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

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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.

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Assessments of cognitive state of Mitragyna speciosa (ketum) users during relaxation state

2023-02-21 , Fadhilah A.W. , Vikneswaran Vijean , Ahmad Faizal Salleh , Rashid R.A. , Palaniappan R. , Mutusamy H. , Helmy K.

The abuse of Mitragyna speciosa or commonly known as ketum leaves is widespread across Asian countries. Ketum leaves that were originally used as medicine were abused for the purpose of deluding their minds. As it has intoxicated properties that similar to drugs, EEG signals of ketum users may differ from normal people as the ketum may alter the brain signal and the cognitive state of ketum users may decrease. Therefore, this study was conducted to assess the cognitive state between ketum users and non-ketum users in terms of their relaxation state by using brain signal characteristics. A total of 8 subjects were involved in the experimental session. The 8 subjects were divided into two groups which are 4 subjects were ketum users for at least one year while the other 4 subjects were non-ketum users, had enough sleep for at least 6 hours and had no mental disorders. The EEG data was recorded during awaken relaxed state and was filtered using a notch filter and Independent Component Analysis (ICA) to remove the powerline artefacts, eye blinking and eye movement. Stockwell Transform was used to reduce the amount of the large data and extract useful features from the signal. Student's t-test is performed in order to compute the percentages of the differences between the ketum users and non-ketum users in each brain lobe. Mean of Shannon Entropy, mean of Tsallis Entropy, and mean of Hurst Exponent features used were able to elucidate the differences in brain activities between the two groups investigated.

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Identification of habitual smokers through speech signal

2024-02-08 , Vikneswaran Vijean , Ragunathan Al Santiagoo , Abdul Ghapar Ahmad , Syakirah Afiza Mohammed , Razi Ahmad , Wan Amiza Amneera Wan Ahmad

Smoking is an addictive behavior and can result major health complications. Nowadays, many young adults tend to pick up this unhealthy habits which could potentially harm their health and affects the future workforce of the nation. Most of the habitual smokers have difficulties in ceasing this habit and require external assistance in the form of group therapy, medical interventions to quit smoking. Therefore, the main aim of this study is to investigate the speech signals of the subjects in an effort to identify the habitual smokers non-invasively. Through this detection, young smokers could be identified. Voice samples from VOice ICar fEDerico II from PhysioNet database were used for this study. Wavelet Packet Transform was used to extract non-linear features from the signals. Due to uneven data, ADASYN algorithm was used to produce a balanced dataset through synthetic data sampling. Subspace KNN and SVM classifiers were used for the investigations and classification accuracies up to 83.7% and 94% of AUC curve were observed from the analysis. The results suggests that the proposed method is effective in detecting habitual smokers, and can be considered as an early screening for smoking habits in young adults for targeted rehabilitation strategies.

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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.

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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.

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Effect of Mindfulness Meditation toward Improvement of Concentration based on Heart Rate Variability

2020-12-20 , Lim Chee Chin , Rosli F.F.B. , Fook C.Y. , Vikneswaran Vijean , Saidatul Ardeenawatie Awang , Palaniappan R.

Mindfulness meditation is a type of therapy for a psychological cure like depression and anxiety that can significantly increase peoples' ability to concentrate and focus. Thus, this paper describes the analysis of mindfulness meditation effect toward concentration study in term of heart rate variability (HRV) signal. A memory test is used as a medium to test the concentration level of 20 participants, and their performance of the electrocardiogram signal was recorded. Peaks detection method and Pan-Tompkin method are used to extract the features like PQRST peaks and R-R interval from the ECG signal. Then, the extracted ECG signal features are classified using KNN method for before and after meditation during the memory test. The result shows that the effect of mindfulness meditation can improve the performance of participants' concentration level. The highest accuracy, sensitivity and specificity performance is obtained from the combination of all six features (P, Q, R, S, T peaks, and R-R interval value), which is 84.58 %, 88.77% and 80.39%. The analysis of memory test produces higher memory test score (69.2%), lesser miss selection (60.8%) and shorter taken time to complete the memory test (2.268 minutes) after mindfulness meditation compared to before mindfulness meditation. The R-R interval value represents heart rate variability (HRV) is important to prove that most of the participants are more relax and can handle their stress better after doing mindfulness meditation.

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Optimum Binder Content of Asphaltic Concrete (ACW14) Mixture Incorporating Limestone

2023-01-01 , Syakirah Afiza Mohammed , Abdul Ghapar Ahmad , Ragunathan Al Santiagoo , Vikneswaran Vijean , Wan Amiza Amneera Wan Ahmad , Razi Ahmad , Kai L.S.

Due to the high demand for natural aggregates in pavement construction, researchers have been looking for alternative materials to replace natural aggregate. In this research, the optimum binder content of asphalt mixture incorporate limestones was investigated. The optimum binder content of asphalt mixture was tested according to Marshall method. About 20 % of limestone was used as aggregate replacement in asphaltic concrete mixture. To determine the stability, volumetric properties, and bitumen binder content, three percentage of asphalt binder content, namely 4.0%, 5.0% and 6.0% was prepared. From analysis, it indicated that stability and volumetric properties of asphalt mixture incorporate limestone meet the requirement set by JKR. From the result obtained, the optimum binder content of the control sample is 5.0% and optimum binder content of limestone mixture is 5.2%. The slightly different in optimum binder content value indicate that the optimum binder content of limestone mixture was comparable with control mixture.

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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.

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Investigation on Body Mass Index Prediction from Face Images

2021-03-01 , Chong Yen Fook , Lim Chee Chin , Vikneswaran Vijean , Lim Whey Teen , Hasimah Ali , Aimi Salihah Abdul Nasir

Body mass index is a measurement of obesity based on measured height and weight of a person and classified as underweight, normal, overweight and obese. This paper reviews the investigation and evaluation of the body mass index prediction from face images. Human faces contain a number of cues that are able to be a subject of a study. Hence, face image is used to predict BMI especially for rural folks, patients that are paralyzed or severely ill patient who unable to undergoes basic BMI measurement and for emergency medical service. In this framework, 3 stages will be implemented including image pre-processing such as face detection that uses the technique of Viola-Jones, iris detection, image enhancement and image resizing, face feature extraction that use facial metric and classification that consists of 3 types of machine learning approaches which are artificial neural network, Support Vector Machine and k-nearest neighbor to analyze the performance of the classification. From the results obtained, artificial neural network is the best classifier for BMI prediction system with the highest recognition rate of 95.50% by using the data separation of 10% of testing data and 90% of training data. In a conclusion, this system will help to advance the study of social aspect based on the body weight.

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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.

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