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Articles by Shyamala Doraisamy
Total Records ( 4 ) for Shyamala Doraisamy
  Farshad Arvin , Shyamala Doraisamy and Marjan Javanmard
  In the present study, a fruit dryer system that is controlled based on fuzzy logic is presented. A laboratory scale cabinet was developed which includes four sensors in different lengths for monitoring the cabin temperature and humidity. Fuzzy base controller is a new monitoring technique in food industrial machines that utilize sensors captured values as its input parameters to make a suitable decision according to temperature values. Furthermore, to implement the fuzzy system, a microcontroller base monitoring system is developed. Microcontroller captured temperature samples and converted them in to digital values. Output of the fuzzy controller will control the speed of the fan and power of the heater. Several performed results indicated the amenability of the proposed monitoring system as a drying machine main controller in different drying curves. Fluctuation of the cabin temperature with fuzzy control was smoother than non-fuzzy control. Nevertheless, fuzzy control has a significant influence on the power consumption as well.
  Ehsan Safar Khorasani , Shyamala Doraisamy and Azreen Azman
  In this study, a musical approach to provide an automatic heart disease detection system is proposed. Heart sounds are recorded with audio format. Audio files are converted to semi-structured music files that can be represented textually. Samples were captured from different heart diseases and were stored in a database. Two different approaches which are information retrieval based on n-gram and longest common subsequence are used to retrieve the similarity of a given sample with existing heart diseases in the database. Since the frequency of heart sound is relative to age and physical characteristics of a patient, an important feature of using n-gram in this study is to retrieve diseases without respect to the different heart sounds frequencies. The effects of window sizes for n-gram approach on the accuracy of the information retrieval were tested and a proper window size was extracted. The results of the performed experiments showed that window size of 5 notes revealed a high performance in comparison with other window sizes. Hence, the proposed technique can detect and recognize a heart disease with a reliable accuracy. Average of precision values for around 85% in information retrieval and 55% in longest common subsequence technique were obtained for the retrieval of heart sound categories. Moreover, the results of string matching technique demonstrated that threshold level of 65% could appropriately detect heart disease.
  Teh Chao Ying , Shyamala Doraisamy and Lili Nurliyana Abdullah
  Music documents are often classified based on genre and mood. In recent years, features from lyrics text have been used for classification of musical documents and the feasibility of lyrics features to classify musical documents has been shown. In this study an approach to lyrics based musical genre classification was presented which utilizing mood information. From the analysis of the lyrics text in the data collection, correlation of terms between genre and mood was observed. Based on this correlation of terms, new weighting equation with combine weights from genre and mood was introduced and implemented in two different ways. Ten musical genre and mood categories were selected respectively based on a summary from the literature. Musical genre classification experiments were performed using a test collection consists of 1000 English songs. To confirm present approach can improve the genre classification, experiments were conducted using similar weighting metric from previous study. Experimental results with new weighting equation reveal improvement in musical genre classification.
  Shahram Golzari , Shyamala Doraisamy , Md Nasir Sulaiman and Nur Izura Udzir
  Problem statement: Artificial Immune Recognition System (AIRS) is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation method is proposed to make AIRS more efficient. New algorithm, AIRS with proposed nonlinear method, is tested on benchmark datasets from UCI machine learning repository. Results: Based on the results of experiments, using proposed nonlinear resource allocation method decreases the training time and number of memory cells and doesn’t reduce the accuracy of AIRS. Conclusion: The proposed classifier is an efficient and effective classifier.
 
 
 
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