Vijayasarveswari Veeraperumal
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Vijayasarveswari Veeraperumal
Official Name
Veeraperumal, Vijayasarveswari
Alternative Name
VEERAPERUMAL, V.
Main Affiliation
Scopus Author ID
57226571921
Researcher ID
EDU-3323-2022

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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
    Class Attendance System Using Viola-Jones Algorithm and Principal Component Analysis
    ( 2021-07-26)
    Yaakub N.D.A.
    ;
    Nasrudin M.W.
    ;
    Vijayasarveswari Veeraperumal
    ;
    Ismail I.
    ;
    Yob R.C.
    ;
    Zhe L.W.
    ;
    Wan Azani Wan Mustafa
    Face recognition is one of the numerous biometrics approaches that can be implemented by smart and automated attendance management systems. The individual identity can be determined by the unique representation of the face structure of each individual face and it cannot be lost, stolen, or reproduced in the same way as other types of identification. Thus, this work is motivated to propose a class attendance system based on face recognition. With current approaches such as passwords, access cards, and identification numbers, face recognition can be used to prevent theft and fraud which can significantly reduce the chances of system hacking. In this proposed work, initially, video framing has been implemented by activating the Universal Serial Bus (USB) camera through a user-friendly interface which was created with Graphical user interfaces (GUI) in the MATLAB software. The image of each student's face that was snapped by using the USB camera will be stored in a dataset. The dataset then will be divided into the training set and testing set. In the detection process, the Viola-Jones algorithm is utilized to detect and segment the image of student's face from the video frame. Next, the scaling of the size of the images is carried out to prevent the loss of information in the pre-processing phase. Then, the Principal Component Analysis (PCA) is utilized in the face recognition process in order to extract the features from facial images.
  • Publication
    Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver
    ( 2022-11-01)
    Halim A.A.A.
    ;
    Allan Melvin Andrew
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Najib Mohd Yasin
    ;
    Muzammil Jusoh
    ;
    Vijayasarveswari Veeraperumal
    ;
    Abd Rahman M.A.
    ;
    Zamin N.
    ;
    Mary M.R.
    ;
    Khatun S.
    Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.
  • 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
    An IoT-based automated gate system using camera for home security and parcel delivery
    ( 2024-02-08)
    Mohd Wafi Nasrudin
    ;
    Jamaluddin A.F.
    ;
    Ismail I.
    ;
    Norfatihah Bahari
    ;
    Vijayasarveswari Veeraperumal
    ;
    Wan Azani Wan Mustafa
    ;
    Abdul Syafiq Abdull Sukor
    ;
    Abdul Rahim A.N.
    The Internet of Things (IoT) has made it possible to set up smart home security and parcel delivery. Therefore, this work proposed an automated gate system using camera for home security and parcel delivery with integrated Internet of Things (IoT). An automated gate system will capture and identify the image of face visitors and delivery riders for admin authentication to open the gate and parcel box. This proposed work is controlled and monitored through mobile apps. The primary purpose and inspiration of this work are to help the delivery rider put the parcel into the parcel box provided if there is no person in the house, and the owner can pick up the parcel without being broken or robbed when she/he comes back home. When the delivery rider presses the button near the gate, the admin will receive the notification "Someone coming,". The admin will click the "okay"button and the system will take a picture using the camera in Blynk App. After the admin verifies that is the delivery rider, the admin will open the box and the delivery rider can access the parcel door box and put the goods inside the box. Another advantage of this work, it also allows familiar people to access our home. The same process with the delivery rider where the visitor needs to press the bell and the admin needs to verify before the visitor can access the single gate. The result indicates that this work is able to monitor and control the gate and parcel door box using an IoT application.
  • Publication
    UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set
    ( 2023-01-01)
    Ahmad Ashraf Abdul Halim
    ;
    Vijayasarveswari Veeraperumal
    ;
    Allan Melvin Andrew
    ;
    Mohd Najib Mohd Yasin
    ;
    Mohd Zamri Zahir Ahmad
    ;
    Hossain K.
    ;
    Bari B.S.
    ;
    Fatinnabila Kamal
    Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.
      1
  • Publication
    Smart Management Waiting System for Outpatient Clinic
    ( 2023-02-01)
    Mohd Wafi Nasrudin
    ;
    Zainuddin N.S.A.
    ;
    Raja Abdullah Raja Ahmad
    ;
    Rashidah Che Yob
    ;
    Mohd Zamri Zahir Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Vijayasarveswari Veeraperumal
    ;
    Zulkifli N.D.M.
    Queuing has become a common occurrence in malls, train stations, and others. Queuing especially in healthcare intuitions has become a center of attraction because of the long waiting time either at the registration or in receiving treatments. Therefore, in solving this problem, a smart management waiting system for outpatient clinics is developed by using AppGyver and Backendless as the data storage. This system will be operating by QR code scanning for administrators to obtain patients’ personal information before patients obtain the queue number via MyQUEUE mobile application (patients’ interface). By providing queue numbers through the mobile application, patients don’t have to wait in a small uncomfortable waiting lounge instead patients can wait at their desired places such as cafeteria, in their car, and others. Patients also don’t have to worry about missing their turns because there will be a 10 minutes reminder before their turn. Other than that, there is a feature that digitalized the appointment details which means patients don’t have to worry about missing their appointment book or card. The performance of both systems which are the patients’ interface and administrators’ interface is successfully designed and the output obtained. The administrator is able to assign queue numbers, notify patients 10 minutes before consultation time, and assign follow-up appointments to patients.
      1
  • Publication
    Existing and emerging breast cancer detection technologies and its challenges: A review
    ( 2021-11-01)
    Ahmad Ashraf Abdul Halim
    ;
    Allan Melvin Andrew
    ;
    Mohd Najib Mohd Yasin
    ;
    Abd Rahman M.A.
    ;
    Muzammil Jusoh
    ;
    Vijayasarveswari Veeraperumal
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Illahi U.
    ;
    Abdul Karim M.K.
    ;
    Scavino E.
    Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.
      1
  • Publication
    Erratum: Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction (PLoS ONE (2020) 15:8 (e0229367) DOI: 10.1371/journal.pone.0229367)
    ( 2021-05-01)
    Vijayasarveswari Veeraperumal
    ;
    Allan Melvin Andrew
    ;
    Muzammil Jusoh
    ;
    R Badlishah Ahmad
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Najib Mohd Yasin
    ;
    Sabira Khatun
    ;
    Hasliza A Rahim @ Samsuddin
    The authors are listed out of order. Please view the correct author order, affiliations, and citation here: V. Vijayasarveswari1, A.M. Andrew1, M. Jusoh1, R.B. Ahmad1, T. Sabapathy1, R.A.A. Raof1, M.N.M. Yasin1, S. Khatun2, H.A. Rahim1 1 Advanced Communication Engineering (ACE) Centre of Excellence, Universiti Malaysia Perlis, Kangar, Perlis, West Malaysia, 2 Faculty of Electrical & Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang Vijayasarveswari V, Andrew AM, Jusoh M, Ahmad RB, Sabapathy T, Raof RAA, et al. (2020) Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction. PLoS ONE 15(8): e0229367. https://doi.org/10.1371/journal.pone.0229367 There are errors in the Funding statement. The correct Funding statement is as follows: The study was supported by Fundamental Research Grant Scheme (FRGS), Ministry of Education Malaysia under grant number: FRGS/1/2019/TK04/UNIMAP/02/3. No additional external funding was received for this study.
      1