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

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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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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.

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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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Simulation of PLC Ladder Logic Programming for an Automated Glass Bottle Molding and Refilling Plant

2021-01-01 , Khan M.M. , Shawareb O.M.A. , Palaniappan R. , Vikneswaran Vijean , Nabi F.G. , Shawareb A.H.A. , Khan N.K.

Automation has been gaining interest in every branch today. The reason for the popularity of automation in industries today is due to its capability to reduce labour cost, reduce material wastage, increase the production quantity, to improve the quality of the product and to reduce idle time in manufacturing industry. In this work, an industrial process of glass molding and filling operation is simulated using the Fiddle PLC simulator. GRAFCET based modelling of discrete event is used in developing the PLC ladder logic program for the industrial process. The proposed method reduces labour cost by 40%, Increases production value by 55%, and reduces material wastage by 20% compared to manual operation. The proposed GRAFCET based model was found to be reliable, efficient, and accurate in performing the control sequence of the glass molding and filling operation. In future, it is proposed to develop the HMI (human machine interface) and the corresponding hardware will be developed for the same application.

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Clinical validation of 3D mesh reconstruction system for spine curvature angle measurement

2023-02-21 , Shanyu C. , Lim Chee Chin , Fook C.Y. , Azizan A.F. , Vikneswaran Vijean , Ahmad Faizal Salleh , Shafriza Nisha Basah

Spine curvature disorders are scoliosis, lordosis, and kyphosis. These disorders are mainly caused by the bad habits of the person during sitting, standing, and lying. There are about 3 to 5 out of 1,000 people who are affected by spine curvature disorder. The current conventional method used for diagnose this disorder, such as radiography, goniometry and palpation. However, these conventional methods require human skills and can be time-consuming, resulting to exhaustion of logistic. Therefore, there is a need to solve this problem by creating a Graphical User Interface (GUI) to analyse the human body posture through the 3D reconstructed model of the person. Hence, 3D map meshing reconstruction of the human body method is proposed. This project divided into three parts, which are the development of the GUI for human posture analysis, clinical validation and posture analysis of the 3D model. The 3D model reconstructed from 3D mapping parameters shows 100% accuracy of the assessed point. The lowest difference of angle for the comparison between clinical method (goniometer) and the GUI for male is (A.Pe) 0.930±0.870 and 1.240±0.860 for female (P.Pe). This finding of 3D model assessment system can be helpful for medical doctor to diagnose patient who have spine problem.

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Optimization of dual-tree complex wavelet packet based entropy features for voice pathologies detection

2020-07-01 , Farah Nazlia Che Kassim , Vikneswaran Vijean , Abdullah Z. , Muthusamy H. , Rokiah Abdullah

The Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) has been successfully implemented in numerous field because it introduces limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions where these properties are lacking in traditional wavelet transform. This paper investigates the performance of features extracted using DT-CWPT algorithms which are quantified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classifiers for detecting voice pathologies. Decomposition is done on the voice signals using Shannon and Approximate entropy (ApEn) to signify the complexity of voice signals in time and frequency domain. Feature selection methods using the ReliefF algorithm and Genetic algorithm (GA) are applied to obtain the optimum features for multiclass classification. It is observed that the best accuracies obtained using DT-CWPT with ApEn entropy are 91.15 % for k-NN and 93.90 % for SVM classifiers. The proposed work provides a promising detection rate for multiple voice disorders and is useful for the development of computer-based diagnostic tools for voice pathology screening in health care facilities.

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Smart fall detection monitoring system using wearable sensor and Raspberry Pi

2024-02-08 , Lim Chee Chin , Mahmud N.F.A. , Vikneswaran Vijean , Ali Yeon Md Shakaff , Tan X.J. , Ahmad Faizal Salleh , Shafriza Nisha Basah

The Smart Fall Detection Monitoring System is the name of the programme that monitors everyday activities and falls. It has an accelerometer sensor (ADXL345) and Raspberry Pi 3 microcontroller board to recognise and classify the patient's fall. Python programming was done on the Raspberry Pi terminal to enable communication between the accelerometer sensor and the computer. There were 10 subjects (5 males and 5 females) collected. While daily living activities include standing, squatting, walking, sitting, and lying, the data on falling includes forward falls and falls from medical beds. The K-nearest Neighbour (kNN) classifier can categorise the data of falling and non-falling (everyday living activity). The accuracy of the kNN classifier was 100% for the combined feature and (>87%) for each feature during the categorization of the falling and non-falling classes. In the meantime, multiclass classification performance for combining features and for each feature separately was >85%. kNN classifier was used to assess the feature. The feature was chosen based on the k-NN classifier's accuracy score as a percentage. For feature selection for falling and non-falling, feature (AcclX, AcclY, AngX, AngY and AngZ) in City-block distance was selected as they performed high accuracy which was 100%. The performance of the AngZ (77%) was good during the sub-classification of the sub-class dataset. As a result, all feature characteristics were chosen to be incorporated in the IoT fall detection device. The system is real-time communication for classifying fall and non-fall conditions with 100% accuracy using kNN classifier with cityblock distance.

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Differential diagnosis tool in healthcare application using respiratory sounds and convolutional neural network

2023-08-03 , Palaniappan R. , Sundaraj K. , Nabi F.G. , Vikneswaran Vijean

This chapter focuses on the classification of respiratory pathology using breathsound signals. The development of a computerised breath-sound analysis system could improve the standard of living of people affected by respiratory-related disease and further also be used as a differential diagnosis tool in affective computing. Accordingly, in this chapter, respiratory sounds recorded according to the Computerised Respiratory Sound Acquisition standard were obtained from subjects with respiratory sounds belonging to five different respiratory pathologies, namely, normal, wheezes, rhonchi, fine crackles, and coarse crackles.

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Data Acquisition System for Web-based Multi-modal Data Repository

2021-03-25 , Rushambwa M.C. , Mukherjee A. , Maity M. , Palaniappan R. , Vikneswaran Vijean , Ghulam Nabi F.

The multimodal medical data with annotation helps to build different automated algorithms. Each reported work has used a specific disease database and developed a CAD based on the considered database. Therefore, the availability and quality of medical database play the most crucial role in developing any CAD. However, it has been observed that most of the reported studies used public database (created by foreign universities/centers) or private database. Unfortunately, the availability of a national medical database in India is negligible. However, development of such medical database is possible. Such medical database can encourage new research activity and help a large research community. The proposed study focuses on developing an online public medical multimodal database platform. Here, the data acquisition software is build for collecting various information.

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Investigation on effects of light transmittance in distinguishing conventional plastics and bioplastics for plastic recycling

2022-12 , Ragunathan Al Santiagoo , Razi Ahmad , Syakirah Afiza Mohammed , Abdul Ghapar Ahmad , Vikneswaran Vijean , Wan Amiza Amneera Wan Ahmad , Mustaffa Zainal

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