Ammar Zakaria
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Preferred name
Ammar Zakaria
Official Name
Ammar, Zakaria
Alternative Name
Zakaria, Ammar
Zakaria, A.
Main Affiliation
Scopus Author ID
36560557700
Researcher ID
D-2902-2015

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  • Publication
    Multi-Stage feature selection based intelligent classifier for classification of incipient stage fire in building
    ( 2016)
    Allan Melvin Andrew
    ;
    Ammar Zakaria
    ;
    Shaharil Mad Saad
    ;
    Ali Yeon Md Shakaff
    In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or “smellprint” emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalised feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odour signals in the time domain. The obtained features undergo the proposed multi-stage feature selection technique and lastly, further reduced by Principal Component Analysis (PCA), a dimension reduction technique. The hybrid PCA-PNN based approach has been applied on different datasets from in-house developed system and the portable electronic nose unit. Experimental classification results show that the dimension reduction process performed by PCA has improved the classification accuracy and provided high reliability, regardless of ambient temperature and humidity variation, baseline sensor drift, the different gas concentration level and exposure towards different heating temperature range.
  • 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 hybrid sensing approach for pure and adulterated honey classification
    ( 2012)
    Norazian Subari
    ;
    Junita Mohamad Saleh
    ;
    Ali Yeon Md Shakaff
    ;
    Ammar Zakaria
    This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.
  • 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.