Ku Nurul Fazira Ku Azir
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Preferred name
Ku Nurul Fazira Ku Azir
Official Name
Ku Azir, Ku Nurul Fazira
Alternative Name
Ku Azir, Ku Nurul Fazira
Fazira Ku Azir, Ku Nurul
Azir, Ku
Azir, Ku Nurul Fazira Ku
Azir, K. N.F.K.
Ku Azir, K. N.F.
Azir, K. N.F.Ku
Main Affiliation
Scopus Author ID
56879016500
Researcher ID
AAY-3466-2021

Research Output

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A general framework for improving electrocardiography monitoring system with machine learning

2019-03-01 , Khairuddin A.M. , Ku Nurul Fazira Ku Azir , Eh Kan P.

As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system.

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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.

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Multi-classification of freshness from leftover-cooked food in Malaysian foods using machine learning

2023 , Wan Nur Fadhlina Syamimi Wan Azman , Ku Nurul Fazira Ku Azir , Amiza Amir , Hamimah Ujir

The objective of this study is to implement machine learning (ML) to identify and classify the level of contamination in leftover cooked foods based on its aroma. An evaluation on the smell profiles using as a model local Malaysian lunch or evening foods that have always been stored as leftover cooked food is done in this study. To capture the data, a simple e-nose application is built and affixed to the food containers, which will accommodate four types of sensors sensitive to different gases and is programmed using the Arduino platform. To determine the aroma categorization of leftover Malaysian cuisine, samples are examined using RStudio. The results in this study demonstrated satisfactory performances by k-Nearest Neighbours (k-NN), Support Vector Machines (SVM), and Random Forest (RF) with accuracies ranging from 87.5% to 100% using the oversampling and undersampling techniques. Unfortunately, Linear Discriminant Analysis (LDA) gave poor performances (19.64% – 58.93%) in classifying the contamination level of the samples. Hence, the results obtained gave an indication that the electronic nose presented in this research was a promising for classification of contamination level for leftover cooked foods, allowing food to be better anticipated as to whether it is still edible or not.

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Footfall shopper traffic counter and information system

2023 , Saidatul Norlyana Azemia , Ku Nurul Fazira Ku Azir , Aisyah Nazirah Razali , Qurratu Ain Qistina , Prisilya Murali , Syed Hassan Alidrus , Norazila Ali

Throughout early 2020 until now the covid 19 pandemic has shown no sign of ending. It has been impactful to most businesses including retailers and malls. Many of them have been forced to shut due to being unable to adapt with the situation and the Covid cases are increasing day by day which leads to the announcement of Movement Control Order (MCO) by the government. Now some places have been allowed to reopen the retailers and malls with the condition that they must follow Covid control measures. As retailers and malls owners grapple with the repercussions of the covid 19 pandemic, many are accelerating their plans and expanding their thinking to find ways to keep malls relevant in the new normal. Here, we proposed to create a footfall shopper traffic count and information system. This system is to measure the flow of traffic at all mall entrances and anchor stores. This system can filter the people who have a higher chance of being infected with coronavirus by detecting human temperature, record the number of shoppers entering and at the same time can limit the shopper number entered daily. Thus, it is to fulfilled the aim of the social distancing and comply with the guideline from the Ministry of Health of Malaysia. This footfall traffic count and information system has been tested several times to ensure that all the components (temperature sensor, IR sensor, lcd, led, etc.) and system flow work correctly. Moreover, this system can still be evolving in the future by adding more features or applying new component innovation for better and faster performance. © 2023 American Institute of Physics Inc.. All rights reserved.

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R-Peaks and Wavelet-Based Feature Extraction on K-Nearest Neighbor for ECG Arrhythmia Classification

2024-01-01 , Khairuddin A.M. , Ku Nurul Fazira Ku Azir , Mohd Rashidi Che Beson

The aim of this research is to classify 17 types of arrhythmias by applying the algorithm developed from combining the morphological and the wavelet-based statistical features. The proposed arrhythmia classification algorithm consists of four stages: pre-processing, detection of R-peaks, feature extraction, and classification. Seven morphological features (MF) that were retrieved from the R-peak locations. Following this, another nine wavelet-based statistical features (SF) were gathered by decomposing wavelets in level 4 from the Daubechies 1 wavelet (Db1). These 16 features are then applied to the k-nearest neighbor (k-NN) algorithm. The accuracy (ACC) of the suggested classification algorithm was assessed by using the MIT-BIH arrhythmia benchmark database (MIT-BIHADB). The experimental results of this work attained an average accuracy (ACC) of 99.00%.

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LaundryMama: humanising laundry tasks using laundry management system and laundry-on-demand mobile applications

2020 , Leong Yi Mei , Ku Nurul Fazira Ku Azir , Siti Zuraidah Ibrahim , Saidatul Norlyana Azemi

Laundry Management System and Laundry-On-Demand Mobile Applications are presented in this paper. Using conventional laundry service method, customer is not informed about the laundry process stage, does not have option to arrange the preferred laundry pick up time for the deliveryman to pick up the unwashed laundry from the address provided by customer and the laundry ordering paper forms are often lost in transit between customer and admin. Therefore, a laundry management system software and laundry on demand mobile application is demanded to solve the problems. The software development is performed using an open source developing platform Android Studio IDE and Firebase Real-time Database, Authentication, Cloud Messaging and Cloud Storage. The method used to develop the software is waterfall modelling and two characters are involved, which is admin and customer. The two characters functions are separated in two different applications. A Laundry Management System Software is developed for admin to manage, make order and monitor the business. A Laundry-On-Demand Mobile Application is developed for customer to make order and monitor the order. These both applications can receive notification from each other. The data can be correctly written and read from Firebase Real-time database, Firebase Authentication, Firebase Cloud Storage. The developed software and mobile application are evaluated in term of its functionality.

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Performance and Stamina Wearable Devices and Monitoring System for Football Players

2023-10-06 , Kamarudzaman M.F. , Ku Nurul Fazira Ku Azir , Siti Zuraidah Ibrahim , Ujir H.

Analysing and monitoring the performance of football players is not something new in the world of football. Having to analyse and monitor the player’s performance, the coaching staff and manager can see the player’s development and at the same time the training drills can be more effective. There are three main player’s attributes that coaching staff and manager need to monitor, which are speed, acceleration, and stamina. The system available today does not represent the player’s performance for each of the attributes in terms of values. This project is aiming to design and develop a system that can analyse and monitor the football player’s performance using micro-electronic technology (MEM) like accelerometer and gyroscope. There will be two microcontrollers that are responsible for controlling the interaction between other components and uploading the collated data into cloud storage. This project will use the ThingSpeak platform to generate the player’s development graph. This platform is available in the form of websites and smartphone applications. By implementing IoT into the system, the coaching staff and manager can monitor the player’s development anywhere at any time. The system is successfully read and analysed the player’s attributes performance for speed, acceleration, and stamina in form of values and graphs where at the same time the project is also capable of analysing the player’s penalty kick technique.

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Ambient Cues of Kitchen Counter in Guiding Cooking Activities for Alzheimer's Patient

2019-03-01 , Basharudin N.W. , Ku Nurul Fazira Ku Azir , Khairuddin A.M. , Ehkan P.

Loss of memory is among the first symptoms reported by patients suffering from Alzheimer's disease (AD). The individual pattern of impaired memory functions correlates with functional brain integrity will result in the inability of the patient to complete a particular task. This research focuses on designing the ambient cues in the kitchen counter to guide Alzheimer's patient to complete cooking activities based on the concept of Stimulus-Response Compatibility (SRC) and whereas the activity is focus on the sequential workflow of cooking activities based on the Hierarchical Task Analysis (HTA). Wizard-of-Oz is used to simulate the behavior of theoretical intelligent ambient cues. The results show with the aid of ambient cues in the kitchen, patient be able to complete the task until the end even if there is confusing or disruption in the middle of the activity. The effectiveness of ambient cues show that patient can easily understood the cues of guiding the cooking flow when mistakes happened.

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E-Nose: spoiled food detection embedded device using machine learning for food safety application

2024 , Wan Nur Fadhlina Syamimi Wan Azman , Ku Nurul Fazira Ku Azir , Adam Mohd Khairuddin

This research aims to employ machine learning (ML) to classify the degree of contamination in leftover cooked foods based on their smell. This study evaluates the odour characteristics of typical leftover cooked lunch or dinner meals that are consumed locally in Malaysia. An easy-to-use e-nose application was attached to the food containers, consisting of four different types of sensors sensitive to various gases, to collect the data. RStudio is used to analyze samples in order to identify the odour classification of leftover Malaysian food. The accuracy ranged from 90% to 100% when using the oversampling and undersampling techniques. The results of this re-search showed satisfactory performances by Support Vector Machines (SVM) is superior compared to that of k-Nearest Neighbours (k-NN) in classifying the samples’ contamination degree. As a result, the findings showed that the electronic nose used in this study was a promising method for classifying the degree of contamination in leftover cooked foods and predicting whether food is still edible or not. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Low cost autonomous robot cleaner using mapping algorithm based on Internet of Things (IoT)

2020 , R J Ong , Ku Nurul Fazira Ku Azir

The objective of this study is to design and develop a low-cost Arduino autonomous robot cleaner using mapping algorithm to clean floor area of houses or offices. The idea is basically to detect any obstacles with the help of sensor and sent its output to microcontroller that will control the autonomous vacuum cleaner movement. A low-cost solution is proposed in this study by using HC-SR04 ultrasonic sensor for obstacle avoidance and control by Arduino UNO. By using an autonomous vacuum cleaner, user can turn ON the autonomous vacuum robot to clean without any help of a human operator. Wall mapping and random mapping is being applied in this study to find the effective mapping algorithm for autonomous robot cleaner. Additionally, instead of using the traditional button or switch to activate the robot, voice recognition through Google Assistant implemented in this project. Hence, this provides a more user friendly platform not only for normal user but also help visually impaired people to activate the robot.

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