classification is essential to the agriculture industry. In this work, a tomato disease classification using Extreme Learning Machine (ELM) with image-based features. Extreme Learning Machine (ELM), a classification algorithm with a single layer feed-forward neural network where it can be combined with few activation functions. The image features as the input where the image is pre-processed via HSV colour space and extracted using Haralick textures, colour moments and HSV histogram. The features are then loaded in the ELM classifier to perform the ELM training and testing. The accuracy result of ELM classification is then analysed after the validation. The dataset used for disease detection is tomato plant leaves which is a subset of the Plant-Village dataset. The results produced from the ELM demonstrate an accuracy of around 84.94% which is comparable to classifiers such as the Support Vector Machine and Decision Tree.
  • Publication
    An Analysis of Background Subtraction on Embedded Platform Based on Synthetic Dataset
    ( 2021-03-01)
    Iszaidy Ismail
    ;
    Ruzelita Ngadiran
    ;
    R Badlishah Ahmad
    ;
    Ramli N.
    ;
    Jais M.I.
    ;
    Vijayasarveswari Veeraperumal
    Background subtraction is a preliminary technique used for video surveillance and a widely used approach for indexing moving objects. Arange of algorithms have been introduced over the years, and it might be hard to implement the algorithms on the embedded platform because the embedded platform comes up with limited processing power. The goal of this study is to provide a comparative analysis of available background subtraction algorithms on the embedded platform:-Raspberry Pi. The algorithms are compared based on the segmentation quality (precision, recall, and f-measure) and hardware performance(CPU usage and time consumption) using a synthetic video from BMC Dataset with different challenges like normal weather, sunny, cloudy, foggy and windy weather.
  • Publication
    Threading implementation on different hardware for travel time estimation purpose
    ( 2017-03-06)
    Iszaidy Ismail
    ;
    Ruzelita Ngadiran
    ;
    R Badlishah Ahmad
    ;
    Mohd Ilman Jais
    ;
    Shuhaizar Daud
    The travel time estimation is one of traffic management system which provide time taken from one point to another point. Travel time estimation system consists of an embedded platform with image sensor for detecting and tracking the vehicle. Due to limited resources of embedded board, it makes challenging to measure the travel time especially for fast moving vehicle. Capturing system required a high capturing rate of the camera to capture most current frame for fast moving vehicle. Threading is implemented in this system to improve embedded board resource utilization and input-output latency between camera and embedded board. In this paper, the threading technology is applied to two types of Raspberry Pi model and the performance of the embedded board is recorded and analyzed.
  • Publication
    Video size comparison for embedded vehicle speed detection & travel time estimation system by using Raspberry Pi
    ( 2017-03-06)
    Iszaidy Ismail
    ;
    Alias A.
    ;
    Ruzelita Ngadiran
    ;
    R Badlishah Ahmad
    ;
    Mohd Ilman Jais
    ;
    Shuhaizar Daud
    As traffic continues to grow up, the issue regarding the road accident also growing quickly. The accident occurred due to the high speed of vehicles on the road. This paper proposed a vehicle speed detection and travel time estimation system using Raspberry Pi to estimate the speed of passing vehicles through this system. The system is designed to detect the moving vehicles and calculate its velocity. The system used OpenCV as an image processing software to detect and track the moving vehicles. Several types of capturing size of the video are used in this system to check and measure the performance of the embedded board.
  • Publication
    Background Subtraction Algorithm Comparison on the Raspberry Pi Platform for Real Video Datasets
    ( 2022-01-01)
    Iszaidy Ismail
    ;
    Ruzelita Ngadiran
    ;
    Ramli N.
    ;
    Nazren A.R.A.
    ;
    Nasruddin M.W.
    ;
    Jais M.I.
    Background subtraction is an advanced method used for video monitoring and is commonly used for indexing of moveable objects. Over the years, several algorithms have been implemented and the implementation of algorithms on the embedded platform can be difficult because the embedded platform has minimal computing resources. The purpose of this study is to conduct a comparative review of background subtraction algorithms available on the embedded platform: Raspberry Pi. The algorithms are compared using a real video dataset based on segmentation accuracy (precision, recall, and f-measure) and hardware efficiency (CPU utilization and time consumption).