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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Md. Jakir Hossen
Multimedia University
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Acute lymphoblastic leukemia detection approach from peripheral blood smear using color threshold and morphological techniques Abdullah Al Mamun; Md. Jakir Hossen; Anik Tahabilder; Ahmmad Musha; Rehnuma Hasnat; Sohag Kumar Saha
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3692-3699

Abstract

Acute lymphoblastic leukemia (ALL) has recently been one of the most significant concerns in cancers, especially child and old age. Therefore, crying needs to diagnose leukemia as early as possible, increasing the treatment options and patient survivability. Some basic handicraft leukemia detection processes have been introduced in this arena though these are not so accurate and efficient. The proposed approach has been introduced an automated ALL recognition system from the peripheral blood smear. Initially, the color threshold has been applied to segment lymphocytes blood cells from the blood smear. Some post-processing techniques like morphological operation and watershed have been executed to segment the particular lymphocytes cell. Finally, we used a support vector machine (SVM) classifier to classify the cancerous image frames using a statistical feature vector obtained from the segmented image. The proposed framework has achieved the highest accuracy of 99.21%, the sensitivity of 98.45%, specificity of 99%, the precision of 99%, and F1 score of 99.1%, which has beat existing and common states of art methods. We are confident that the proposed approach will positively impact the ALL detection arena.
Stress detection during job interview using physiological signal Ali Afzalian Mand; Md. Shohel Sayeed; Md. Jakir Hossen; Muhammad Amer Ridzuan bin Zuber
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5531-5542

Abstract

A job interview can be challenging and stressful even when one has gone through it many times. Failure to handle the stress may lead to unsuccessful delivery of their best throughout the interview session. Therefore, an alternative method which is preparing a video resume and interview before the actual interview could reduce the level of stress. An intelligent stress detection is proposed to classify individuals with different stress levels by understanding the physiological signal through electrocardiogram (ECG) signals. The Augsburg biosignal toolbox (AUBT) dataset was used to obtain the state-of-art results. Only five selected features are significant to the stress level were fed into neural network multi-layer perceptron (MLP) as the optimum classifier. This stress detection achieved an accuracy of 92.93% when tested over the video interview dataset of 10 male subjects who were recording the video resume for the analysis purposes.
Co-Authors Abdullah Al Mamun Ahmmad Musha Ali Afzalian Mand Anik Tahabilder Joseph Emerson Raja Md. Shohel Sayeed Muhammad Amer Ridzuan bin Zuber Rehnuma Hasnat Sohag Kumar Saha Thirumalaimuthu Ramanathan Thirumalaimuthu Thirumalaiappan Ramanathan