Vikneswaran Vijean
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Preferred name
Vikneswaran Vijean
Official Name
Vijean, Vikneswaran
Alternative Name
Vikneswaran
Vikneswaran, V.
Vijean, V.
Main Affiliation
Scopus Author ID
54785424700
Researcher ID
D-2539-2015

Research Output

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  • Publication
    Recognition of plant diseases by leaf image classification using deep learning approach
    ( 2023-02-21)
    Goy S.Y.
    ;
    Chong Y.F.
    ;
    Teoh T.K.K.
    ;
    Lim Chee Chin
    ;
    Vikneswaran Vijean
    Plant health is important in maintaining the sustainability of the foods crop. The key to prevent the loss of yield of plant crops is the identification of plant diseases. The process of monitor plant health manually is challenging as it required expert knowledge which is expensive and time-consuming. Hence, the image processing techniques can be useful for the detection and classification of plant leaf disease. In this project, the leaf images of 5 plant types in the PlantVillage dataset are used for plant type and plant disease classification. The original images are resized to the required input sized and the proposed background removal methods (improved HSV and GrabCut segmentation) are performed to reduce the background noise. The segmented images are then given to proposed models (AlexNet and DenseNet121) for training and classification. For plant type classification, DenseNet121 got a better validation accuracy of 99% compared to AlexNet with 91.2%. After that, the leaf image is given to plant disease models according to their species. All the plant disease models training with DenseNet121 can achieve high validation accuracy of 99%, 99%, 100%, 100% and 97% for apple, grape, potato, strawberry and tomato. Lastly, a user-friendly graphical user interface (GUI) is developed.
  • Publication
    Cloud based analysis and classification of EEG signals to detect epileptic seizures
    ( 2021-03-25)
    Rushambwa M.C.
    ;
    Gezimati M.
    ;
    Govindaraj P.
    ;
    Palaniappan R.
    ;
    Vikneswaran Vijean
    ;
    Ghulam Nabi F.
    Epileptic seizures are explained as the abnormal electrical activity occurring in the brain due to an internal or external triggering factors. EEG (Electroencephalograph) is used to record brain activity and can be used to detect the seizures before, during or after they occur. These signal characteristics, however differ from patient to patient due to the different emotional and physical wellbeing of the various individuals. In normal circumstances, anti-epileptic medication is used to treat patients but very few systems have been developed to manage and track the seizures. In most extreme and rare cases, some patients undergo invasive surgery to treat the seizures and this is common in seizures that are caused by tumors or physical brain damage. Non-invasive surface electrode EEG measurement gives an estimate of the seizure onset but more invasive intracranial electrocorticogram (ECoG) are required at times for precise localization of the epileptogenic zone. This project aims at designing and implementing a device that can be used to detect and monitor the attention and meditation values of a person in real time. The system measures the EEG waves of the brain, performs feature extraction, classification and sends the control command over wireless to a remote controller. The remote controller in turn issues commands with corresponding brain wave frequency and sends it to the cloud for remote analysis and classification.
  • Publication
    Non-invasive Detection of Ketum Users through Objective Analysis of EEG Signals
    ( 2021-11-25)
    Nawayi S.H.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Rashid R.A.
    ;
    Planiappan R.
    ;
    Lim C.C.
    ;
    Fook C.Y.
    ;
    Saidatul Ardeenawatie Awang
    Ketum leaves are traditionaly used for treatment of backpain and reduce fatigue. However, in recent years people use ketum leaves to substitute traditional drugs as they can easily be obtained at a low cost. Currently, a robust test for ketum detection is not available. Although ketum usage detection via test strip is available, however, the method is possible to be polluted by other substances and can be manipulated. Brain signals have unique characteristics and are well-known as a robust method for recognition and disease detection. Thus, this study has been done to distinguish between ketum users and non-users via brain signal characteristics. Eight participants were chosen, four of whom are heavy ketum users and four non-users with no health issues. Data were collected using the eegoSports device in relaxed state. In pre-processing, notch filter and Independent Component Analysis (ICA) were used to remove artifacts. Wavelet Packet Transform (WPT) was used to reduce the large data dimension and extract features from the brain signal. To select the most significant features, T-Test was used. Support Vector Machine (SVM), K-Nearest Neighbour, and Ensemble classifier were used to categorize the input data into ketum users and non-users. Ensemble classifier was found to be able to predict the testing instances with 100% accuracy for open and closed eyes task with Teager energy and energy to standard deviation ratio as the features.
  • Publication
    Bio-Char And Bio-Oil Production From Pyrolysis of Palm Kernel Shell And Polyethylene
    ( 2023-01-01)
    Ibrahim N.R.
    ;
    Razi Ahmad
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Vikneswaran Vijean
    ;
    Ragunathan Al Santiagoo
    ;
    Syakirah Afiza Mohammed
    ;
    Khalid A.F.
    ;
    Ani A.Y.
    In recent years, palm kernel shell (PKS) has become a viable feedstock for making biofuels and value-added commodities using a variety of thermal conversion routes. Therefore, significant conservation is required for PKS as a resource for fuel production in biofuel facilities. Thus, this research was intended to elucidate the effects on PKS as a solid fuel through torrefaction and the production of bio-char and bio-oil by single and co-pyrolysis of PKS and polyethylene (PE). The PKS was treated through torrefaction at different temperatures and holding times. The optimum parameters for torrefaction were a temperature of 250 oC and a holding time of 60 min. Then the PKS and PE were pyrolyzed in a fixed-bed reactor at different temperatures and ratios. The product yield was analysed for single and co-pyrolysis of PKS and PE for pyrolysis. The properties of the product composition for single and co-pyrolysis of the PKS and PE were determined by proximate analysis, Fourier transform infrared (FTIR) analysis, and gas chromatography-mass spectrometry (GC-MS). The optimum parameter obtained for biochar and bio-oil production from co-pyrolysis of PKS and PE was at temperature of 500 oC at a ratio of 1:2 (PKS: PE). The ester and phenol compounds were increased around 19.02 to 23.18% and 32.51 to 34.80 %, respectively, while amide and amine decreased around 4.94 to 18.87% and 0.63 to 32.39 %, respectively, compared to the single pyrolysis of PKS. Therefore, the PKS and PE co-pyrolysis significantly increased the amount of phenol and ester compounds while slightly reducing the amount of amide and amine compounds in the bio-oil product. As a conclusion, biomass conservation enables the manufacturing of value-added chemicals.
  • Publication
    Compatibilizers Effect on Recycled Acrylonitrile Butadiene Rubber with Polypropylene and Sugarcane Bagasse Composite for Mechanical Properties
    ( 2023-10-16)
    Zainal M.
    ;
    Ragunathan Al Santiagoo
    ;
    Razi Ahmad
    ;
    Mohammed S.A.
    ;
    Abdul Ghapar Ahmad
    ;
    Vikneswaran Vijean
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Allan Melvin Andrew
    Compatibilizers effect on recycled acrylonitrile butadiene rubber (NBRr) with polypropylene (PP) and sugarcane bagasse (SCB) composite for mechanical properties is evaluated. Trans-Polyoctylene Rubber (TOR) and Bisphenol a Diglycidyl Ether (DGEBA) are used as compatibilizers in this study. Three (3) different composites (80/20/15, 60/40/15, and 40/60/15), with fixed filler (15 phr) and compatibilizers (10 phr) content, were carried out. These composites were arranged via melt mixing technique utilizing a heated two-roll mill at a temperature of 180 C for 9 minutes employing a 15-rpm rotor speed. Tensile and morphological properties were evaluated. The result shown average tensile strength dropped by 48.50% as the recycle NBR content rises 20 phr. Nevertheless, subsequent compatibilization reveals that the compositesâ tensile properties were all greater than control composites. The morphology discovered validates the tensile properties, indicating a stronger interaction between the PP/SCB and recycle NBR composites with the addition of compatibilizer DGEBA.
  • Publication
    Potential of pretreated palm kernel shell on pyrolysis
    ( 2023-01-01)
    Razi Ahmad
    ;
    Ragunathan Al Santiagoo
    ;
    Abdul Ghapar Ahmad
    ;
    Syakirah Afiza Mohammed
    ;
    Wan Ahmad W.A.M.
    ;
    Vikneswaran Vijean
    ;
    Ibrahim N.R.
    The impact of pretreatment on palm kernel shell (PKS) with torrefaction for the possibility of pyrolysis is discussed in this study. PKS samples were torrefied at different holding times of 30 and 60 minutes at temperatures of 200, 225, 250, 275, and 300 °C. In a fixed-bed reactor with a constant nitrogen flow rate of 500 ml/min, torrefaction pretreatment was carried out. The elemental composition, mass, and energy yield, as well as proximate analysis, were all performed on the pretreated PKS. The optimised pretreated PKS was pyrolyzed next at a temperature of 400 to 550 °C in a fixed-bed reactor. The outcomes demonstrated that the pretreated PKS had a significant mass and energy yield at a temperature of 250 °C and a holding time of 30 min. PKS's calorific value and carbon content both rose after pretreatment. However, the oxygen and moisture content decreased for pretreated PKS. The maximum bio-oil production of 58% was achieved during the pyrolysis of pretreated PKS at a temperature of 500 °C. At higher temperature of 550 ℃, the bio-oil decreased due to secondary cracking reaction. Consequently, the pretreated PKS has greater potential as effective feedstock for successive proses particularly pyrolysis for bio-oil production.
  • Publication
    Review Article A Review of Optical Ultrasound Imaging Modalities for Intravascular Imaging
    ( 2023-01-01)
    Rushambwa M.C.
    ;
    Suvendi R.
    ;
    Pandelani T.
    ;
    Palaniappan R.
    ;
    Vikneswaran Vijean
    ;
    Nabi F.G.
    Recent advances in medical imaging include integrating photoacoustic and optoacoustic techniques with conventional imaging modalities. The developments in the latter have led to the use of optics combined with the conventional ultrasound technique for imaging intravascular tissues and applied to different areas of the human body. Conventional ultrasound is a skin contact-based method used for imaging. It does not expose patients to harmful radiation compared to other techniques such as Computerised Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. On the other hand, optical Ultrasound (OpUS) provides a new way of viewing internal organs of the human body by using skin and an eye-safe laser range. OpUS is mostly used for binary measurements since they do not require to be resolved at a much higher resolution but can be used to check for intravascular imaging. Various signal processing techniques and reconstruction methodologies exist for Photo-Acoustic Imaging, and their applicability in bioimaging is explored in this paper.
  • Publication
    Comparison between predicted results and built-in classification results for brain-computer interface (BCI) system
    ( 2021-05-03)
    Ong Z.Y.
    ;
    Saidatul Ardeenawatie Awang
    ;
    Vikneswaran Vijean
    Brain-computer interface (BCI) system is a system of receiving information and transferring responses by communication between a computer and human brain. BCI system acts as assistive device to help the severe motor disabilities patients to live like a normal human being. Classification results used to validate the performances of BCI system. Several classification methods have been used in BCI system. However, previous researchers did not compare the classification results with predicted results. In this study, the predicted results were calculated from the questionnaire which collected from participants after completed the experiments. These predicted results were used to compare with the results from classification learner tool. The built-in classification methods included decision tree, support vector machine (SVM), k-nearest neighbor (KNN) and ensemble classifiers. Based on the results, the average difference of predicted results and built-in classification results for cubic SVM is the smallest which is 2.41% and 1.81% for motor imagery 1 and motor imagery 2 respectively. This finding shows that the cubic SVM classifier can detect the mistake that did by the subjects during the experiment.
  • Publication
    Assessment on Water Footprint of Paddy Cultivation in Kedah
    ( 2023-01-01)
    Azhar N.
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Syakirah Afiza Mohammed
    ;
    Abdul Ghapar Ahmad
    ;
    Ragunathan Al Santiagoo
    ;
    Vikneswaran Vijean
    ;
    Razi Ahmad
    A measurable metric to gauge both the amount of water pollution and water consumption per unit of crop is the water footprint (WF). WF can be used as a thorough indicator of the utilization of freshwater resources. To increase the yield of crops, agriculture, consumed large amount of water. To prevent water shortages, it is crucial to understand how much water is consumed during agriculture. This study's goal is to estimate the region of Kedah's water footprint for paddy cultivation in the year 2017. By using CROPWAT 8.0 computer programming, climatic parameters were used for the estimation of water evapotranspiration for blue and green to proceed with the water footprint accounting. The methodological framework followed the crop water requirement (CWR) option based on the water footprint assessment manual. Focusing on the water footprint accounting phase, the blue, green and grey water footprint was calculated. The findings demonstrated that the three forms of water footprints differ significantly from one another. In Kedah, the value of the green water footprint (1201m3/ton) is higher than that of the blue water (130m3/ton) and the grey water (357m3/ton) footprints. 71% of green water footprint indicates that there is enough rainfall to support paddy growth.
      25  2
  • Publication
    Investigation on Medicated Drugs in ECG of Healthy Subjects
    ( 2022-01-01)
    Vikneswaran Vijean
    ;
    Abdul Ghapar Ahmad
    ;
    Syakirah Afiza Mohammed
    ;
    Razi Ahmad
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Ragunathan Al Santiagoo
    ;
    Lim Chee Chin
    Heart diseases are now the leading cause of death worldwide, it is estimated that around 7 million patients who are living in developed countries, lost their lives due to diseases related to their cardiovascular system. In Malaysia, cardiovascular diseases represents one fifth of total deaths in the country in the past three decades. Currently patients need some sort of drugs that help them to stabilize and restore the regular patterns of their heart beat because if the patients cannot manage to restore the normal heart beat pattern, the undesired heart condition could lead life threatening situations. Advancement of biotechnology has enabled the creation of new medicated drugs to provide better treatment options. However, when this treatment option fails and there is a need to provide emergency intervention to the patients in hospitals, the medical experts often need to know about the patients' intake of any medications prior to hospital admittance for providing suitable treatments. Sometimes, this would be a difficult task as the patient might be admitted in semi-conscious or unconscious state. Therefore, this study focusses on identification of different medicated drugs usage through analysis of ECG data of the users. The data for the experiment was obtained from physionet library, which provides ECG data of subjects administered with a combination of Dofetilide, Mexiletine, lidocaine, Moxifloxacin and Diltiazem medicated drugs. The use of morphological and non-linear features derived from the ECG signals were able to provide prediction accuracy of 77.26% using SVM classifier.
      27  2