Allan Melvin Andrew
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Preferred name
Allan Melvin Andrew
Official Name
Allan Melvin, Andrew
Alternative Name
Andrew, Allan Melvin
Andrew, Allan M.
Melvin, A. A.
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Scopus Author ID
36469871200
Researcher ID
GDH-3820-2022

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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
    Pollutant recognition based on supervised machine learning for Indoor air quality monitoring systems
    ( 2017)
    Shaharil Mad Saad
    ;
    Allan Melvin Andrew
    ;
    Ali Yeon Md Shakaff
    ;
    Mohd Mat Dzahir
    ;
    Mohamed Hussein
    ;
    Maziah Mohamad
    ;
    Zair Ahmad
    Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke, water-damaged building materials and outdoor pollutants. Although these gases are usually safe for humans, they could be hazardous if their amount exceeded certain limits of exposure for human health. A sophisticated indoor air quality (IAQ) monitoring system which could classify the specific type of pollutants is very helpful. This study proposes an enhanced indoor air quality monitoring system (IAQMS) which could recognize the pollutants by utilizing supervised machine learning algorithms: multilayer perceptron (MLP), K-nearest neighbour (KNN) and linear discrimination analysis (LDA). Five sources of indoor air pollutants have been tested: ambient air, combustion activity, presence of chemicals, presence of fragrances and presence of food and beverages. The results showed that the three algorithms successfully classify the five sources of indoor air pollution (IAP) with a classification rate of up to 100 percent. An MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into the IAQMS. The system has also been tested with all sources of IAP presented together. The result shows that the system is able to classify when single and two mixed sources are presented together. However, when more than two sources of IAP are presented at the same period, the system will classify the sources as ‘unknown’, because the system cannot recognize the input of the new pattern.