Journal Articles

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Muhammad Aizat Abu Bakar

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Mohd Hafiz Fazalul Rahiman

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Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments. Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets. To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor. We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map. The environment contained static obstacles and a single mobile robot. Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets. The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL. In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality. The VFH-QL method was then evaluated using a benchmark dataset. The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.

11 26

Publication

A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration

( 2011-08)

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Ali Yeon Md Shakaff

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Masnan, Maz Jamilah

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Norazian Subari

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Nazifah Ahmad Fikri

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Mohd Noor Ahmad

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Mahmad Nor Jaafar

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Abdul Hallis Abdul Aziz

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Abu Hassan Abdullah

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Supri A. Ghani

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

18 19

Publication

A heuristic ranking approach on capacity benefit margin determination using pareto-based evolutionary programming technique

( 2015)

Muhammad Murtadha Othman

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Nurulazmi Abd. Rahman

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Ismail Musirin

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Mahmud Fotuhi-Firuzabad

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Abbas Rajabi-Ghahnavieh

This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.

4 10

Publication

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

( 2019)

Abdul Syafiq Abdull Sukor

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Norasmadi Abdul Rahim

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Rossi Setchi

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Hiromitsu Nishizaki

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.

19 16

Publication

A systematic review of phacoemulsification cataract surgery in virtual reality simulators

( 2013-01-27)

Lam Chee Kiang

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Kenneth Sundaraj

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Mohd Nazri Sulaiman

The aim of this study was to review the capability of virtual reality simulators in the application of phacoemulsification cataract surgery training. Our review included the scientific publications on cataract surgery simulators that had been developed by different groups of researchers along with commercialized surgical training products, such as EYESI® and PhacoVision®. The review covers the simulation of the main cataract surgery procedures, i.e., corneal incision, capsulorrhexis, phacosculpting, and intraocular lens implantation in various virtual reality surgery simulators. Haptics realism and visual realism of the procedures are the main elements in imitating the actual surgical environment. The involvement of ophthalmology in research on virtual reality since the early 1990s has made a great impact on the development of surgical simulators. Most of the latest cataract surgery training systems are able to offer high fidelity in visual feedback and haptics feedback, but visual realism, such as the rotational movements of an eyeball with response to the force applied by surgical instruments, is still lacking in some of them. The assessment of the surgical tasks carried out on the simulators showed a significant difference in the performance before and after the training.

Publication

An alternative approaches to predict flashover voltage on polluted outdoor insulators using artificial intelligence techniques

( 2020-04-01)

Salem, Ali. Ahmed. Ali

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Rahman, Rahisman Abd

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Kamarudin M.S.

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Othman, Nordiana Azlin

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Jamail, Nor. Akmal. Mohd.

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Haziah Abdul Hamid

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Ishak, Mohd. Taufiq

This paper presents an alternative approach for predicting critical voltage of pollution flashover by using Artificial Intelligence (AI) technique. Data from experimental works combined with the theoretical results from well-known theoretical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining critical voltage of flashover. Series of laboratory testing and measurement are carried for 1:1, 1:5 and 1:10 ratios of top to bottom surface salt deposit density on cup and pin insulators. Insulators variables such as height H, diameter D, form factor F, creepage distance L, equivalent salt deposit density (ESDD) and flashover voltage correction are identified and used to train the AI network. Comparative studies have evidently shown that the proposed (AI) technique gives the satisfactory results compared to the analytical model and test data with the Coefficient of determination R-Square value of more than 97%.

4 3

Publication

An emotion assessment of stroke patients by using bispectrum features of EEG Signals

( 2020)

Choong Wen Yean

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Wan Khairunizam Wan Ahmad

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Wan Azani Wan Mustafa

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Murugappan Murugappan

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Yuvaraj Rajamanickam

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Mohammad Iqbal Omar

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Bong Siao Zheng

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Ahmad Kadri Junoh

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Zuradzman Mohamad Razlan

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Shahriman Abu Bakar

Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.

Publication

Analysis of space charge formation in LDPE in the presence of crosslinking byproducts

( 2012-02)

Nuriziani Hussin

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George Chen

Cross-linking byproducts are suspected to be the main contributing factor in space charge formation observed in XLPE. To investigate the mechanism behind this phenomenon, low density polyethylene was soaked into three main crosslinking byproducts, acetophenone, α- methylstyrene and cumyl alcohol, and space charge measurements were performed using the Pulse Electroacoustic technique (PEA). It has been found that soaking LDPE in cumyl alcohol introduces more charges into the system, with homocharges and heterocharges accumulating within the sample compared to the additive free sample. In contrast, α- methylstyrene and acetophenone reduce the amount of accumulated charges. In terms of charge decay, all three byproducts enhance the decay process in the insulator. Further investigations were conducted in conditions where two byproducts are present in a sample. The results shows that acetophenone is a dominant byproduct in determining the charge density patter built up during the charging process, whilst the rate of charge decay is observed to be high in the presence of α-methylstyrene in the sample.

Publication

Auditory evoked potential response and hearing loss: a review

( 2015)

M. P Paulraj

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Kamalraj Subramaniam

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Sazali Bin Yaccob

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C. R Hema

Hypoacusis is the most prevalent sensory disability in the world and consequently, it can lead to impede speech in human beings. One best approach to tackle this issue is to conduct early and effective hearing screening test using Electroencephalogram (EEG). EEG based hearing threshold level determination is most suitable for persons who lack verbal communication and behavioral response to sound stimulation. Auditory evoked potential (AEP) is a type of EEG signal emanated from the brain scalp by an acoustical stimulus. The goal of this review is to assess the current state of knowledge in estimating the hearing threshold levels based on AEP response. AEP response reflects the auditory ability level of an individual. An intelligent hearing perception level system enables to examine and determine the functional integrity of the auditory system. Systematic evaluation of EEG based hearing perception level system predicting the hearing loss in newborns, infants and multiple handicaps will be a priority of interest for future research.

3 10

Publication

Cervical cancer detection techniques: a chronological review

( 2023-05-17)

Wan Azani Wan Mustafa

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Shahrina Ismail

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Fahirah Syaliza Mokhtar

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Hiam Alquran

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Yazan Al-Issa

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.

2 9

Publication

Classification of Agarwood oil using an Electronic Nose

( 2010)

Wahyu Hidayat

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Ali Yeon Md Shakaff

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Mohd Noor Ahmad

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Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.

18 13

Publication

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

( 2013-05-16)

Jerritta Selvaraj

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Murugappan Murugappan

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Wan Khairunizam Wan Ahmad

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Sazali Yaacob

Background: Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.Methods: Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.Results: Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.Conclusions: The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.

Publication

Correction model for metal oxide sensor drift caused by ambient temperature and humidity

( 2022)

Abdulnasser Nabil Abdullah

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Zaffry Hadi Mohd Juffry

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Victor Hernandez Bennetts

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

11 12

Publication

Correction Model for Metal Oxide Sensor Drift Caused by Ambient Temperature and Humidity

( 2022-05-01)

Abdulnasser Nabil Abdullah

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Zaffry Hadi Mohd Juffry

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Bennetts V.H.

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16◦C to 30◦C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

3

Publication

Correction model for metal oxide sensor drift caused by ambient temperature and humidity

( 2022)

Abdulnasser Nabil Abdullah

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Zaffry Hadi Mohd Juffry

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Victor Hernandez Bennetts

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

1 9

Publication

Descriptive analysis of skin temperature variability of sympathetic nervous system activity in stress

( 2012-12-02)

Palanisamy Karthikeyan

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Murugappan Murugappan

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Sazali Yaacob

[Purpose] Stress is a common factor of several diseases. Stress can be reduced through appropriate stress management and relaxation methods. In this study, variation in skin temperature (ST) was investigated as a primary measure for identifying changes in stress levels. Our results should be helpful for the development of a stress measurement tool based on multimodal signals. [Subjects] Sixty healthy volunteers (30 females and 30 males) of three different races (Malay, Chinese, and Indian) with a mean age of 22.5±2.5 years participated in this study. [Methods] The Stroop color word test was used to design a data acquisition protocol of 12.36 min for this experiment. ST variation was measured continuously during the Stroop colour word test and statistical features were computed. Further, descriptive analysis and stress levels were classified using a Probabilistic Neural Network (PNN) to find the optimum features. [Results] Among the 60 subjects, the mean ST of 48 subjects (80%) rose linearly from the normal state to the high-stress state. In addition, Malay subjects were more sensitive to stress than other two races as measured by the mean skin temperature. A maximum mean classification rate of 88% was achieved for the four different stress levels on all the subjects using PNN. [Conclusion] Our investigation proves that the mean ST is a reliable measure for identifying stress level changes and may be useful for designing a multimodal stress measurement system.

Publication

Design of investment detection in fish cultivation uno arduino based

( 2022-02-22)

Arnawan Hasibuan

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Asran Asran

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Rizky Ramadhana Sembiring

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Muzamir Isa

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Mohd Irwan Yusoff

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Siti Rafidah Abdul Rahim

Fish farming is a job that many Indonesian people do, especially in villages. The nature of fish that is able to adapt quickly to transfer from pond to pond makes many residents choose to breed fish over other livestock. Many types of fish can be cultivated well, but most people choose to cultivate goldfish. Goldfish have an economic value that is quite tempting for the community. There are some fish that have a higher economic value, but people are more extra in maintaining them, for example in terms of feed. Goldfish can be fed only when the sun is hot. If the sun does not come out, then the goldfish should not be fed because it can cause the fish to die. Besides that, goldfish only need water flowing into the pond for fish oxygen. The number of people who breed fish, there are also many irresponsible people. The reason is, when the fish begin to grow up, many fish begin to disappear, making people nervous and hot. Based on the above problems, it provides a very potential opportunity to create a tool that can overcome community unrest. Fish farming theft detection tool which is an innovative tool to overcome or find out the perpetrators of theft. Thus, hopefully the tools used can reduce the risk of stealing fish and reduce the losses of fish cultivators.

Publication

Development of a symmetric ring junction as a four-port reflectometer for complex reflection coefficient measurements

( 2015)

K. Y. Lee

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B. K. Chung

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K. Y. You

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Cheng Ee Meng

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Z. Abbas

Six-port reflectometer is well-known for its ability to measure magnitude and phase-shift of microwave signal using four power detectors that perform magnitudeonly measurements. This paper presents the development of an innovative symmetric ring junction as four-port reflectometer for complex reflection coefficient measurements. It reduces the number of required detectors to two. Design optimization, new calibration modeling and algorithm are discussed in details for this four-port reflectometer. The developed four-port reflectometer is compared to five-port reflectometer and vector network analyzer. It is found that the measured magnitude and phase-shift show good performance in comparison with the commercial vector network analyzer and the five-port reflectometer.

5 9

Publication

Development of rapid and accurate method to classify malaysian honey samples using UV and colour image

( 2017)

Abd Alazeez Almaleeh

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