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This Author published in this journals
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
Economics, Econometrics & Finance Electrical & Electronics Engineering

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Comparison of convolutional neural network and bag of features for multi-font digit recognition Nasibah Husna Mohd Kadir; Sharifah Nur Syafiqah Mohd Nur Hidayah; Norasiah Mohammad; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1322-1328

Abstract

This paper evaluates the recognition performance of Convolutional Neural Network (CNN) and Bag of Features (BoF) for multiple font digit recognition. Font digit recognition is part of character recognition that is used to translate images from many document-input tasks such as handwritten, typewritten and printed text.  BoF is a popular machine learning method while CNN is a popular deep learning method.  Experiments were performed by applying BoF with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier and compared with CNN on Chars74K dataset. The recognition accuracy produced by BoF is just slightly lower than CNN where the accuracy of CNN is 0.96 while the accuracy of BoF is 0.94.
Vehicle detection and classification using three variations of you only look once algorithm Gehad Saleh Ahmed Mohammed; Norizan Mat Diah; Zaidah Ibrahim; Nursuriati Jamil
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp442-452

Abstract

Vehicle detection and classification are essential for advanced driver assistance systems (ADAS) and even traffic camera surveillance. Yet, it is challenging due to complex backgrounds, varying illumination intensities, occlusions, vehicle size, and type variations. This paper aims to apply you only look once (YOLO) since it has been proven to produce high object detection and classification accuracy. There are various versions of YOLO, and their performances differ. An investigation on the detection and classification performance of YOLOv3, YOLOv4, and YOLOv5 has been conducted. The training images were from common objects in context (COCO) and open image, two publicly available datasets. The testing input images were captured on a few highways in two main cities in Malaysia, namely Shah Alam and Kuala Lumpur. These images were captured using a mobile phone camera with different backgrounds during the day and night, representing different illuminations and varying types and sizes of vehicles. The accuracy and speed of detecting and classifying cars, trucks, buses, motorcycles, and bicycles have been evaluated. The experimental results show that YOLOv5 detects vehicles more accurately but slower than its predecessors, namely YOLOv4 and YOLOv3. Future work includes experimenting with newer versions of YOLO.
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