Institute of Engineering Mathematics
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MY
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Arau

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  • Publication
    Future leaders in reshaping an organization
    ( 2012)
    Noormaizatul Akmar Ishak
    ;
    Salmah Ayub
    ;
    Evawaynie Valquis Md. Isa
    ;
    Masnan, Maz Jamilah
    ;
    Mohd Faizal Mohd Isa
    ;
    Mohd Fisol Osman
    It is the interest of the researchers to find how the benchmarking of the future leaders in done in the Government-linked Companies GLCs. A mixed method approach has been selected as the research strategy to study the issues under investigations and the relationship between them. Six interviews with prominent experts are carried out to gather the items for the questionnaire. Then sets of constructed questionnaire are distributed to the managerial staff of Universiti Malaysia Perlis as a pilot study. To gather the data from GLCs, three Government-linked Investment Companies (GLICs) and fifteen GLCs have been identified as the samples. Then an interview with a prominent statesman is carried out, and focus group discussions with the top management representative are conducted to support the survey. The findings of the study indicate that the choice for future leadership dimensions are traits, behaviours, performance functions, competence, skills and background. These six leadership dimension qualities are desired in GLCs as the future leaders are expected to use their best endeavours to prompt or reshape the organizations.
  • Publication
    Improved maturity and ripeness classifications of Magnifera Indica cv. Harumanis Mangoes through sensor fusion of an electronic nose and acoustic sensor
    ( 2012)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Mahmad Nor Jaafar
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.
  • Publication
    A Bio-Inspired herbal tea flavour assessment technique
    ( 2014)
    Nur Zatul 'Iffah Zakaria
    ;
    Masnan, Maz Jamilah
    ;
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One famous herbal-based product is herbal tea. This paper investigates bio-inspired flavour assessments in a data fusion framework involving an e-nose and e-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion and intermediate level data fusion. Four classification approaches; LDA, SVM, KNN and PNN were examined in search of the best classifier to achieve the research objectives. In order to evaluate the classifiers' performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC-MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC-MS TIC data are varied.
  • Publication
    Improved maturity and ripeness classifications of magnifera indica cv. harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor
    ( 2012)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Mahmad Nor Jaafar
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.
  • Publication
    Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors
    ( 2010)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Masnan, Maz Jamilah
    ;
    Abdul Hallis Abdul Aziz
    ;
    Nazifah Ahmad Fikri
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.
  • Publication
    A Biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration
    ( 2011)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Mohd Noor Ahmad
    ;
    Abdul Hamid Adom
    ;
    Mahmad Nor Jaafar
    ;
    Supri A. Ghani
    ;
    Abu Hassan Abdullah
    ;
    Abdul Hallis Abdul Aziz
    ;
    Latifah Munirah Kamarudin
    ;
    Norazian Subari
    ;
    Nazifah Ahmad Fikri
    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.
  • Publication
    A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration
    ( 2011-08)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Mohd Noor Ahmad
    ;
    Abdul Hamid Adom
    ;
    Mahmad Nor Jaafar
    ;
    Supri A. Ghani
    ;
    Norazian Subari
    ;
    Nazifah Ahmad Fikri
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    ;
    Abdul Hallis Abdul Aziz
    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.
  • Publication
    A Biomimetic Sensor for the Classification of Honeys of Different Floral Origin and the Detection of Adulteration
    ( 2011)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Norazian Subari
    ;
    Nazifah Ahmad Fikri
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Mahmad Nor Jaafar
    ;
    Latifah Munirah Kamarudin
    ;
    Abdul Hallis Abdul Aziz
    ;
    Abu Hassan Abdullah
    ;
    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.
  • Publication
    Improved Classification of Orthosiphon stamineus by Data Fusion of Electronic Nose and Tongue Sensors
    ( 2010)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Masnan, Maz Jamilah
    ;
    Abdul Hallis Abdul Aziz
    ;
    Nazifah Ahmad Fikri
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.
  • Publication
    Evaluation of predictors for the development and progression of Diabetic Retinopathy among diabetes Mellitus Type 2 patients
    ( 2022-12)
    Syafawati Ab Saad
    ;
    Maz Jamilah Masnan
    ;
    Karniza Khalid
    ;
    Safwati Ibrahim
    Diabetic retinopathy is one of the microvascular complications caused by prolonged uncontrolled diabetes. It is believed that diabetic retinopathy correlates with certain predictors and risk factors that might worsen the disease, eventually causing visual loss and blindness among diabetes patients. There are some predictors and risk factors that attribute to the development and progression of diabetic retinopathy, such as the duration of diabetes and HbA1c trends. This study aims to evaluate the predictors and risk factors associated with the development and/or progression of diabetic retinopathy. Retrospective data were collected from a single healthcare facility in the northwest of Peninsular Malaysia. Patients included in this study were those with type 2diabetes mellitus diagnosed with diabetic retinopathy. The total number of patients involved in this study were 197, where 161 of them were newly diagnosed or with progressive diabetic retinopathy. The characteristics of diabetes patients with complication of diabetic retinopathy were described through descriptive statistics. Characteristics include demographics data such as age, gender, race and clinical data such as HbA1c readings HbA1c, estimated glomerular filtration rate (eGFR), urea and haemoglobin concentration (Hb). The results show that 7 predictors and risk factors are significant to the development and progression of diabetic retinopathy among diabetes patients. By using multinomial logistic regression, this study offers better understanding of the significant predictors and risk factors related to diabetic retinopathy.