Ruzelita Ngadiran
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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

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  • 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
    Perceptual-based features for blind image quality assessment using extreme learning machine for biodiversity monitoring
    ( 2024-02-08)
    Verak N.
    ;
    Pakhlen Ehkan
    ;
    Ruzelita Ngadiran
    ;
    Jungjit S.
    ;
    Fazrul Faiz Zakaria
    ;
    Mohd Nazri Mohd Warip
    ;
    Ilyas M.Z.
    Blind Image Quality Assessment (BIQA) is crucial for various image processing applications, including image denoising, transmission evaluation, optimization, watermarking, and situations where a reference image is unavailable. However, existing state-of-the-art Image Quality Assessment (IQA) metrics are often specific to certain types of distortion and fail to align with human perception. To address this issue, our research study proposes a novel approach that incorporates perceptual-based features and utilizes a pooling algorithm based on Extreme Learning Machine (ELM). By considering human visual characteristics and the impact on image content, we aim to mimic human perception, which can detect noticeable differences at specific frequency ranges, akin to neurons. The work is divided into three phases. In the first phase, we derive perceptual features using lifting wavelet, focusing on texture, edge, and contrast components. Subsequently, in the second phase, these features are trained to generate output for pooling using Extreme Learning Machine (ELM). The pooling strategy of ELM is chosen due to its ability to overcome limitations found in previous pooling techniques like Neural Networks (NNs) and Support Vector Regression (SVR). This approach enables us to evaluate the quality score of images accurately, benefiting from ELM's superior learning accuracy and faster learning speed. The third phase involves performance evaluation, including statistical analysis for algorithm validation and a comparison with existing BIQA methods using MATLAB software. We verify our proposed approach on diverse image databases containing various distortion types, aiming to create a general-purpose BIQA solution. The outcomes of this work will have significant implications in several image processing applications, such as optimizing image enhancement for medical purposes like tumor or cancer detection, image watermarking for security applications, image coding and compression, and image forensic analysis. In biodiversity monitoring, image enhancement plays a crucial role in tracking and data collection.
  • Publication
    IoT-based Door Access Using Three Security-Layers
    ( 2023-10-06)
    Aznan M.A.
    ;
    Siti Zuraidah Ibrahim
    ;
    Ku Nurul Fazira Ku Azir
    ;
    Ruzelita Ngadiran
    ;
    Mohd Noh F.H.
    This paper demonstrates an Internet-of-Things, IoT-based Door Access using three security layers, which are biometric identification, authentication, and authorized reply. The IoT-based door access is developed with Closed Circuit Television (CCTV) monitoring to control door access by authorized users using facial recognition technique and the Telegram application along with a database to record user logs. In the first security layer, the user’s face will be captured by CCTV camera and then processed to match to the registered face. In the authentication layer, the system will use Telegram Bot to send a message to the user registered Telegram Chat Identification (ID) only for entering the password. In the third security layer, if the password is valid, the system will send a signal to the hardware to unlock the door. The results showed that the developed prototype of this system successfully operated as expected.
  • 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
    Design and Implementation of Lifting Wavelet Transform Using Field Programmable Gate Arrays
    ( 2020-03-20)
    Taha T.B.
    ;
    Ruzelita Ngadiran
    ;
    Phak Len Al Eh Kan
    Lifting Wavelet transform (LWT) has an extensive usage in different image processing applications as image compression and information hiding. LWT is considered a good solution for hardware designs as it relies only on integer calculations while applying the wavelet transform. In this paper, an FPGA design and implementation of LWT is presented, the implementation is achieved using VHDL coding without importing off-shelf components which make the proposed design applicable to a wide range of devices. The design is based on parallel execution to perform LWT implementation with real time response. The design utilized 421 logic registers of DE2 Cyclone II (EP2C35F672C6) FPGA device with 151.91MHz frequency.
      2  15
  • 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).
      1  38
  • 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.
      34  9
  • Publication
    A hybrid modified sine cosine algorithm using inverse filtering and clipping methods for low autocorrelation binary sequences
    ( 2022-01-01)
    Rosli S.J.
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Khairul Najmy Abdul Rani
    ;
    Ruzelita Ngadiran
    ;
    Wan Azani Wan Mustafa
    ;
    Abdulmalek M.
    ;
    Mohd Najib Mohd Yasin
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Wan Nur Suryani Firuz Wan Ariffin
    ;
    Alkhayyat A.
    The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains' properties shows that a class of signals is mainlywell suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximumpossibleMF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) using Inverse Filtering (IF) and clipping method to achieve better results. The proposed algorithms, LABS-IF and HMSCACSA-IF, achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237, respectively, where the optimal solutions belong to the skew-symmetric sequences. TheMFoutperformed up to 24.335% and 2.708% against the state-of-the-art LABS heuristic algorithm, xLastovka, and Golay, respectively. These results indicated that the proposed algorithm's simulation had quality solutions in terms of fast convergence curve with better optimal means, and standard deviation.
      2  21
  • Publication
    A hybrid modified method of the sine cosine algorithm using latin hypercube sampling with the cuckoo search algorithm for optimization problems
    ( 2020-11-01)
    Rosli S.J.
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Khairul Najmy Abdul Rani
    ;
    Ruzelita Ngadiran
    ;
    R Badlishah Ahmad
    ;
    Yahaya N.Z.
    ;
    Abdulmalek M.
    ;
    Muzammil Jusoh
    ;
    Mohd Najib Mohd Yasin
    ;
    Thennarasan Sabapathy
    ;
    Allan Melvin Andrew
    The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.
      4  21