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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Zamri Ibrahim
Universiti Malaysia Pahang
Computer Science & IT Electrical & Electronics Engineering

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A novel fern-like lines detection using a hybrid of pre-trained convolutional neural network model and Frangi filter Heri Pratikno; Mohd Zamri Ibrahim; Jusak Jusak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.23319

Abstract

Full ferning is the peak of the formation of a salt crystallization line pattern shaped like a fern tree in a woman’s saliva at the time of ovulation. The main problem in this study is how to detect the shape of the salivary ferning line patterns that are transparent, irregular and the surface lighting is uneven. This study aims to detect transparent and irregular lines on the salivary ferning surface using a comparison of 15 pre-trained convolutional neural network models. To detect fern-like lines on transparent and irregular layers, a pre-processing stage using the Frangi filter is required. The pre-trained convolutional neural network model is a promising framework with high precision and accuracy for detecting fern-like lines in salivary ferning. The results of this study using the fixed learning rate model ResNet50 showed the best performance with an error rate of 4.37% and an accuracy of 95.63%. Meanwhile, in implementing the automatic learning rate, ResNet18 achieved the best results with an error rate of 1.99% and an accuracy of 98.01%. The results of visual detection of fern-like lines in salivary ferning using a patch size of 34×34 pixels indicate that the ResNet34 model gave the best appearance
Camera-projector calibration for near infrared imaging system Marlina Yakno; Junita Mohamad-Saleh; Mohd Zamri Ibrahim; W. N. A. W. Samsudin
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (623.929 KB) | DOI: 10.11591/eei.v9i1.1697

Abstract

Advanced biomedical engineering technologies are continuously changing the medical practices to improve medical care for patients. Needle insertion navigation during intravenous catheterization process via Near infrared (NIR) and camera-projector is one solution. However, the central point of the problem is the image captured by camera misaligns with the image projected back on the object of interest. This causes the projected image not to be overlaid perfectly in the real-world. In this paper, a camera-projector calibration method is presented. Polynomial algorithm was used to remove the barrel distortion in captured images. Scaling and translation transformations are used to correct the geometric distortions introduced in the image acquisition process. Discrepancies in the captured and projected images are assessed. The accuracy of the image and the projected image is 90.643%. This indicates the feasibility of the captured approach to eliminate discrepancies in the projection and navigation images.
Kelulut honey-filled pots detection using image processing based techniques Wan Nur Azhani W. Samsudin; Mohd Harizan Zul; Mohd Zamri Ibrahim; Rohana Abdul Karim
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1028-1036

Abstract

Kelulut bee is one of the stingless bee species in Malaysia, which is not dangerous to human. Honey from Kelulut bee can be used for the treatment of a variety of illness. The awareness of honey nutrition in our health makes it received high demands from the consumers. Traditionally, beekeepers did the manual inspection to check the honey-filled pots by using the straw or needle. The high demand from the consumers and the greater size of Kelulut beehive make it impractical to check manually all the honeypots which are time-consuming. The hygiene of the collected honey is also important to produce a good quality of honey. Hence, an automated honey-filled pots detection system is proposed to overcome these limitations. The proposed system will reduce the time consuming and less prone to error of the wrong estimation of honey-filled pots. MATLAB software is used to process the image of the Kelulut beehive which is challenging due to the overlapped honeypots in the image. Using the proposed algorithm, it can detect whether the pots filled with honey or not by using image processing techniques and it will analyse the image which represents the percentage amount of honey in the beehives.
Co-Authors Heri Pratikno Junita Mohamad-Saleh Jusak Jusak Marlina Yakno Mohd Harizan Zul Rohana Abdul Karim W. N. A. W. Samsudin Wan Nur Azhani W. Samsudin