Browsing by Author "Turaev, S."

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    BENCHMARKING OF HALAL FOOD PRODUCTS USING SIMILARITY MEASURES - A CONCEPTUAL RETRIEVAL MODEL
    (Journal of Information Systems and Digital Technologies, 2019) Tamrin, M.I.M.; Turaev, S.; Azemin, M.Z.C.; Razi, M.J.M.; Maifiah, M.H.M.
    Muslims are concerned with the Halal status of food products sold in the supermarkets. Many products that are imported from overseas are not certified by JAKIM. In this paper, we proposed a conceptual model for benchmarking food products against certified Halal products. Our motivation is to provide similarity measurement between certified and non-certified food products based on their ingredients. This model comprises three main phases: ingredient acquisition, ingredient transformation and similarity measures calculation. In the first phase, web crawlers are employed to retrieve product information from JAKIM online database and supermarket web pages. In the second phase, an index structure will be constructed to allow faster ingredient retrieval which will be used for similarity calculation. In the last phase, Euclidian distance, cosine similarity measure and Jaccard correlation coefficient will be used to measure the similarities between two products. Our proposed model is to complement but not to replace the existing JAKIM procedure to verify food products by empowering Muslim consumers with informed decision making.
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    Supervised Identification of Acinetobacter Baumanni Strains Using Artificial Neural Network
    (Journal of Information Systems and Digital Technologies, Vol. 1, No. 2, 2019) Tamrin, M.I.M.; Maifiah, M.H.M.; Azemin, M.Z.C.; Turaev, S.; Razi, M.J.M.
    In hospital environments around the world bacterial contamination is prevalence. One of the most commonly found bacteria is the Acinetobacter Baumannii. It can cause unitary tract, lung, abdominal and central nervous system infection. This bacteria is becoming more resistant to antibiotics. Thus, identification of the non-resistant from the resistant bacteria strain is of important for the correct course of treatments. We propose to use the artificial neural network (ANN) for supervised identification of this bacteria. The mass spectra generated from the liquid chromatography mass spectrometry (LCMS) were used as the features to train the ANN. However, due to the massive number of features, we applied the principle component analysis (PCA) to reduce the dimensions. Less than 1% of the original number of features were utilized. The hand out validation method confirmed that the accuracy, sensitivity and specificity are 0.75 respectively. In order to avoid selection biasness in the sampling, 5-fold cross validation was performed. In comparison, the average accuracy is close to 0.75 but the average sensitivity is slightly higher by 0.50