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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science

Zaidah Ibrahim

Universiti Teknologi MARA

Author-ID : 2460595

Economics, Econometrics & Finance Electrical & Electronics Engineering

Published : 12 Documents Claim Missing Document

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Herbal plant recognition using deep convolutional neural network Izwan Asraf Md Zin; Zaidah Ibrahim; Dino Isa; Sharifah Aliman; Nurbaity Sabri; Nur Nabilah Abu Mangshor
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

This paper investigates the application of deep convolutional neural network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy.

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor Nur Hanina Izani Muhammad Zaihani; Rosniza Roslan; Zaidah Ibrahim; Khyrina Airin Fariza Abu Samah
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

There are numerous studies on brain imaging applications. The statistics in Malaysia showed that glioma is one of the most common type disease in brain tumor. Glioma brain tumor is an abnormal growth of glial cells inside the brain tissues which known as cerebral tissues. Radiologist commonly used Magnetic Resonance Imaging (MRI) image sequences to diagnose the brain tumor. However, manual examination of the brain tumor diagnosis by radiologist is difficult and time-consuming task as tumors are occurred in variability of shape and appearance. They will also inject a gadolinium contrast agent to enhance the image modality which will give the side effects to the patients. Therefore, this paper presents an automated segmentation and detection of MRI brain images using Sobel edge detection and mathematical morphology operations. The total of 30 glioma T1-Weighted MRI brain images are obtained from Brain Tumor Image Segmentation Benchmark (BRATS). The results of segmentation and detection are quantitatively evaluated by using Area Overlap which produced the accuracy rate of 80.2% and shows that the presented methods are promising.

Co-Authors Ahmad Khairi Juha Amelina Ab Nasir Dino Isa Dino Isa Gehad Saleh Ahmed Mohammed Hafizatul Hanin Hamzah Izwan Asraf Md Zin Khyrina Airin Fariza Abu Samah Mohd Afiq Zamanhuri Muhaafidz Md Saufi Nasibah Husna Mohd Kadir Nik Noor Akmal Abdul Hamid Norasiah Mohammad Norizan Mat Diah Nur Azida Muhammad Nur Hanina Izani Muhammad Zaihani Nur Nabilah Abu Mangshor Nurbaity Sabri Nursuriati Jamil Nurul Fatihah Sahidan Nurul FatihahSahidan Rabiatul Adawiya Razali Rosniza Roslan Sharifah Aliman Sharifah Nur Syafiqah Mohd Nur Hidayah

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Journal : Bulletin of Electrical Engineering and Informatics