Browsing Sazali Yaacob, Prof. Dr. by Subject "Feature extraction"

Now showing items 1-14 of 14

    • Application of feedforward neural network for the classification of pathological voices 

      Sazali, Yaacob, Prof. Dr.; Murugesa Padiyan, Paulraj, Dr.; Mohd Rizon, Mohammed Juhari, Prof. Dr.; Muthusamy, Hariharan, Dr. (Universiti Teknologi MARA (UiTM), 2007-03-09)
      This paper present the application of feed forward neural network for the classification of pathological voices based on the on the acoustic analysis and EGG features. Acoustic analysis is a non-invasive technique based ...

    • Automated system for stress evaluation based on EEG signal: A prospective review 

      Saidatul Ardeenawatie, Awang; Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Nashrul Fazli, Mohd Nasir (Institute of Electrical and Electronics Engineers (IEEE), 2011-03-04)
      This paper reviews the issues related to the automated system for stress evaluation based on brain signal. It describes the current status of mental health especially in Malaysia. The anatomy of stress is briefly discussed ...

    • Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abd Hamid, Adom; Ramachandran, Nagarajan (Institute of Electrical and Electronics Engineering (IEEE), 2007-11-25)
      Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients ...

    • Detection of vocal fold paralysis and edema using time-domain features and probabilistic neural network 

      Hariharan, Muthusamy; Paulraj, Murugesa Pandiyan, Assoc. Prof.; Sazali, Yaacob, Prof. Dr. (Inderscience Publisher, 2011)
      This paper proposes a feature extraction method based on time-domain energy variation for the detection of vocal fold pathology. In this work, two different vocal fold problems (vocal fold paralysis and edema) are taken ...

    • Development of a personified face emotion recognition technique using fitness function 

      Muthukaruppan, Karthigayan; Mohd Rizon, Mohamed Juhari; Ramachandran, Nagarajan; Masanori, Sugisaka; Sazali, Yaacob; Mohd Rozailan, Mamat; Hazry, Desa (Springer Japan, 2007-08-10)
      In this article, two subjects, one South East Asian (SEA) and the other Japanese, are considered for face emotion recognition using a genetic algorithm (GA). The parameters relating the face emotions in each case are ...

    • Estimating Face Emotion using Genetic Algorithm 

      Karthigayan, M.; Mohd Rizon, Muhamed Juhari; Sazali, Yaacob; Nagarayan, R. (Kolej Universiti Kejuruteraan Utara Malaysia, 2006-09-15)
      Recognition of emotion through face features (Face Emotion) is a recent concept undertaken by several researchers. Face features have to be extracted from face images before applying the emotion recognition techniques. ...

    • Feature extraction based on mel-scaled wavelet packet transform for the diagnosis of voice disorders 

      Murugesa Pandian, Paulraj, Prof. Madya Dr,; Sazali, Yaacob, Prof. Dr.; Muthusamy, Hariharan, Dr. (SpringerLink, 2008-06-25)
      Feature extraction from the vocal signal plays very important role in the area of automatic detection of voice disorders. Many feature extraction algorithms have been developed in the last three decades based on acoustic ...

    • A hybrid expert system approach for telemonitoring of vocal fold pathology 

      Hariharan, Muthusamy, Dr.; Kemal, Polatb; Sindhu, Ravindran; Sazali, Yaacob, Prof. Dr. (Elsevier B.V., 2013)
      Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. ...

    • Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks 

      Muthusamy, Hariharan; Jeyaraman, Saraswathy; Sindhu, Ravindran; Wan Khairunizam, Wan Ahmad, Dr.; Sazali, Yaacob, Prof. Dr. (Elsevier Ltd, 2012-08)
      A cry is the first verbal communication of infants and it is described as a loud, high-pitched sound made by infants in response to certain situations. Infant cry signals can be used to identify physical or psychological ...

    • Japanese face emotions classification using lip features 

      Mohamad Rizon, Mohamed Juhari; Muthukaruppan, Karthigayan; Sazali, Yaacob; Nagarajan, R. (Institute of Electrical and Electronics Engineering (IEEE), 2007)
      In this paper, lip features are applied to classify the human emotion using a set of irregular ellipse fitting equations using Genetic algorithm. As Japanese, is considered in this study. All six universally accepted ...

    • A new approach for recognition of human emotions 

      Karthigayan, M.; Mohd Rizon, Muhamed Juhari; Sazali, Yaacob; Nagarajan, R. (Institut Teknologi Bandung, 2006-11-29)
      In the modern world, all elder people and young child are left alone at home. As long, they are staying alone at home will lead some depression and diversion for them. To overcome this problem, robots are implemented with ...

    • Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network 

      Hariharan, Muthusamy, Dr.; Sindhu, R; Sazali, Yaacob, Prof. Dr. (Elsevier Ireland Ltd., 2012-11)
      Crying is the most noticeable behavior of infancy. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and ...

    • Supervised neural network classifier for voice pathology 

      Murugesa Pandiyan, Paulraj, Prof. Madya Dr,; Sazali, Yaacob, Prof. Dr.; Sivanandam, S. N.; Hariharan, Muthusamy (Kongu Engineering College, 2008-01-03)
      The classification of normal and pathological voices using noninvasive acoustical analysis features helps speech specialist to perform accurate diagnoses of vocal and voice disease. Acoustic analysis is a non-invasive ...

    • Vowel recognition based on frequency ranges determined by bandwidth approach 

      Paulraj, M. P.; Sazali, Yaacob; Mohd Yusof, S. A. (Institute of Eelectrical and Electronics Engineering (IEEE), 2009-07)
      Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software especially using English as the language of choice. In this paper, a new feature extraction ...