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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Normal and abnormal red blood cell recognition using image processing Hajara Aliyu Abdulkarim; Rubita Sudirman; Mohd Azhar Abdul Razak
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp96-100

Abstract

In medical field, the recognition of red blood cells (RBC) are used as an indicator to detect the type of diseases such as anaemia, malaria and leukaemia etc. The problems using manual detection of normal and abnormal RBCs under the microscope is tend to give inaccurate result and errors. This paper proposed a method to recognize the normal and abnormal shaped RBCs image by using Form Factor as feature descriptor. Detecting normal cells of RBCs indicate a healthy patient and abnormal cells indicate presence of disease. And is very important in medical field to detect abnormal condition in early stage because it saves and protects human lives. The patients waiting time for blood test is more because the time taking to generate the result of the patient is high due to high demand and less equipment this method is used in order to improve the accuracy of the existing one and 94% accuracy was achieved in the detection.
Significance of electrodermal activity response in children with autism spectrum disorder Awais Gul Airij; Rubita Sudirman; Usman Ullah Sheikh; Lee Yoot Khuan; Nor Aini Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp1113-1120

Abstract

The human Autonomic Nervous System (ANS) controls the body’s physiological responses such as heart rate, electrodermal activity, temperature, and pupil diameter. The physiological responses are increased in the presence of a stressing stimuli and this is a typical ANS response. However, in case of children with Autism Spectrum Disorder (ASD), they suffer from autonomic dysregulation as reported in past owing to their atypical ANS response. This study investigated the ANS response of children with ASD and compares it with the response of normal children. EDA response datasets of 35 children with ASD and 55 normal children were acquired with the help of E4 wristband at a sampling rate of 4Hz. The signals were preprocessed to remove artefacts and noise and later compared. Furthermore, an SVM classifier was also used to classify the EDA response signals of normal children and children with ASD. The obtained results highlight that the ANS response of children with ASD is atypical as their EDA response is blunt and shows no significant tonic and phasic changes in EDA levels in the presence of stressing stimuli. In addition to that, an accuracy of 75% was obtained using the LF kernel of SVM classifier. The study further unfolds the hypoactive sympathetic response of children with ASD during a stressing event. Furthermore, this will help in future to anticipate the emotional responses of children with ASD such as anger, happiness, and anxiety.