Muhamad Khairul Ali Hassan
Thumbnail Image
Preferred name
Muhamad Khairul Ali Hassan
Official Name
Muhamad Khairul , Ali Hassan
Alternative Name
Hassan, Muhamad Khairul Ali
Hassan, M. K.Ali
Khairul Ali Hassan, Muhamad
Hassan, M. K.A.
Ali Hassan, M. K.
Main Affiliation
Scopus Author ID
55537856900
Researcher ID
GSD-4118-2022

Research Output

Filters

13
Reset filters

Settings
Now showing 1 - 10 of 13
  • Publication
    Design of a portable continuous systolic blood pressure monitoring kit with built-in low and high blood pressure early warnings
    ( 2009)
    Muhamad Khairul Ali Hassan
    About one in three adults in the United States have high blood pressure but high blood pressure itself usually has no symptoms. The prevalence of hypertension in Malaysians aged 30 years and above was 42.6%. The majority of cases (64%) in this country remain undiagnosed. Only 26% of Malaysian patients achieved blood pressure control (<140/90 mmHg). Now days, many people have high blood pressure for years without knowing it. Uncontrolled high blood pressure can lead to stroke, heart attack, heart failure or kidney failure. This is why high blood pressure is often called the "silent killer." The only way to tell if you have high blood pressure is to have your blood pressure checked. Blood pressure is often measured using a device called a sphygmomanometer, a stethoscope and a blood pressure cuff. Almost all the existing manual or automatic measuring techniques of blood pressure are based on this principle, which is not convenient for continuous monitoring of blood pressure. The objective of this study is to develop a portable continuous blood pressure monitoring system using an electrocardiography (ECG) sensor and a pulse sensor. Two methods were used to measure blood pressure continuously. The first method measures blood pressure continuously based on Heart Rate (HR) and the second method is based on Pulse Wave Transit Time (PWTT). Both methods were separately implemented for different techniques to measure systolic blood pressure (SBP). The techniques that were used to model the relationship between the PWTT or HR data to systolic blood pressure are linear regression model, non- linear regression model and neural network model. Neural network model gave the smallest value of mean of error and standard deviation of error for measuring blood pressure based on PWTT or HR. These errors are acceptable and relatively small compared to the standard accuracy, which should have a minimum mean of error value of 6 mmHg with a standard deviation of error of ±10mmHg. The subjects that were involved in portable BP monitoring kit testing are normal blood pressure subjects, low blood pressure subjects and high blood pressure subjects. All the data were taken about five minutes for each subject and the results were monitored by medical cardiologist/doctor or nurses. The accuracy of the SBP data from portable continuous BP monitoring kit was validated using sphygmomanometer. The results indicate that the developed portable BP system is adequate to be used for monitoring or measuring systolic blood pressure continuously. Warning system was developed in this portable BP monitoring kit. The warning system is generated based on blood pressure value and trend of increasing or decreasing of systolic blood pressure values. The warning is given in form of alarm. The alarm will be “on” when the systolic blood pressure value goes more than 140mmHg (High Blood Pressure) or less than 100mmHg (Low Blood Pressure) or if the SBP increasing or decreasing trend in more than 5mmHg for each 30 seconds.
  • Publication
    Performance analysis of diabetic retinopathy detection using fuzzy entropy multi-level thresholding
    (Elsevier Ltd, 2023)
    Mohammed Saleh Ahmed Qaid
    ;
    Shafriza Nisha Basah
    ;
    Khairul Salleh Basaruddin
    ;
    Haniza Yazid
    ;
    Muhamad Khairul Ali Hassan
    ;
    Mohd Hanafi Mat Som
    Diabetic Retinopathy (DR) is one of the major causes of blindness. Many DR detection systems were developed to segment and determine the type and number of lesions that appeared on retinal images and used to classify DR and its severity level. Even though several researchers have already proposed many automated diagnosis systems with different image segmentation algorithms, their accuracy and reliability are generally unexplored. The accuracy of an automated diagnosis system usually depends on the segmentation techniques. The accuracy of this system is heavily dependent upon the retinal and image parameters, which have intensity level difference between background (BG)-blood vessels (BV), BV-bright lesions, BV-dark lesions, and noise levels. In this work, the automated diagnosis system accuracy has been analysed to successfully detect DR and its severity levels. The focus is on fundus image modalities segmentation based on fuzzy entropy multi-level thresholding. The analysis aimed to develop conditions to guarantee accurate DR detection and its severity level. Firstly, a retinal image model was developed that represents the retina under the variation of all retinal and image parameters. Overall, 45,000 images were developed using the retinal model. Secondly, feasibility and consistency analysis were performed based on a specific design Monte Carlo statistical method to quantify the successful detection of DR and its severity levels. The conditions to guarantee accurate DR detections are: BG to BV > 30% and BV to the dark lesions (MAs) >15% for mild DR, BG to BV > 40% and BV to the dark lesions (MAs and HEM) > 20% for moderate DR, and BG to BV > 30% and BV to the dark lesions (MAs and HEM) > 15%, and BV to the bright lesions (EX) > 55% for severe DR. Finally, the validity of these conditions was verified by comparing their accuracy against real retinal images from publicly available datasets. The verification results demonstrated that the condition for the analysis could be used to predict the success of DR detection.
  • Publication
    Aquaculture monitoring system using multi-layer perceptron neural network and adaptive neuro fuzzy inference system
    ( 2024-01-01)
    Abu Hassan Abdullah
    ;
    Sukhairi Sudin
    ;
    Saad F.S.A.
    ;
    Muhamad Khairul Ali Hassan
    ;
    Muhammad Imran Ahmad
    ;
    bin Abdul Khalid K.A.
    The water quality is the most important parameter for aquatic species health and growth. The condition is very critical and is essential to monitor continuously. Poor water quality will affect health, growth and ability of the animal to survive. These also affected their harvesting yields based on the amount and size of the animal. The main water parameters such dissolved oxygen (DO), pH, temperature, salinity and turbidity are monitored and control for good water quality. The data were acquired by the developed instrument and send wirelessly through GPRS/GSM module to cloud-based database. The data were retrieved and the water quality is predicted using fuzzy logic and multi-layer perceptron. MATLAB software was used for the model which is developed based on Mamdani fuzzy interface system. The membership functions of fuzzy were generated, as well as the simulation and analysis of the water quality system. Results show that the performance of fuzzy method can improve system performance in monitoring the water quality. This system also provides alert signals to farmers based on specific limit value for the water quality parameters. This will help the breeders to make certain adjustment to ensure suitable water quality for the aquaculture system.
      1  42
  • Publication
    An Overview of Medical Applications in Meningitis Detection
    ( 2020-07-09)
    Abdulrahman Ahmed A.
    ;
    Zulkarnay Zakaria
    ;
    Hamood Ali M.
    ;
    Pusppanathan J.
    ;
    Zarina Mohd Mhji S.
    ;
    Anas Mohd Noor
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Muhamad Khairul Ali Hassan
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Ahmad Faizal Salleh
    Meningitis remains one of the common infections among young children with high morbidity and mortality rates. In Southeast Asian, only few studies were reported published which evaluated meningitis clinically in the last two decades. Similarly, few studies in Malaysia evaluated meningitis among adolescents and children. Globally, more than one million cases with 135,000 deaths has been recorded yearly, and in Malaysia, severe neurological complications occurs in 9-25% of cases which affirms the most serious risk manifests from bacterial meningitis. Therefore, early detection and effective treatment are required before the irreversible damages occur. This paper reviews the current states and perspectives of diagnostic techniques on meningitis detection. Currently, there are three diagnostic techniques available for meningitis detection, such as blood cultures, spinal tap (lumbar puncture), and imaging techniques (CT scan, MRI, EIT, Ultrasonography, Nuclear imaging and X-ray). However, these techniques have limitations that may limit the chances of carrying out the early detection of the disease. The essence of this review is that meningitis requires an effective technique that is capable of carrying out the early detection of the disease by differentiating normal people and Meningitis infected patients so as to promote longevity worldwide. In this review magnetic induction tomography (MIT) technique is proposed to diagnose meningitis earlier as it is non-intrusive, non-invasive, contactless, and electrode-less imaging technique which does not expose the patients to a harmful radiation.
      26  6
  • Publication
    An IoT Agricultural System for Harumanis Farm
    ( 2021-12-01)
    Ismail F.A.
    ;
    Muhamad Khairul Ali Hassan
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Khairul Salleh Basaruddin
    ;
    Abdul Halim Ismail
    ;
    Mohd Nasir Ayob
    Internet of Things (IoT) is a revolutionary technology that represents the future of communication and computing. The field of IoT implementation is vast and can be applied in every field. This project is about to develop an IoT system for Harumanis Farm as agriculture is becoming an essential growing sector throughout the world due to the increasing population. The major challenge in the Harumanis sector is to improve the productivity and quality of Harumanis without continuous manual monitoring. IoT improves crop management, cost-effectiveness, crop monitoring and also improves the quality and quantity of the crop. This IoT system completes with several sensors to monitor the Harumanis farm, such as temperature and humidity sensor, pH level sensor, soil moisture sensor, also nitrogen, phosphorous, and potassium (NPK) sensor. The system is a simple IoT architecture where sensors collect information and send it over the Wi-Fi network to the mobile applications.
      1  34
  • Publication
    Potential of Near-Infrared (NIR) spectroscopy technique for early detection of Insidious Fruit Rot (IFR) disease in Harumanis mango
    ( 2021-12-01)
    Noor Shazliza Zakaria
    ;
    Abu Hassan Abdullah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Muhamad Khairul Ali Hassan
    ;
    Mohd Nazri Abu Bakar
    ;
    Saad A.R.M.
    ;
    Sukhairi Sudin
    ;
    Ibrahim M.F.
    Harumanis mango 'Insidious Fruit Rot'(IFR), is one of the common issues that hampered the fruit quality and consequently lowered the premium value of Harumanis Mango. Physically and visually the affected fruit does not show any attributes that indicates the presence of IFR on any part of the fruit until it has been cut open. This paper investigates the feasibility of a non-destructive method to screen the Harumanis mango from IFR using near-infra red light and artificial neural network. A common NIR light emitting diodes of 1000nm wavelength was used as the light source to emit NIR light while a photodiode was used to measure the intensity of the reflected NIR light from Harumanis mango. Early detection of IFR were done manually by local expert using acoustic method by flicking fingers to detect any abnormality inside the fruit. Sample data on NIR Spectroscopy reflectance results of 120 samples were used to classify the presence of IFR using neural network. Mean value of NIR reflectance of RBG for Harumanis mango with an incidence of Insidious Fruit Rot are R= 0.651, G= 0.465 and B=0.458, while without IFR are R = 0.211, G=0.15 and B=0.146. Using MATLAB's neural network training tool, a training set regression was obtained with an accuracy value of 0.9805 for prediction of IFR, thus this value is very high in accuracy.
      46  7
  • Publication
    Pre- and Post-operative Assessment of Bone with Osteogenesis Imperfecta using Finite Element Analysis: A Review
    ( 2024-02-01)
    Wanna Soh Bua Chai
    ;
    Khairul Salleh Basaruddin
    ;
    Mohd Hanafi Mat Som
    ;
    Fauziah Che Mat
    ;
    Muhamad Khairul Ali Hassan
    Applications of finite element analysis (FEA) to demonstrate the pre-and post-operative conditions of the brittle bone-related disease known as osteogenesis imperfecta (OI) has been widely used in the past and at present. The method used to reconstruct the bone model that resemble the OI bone geometry plays an important aspect to accurately represent the bone condition to provide more alternative ways to evaluate surgical intervention options. Other factors such as material properties and boundary conditions also reflect the results of the analysis. Therefore, the aim of this review paper is to analyse the approaches of previous studies in terms of model geometry construction, selection of materials properties and boundary conditions to enable a deeper understanding and evaluation of bone fractures in OI patients. The biomechanical design of the intramedullary (IM) rods used in post-operative surgery and the interface between IM rods and bone fragments are also discussed in this review paper.
      5  33
  • Publication
    A Review on Magnetic Induction Spectroscopy potential for fetal acidosis examination
    ( 2022-02-01)
    Siti Fatimah Abdul Halim
    ;
    Zulkarnay Zakaria
    ;
    Pusppanathan J.
    ;
    Anas Mohd Noor
    ;
    Ahmad Nasrul Norali
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Muji S.Z.M.
    ;
    Rahim R.A.
    ;
    Engku-Husna E.I.
    ;
    Muhamad Khairul Ali Hassan
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Ahmad Faizal Salleh
    ;
    Mohd Hanafi Mat Som
    Fetal acidosis is one of the main concerns during labor. Currently, fetal blood sampling (FBS) has become the most accurate measurement of acidosis detection. However, it is invasive and does not provide a real time measurement due to laboratory procedures. Delays in diagnosis of acidosis have caused serious injury to the fetus, especially for the brain and the heart. This paper reviews the new technique in diagnosis of acidosis non-invasively. Magnetic Induction Spectroscopy (MIS) has been proposed to be a new device for acidosis detection in recent years. This paper explains the basic principle of MIS and outlines the design specifications and design considerations for a MIS pH probe. It is expected that readers will gain a basic understanding of the development of a MIS pH probe from this review.
      2  35
  • Publication
    Single Channel Magnetic Induction Measurement for Meningitis Detection
    ( 2021-01-01)
    Aiman Abdulrahman Ahmed
    ;
    Zulkarnay Zakaria
    ;
    Ali M.H.
    ;
    Pusppanathan J.
    ;
    Rahim R.A.
    ;
    Muji S.Z.M.
    ;
    Anas Mohd Noor
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Muhamad Khairul Ali Hassan
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Ahmad Faizal Salleh
    Bacterial meningitis is one of the most common and prominent infections which infects the central nervous system through the tissue layers and membranes that cover our brain and spinal cord. It is a staggering and fatal illness that kills patients within hours. The number of meningitis cases that has been recorded annually around the world are one million cases and 135,000 deaths. Early detection and start of sufficient treatment are considered as the main determinants for better result. MIT mechanism is noncontact electrodes of impedance measurement. This mechanism uses induction principle instead of contact electrodes to get the required information. This paper presents an overview on the potential of Magnetic induction tomography (MIT) in detecting meningitis disease. In MIT principle, single channel measurement process which consist of transmitter (Tx) and receiver (Rx) coil has been studied. In this field is disclosed about passive electrical field (PEP) which focuses on the three parameters which are dielectric permittivity, electrical conductivity, and magnetic permeability. In addition, this research project involves experimental setup. The applied frequency is between 1–10 MHz. Finally, in this project, the performance of the square coil with 12 number of turns (5Tx–12Rx) with 10 MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.
      1
  • Publication
    A Device-to-Device (D2D) Communication between Mobile Robots using Wireless Communication Protocol in Dynamic Environments
    ( 2024-03-11)
    Sarhan M.A.H.
    ;
    Hashim M.S.M.
    ;
    Abdul Halim Ismail
    ;
    Mohd Nasir Ayob
    ;
    Hassrizal Hassan Basri
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Othman S.M.
    ;
    Kanafiah S.N.A.M.
    ;
    Muhamad Khairul Ali Hassan
    Mobile robots must have the ability to guarantee safety for operation in a dynamic environment and close to other moving objects. There are many research had been conducted to make the robot safer by utilizing sensors and big data technology to make the mobile robot able to navigate autonomously and intelligently. One of the key elements in autonomous robots is communication between robots. In this paper, device-to-device (D2D) technology has been used to develop communication between robots. To establish the algorithm for D2D communications, radio frequency (RF) used as communication protocols that can perform D2D communication in real-time applications. The performance of D2D communication was then be assessed in terms of distance and latency. RF transceiver module has been mounted on the robot with Arduino to allow communication between mobile robot to other mobile robots in order to transfer data from robot's sensors to the other mobile robots. By utilizing the gathered information and data, the robot can assess its surroundings and predict the movement of other robots to avoid collisions between robots. The results show that the RF transceiver module is capable to send and receive data between two robots with latency up to 4.865s. It is envisaged that the proposed module can be very useful for developing D2D communication between robots to operate in dynamic environments.
      49  8