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All Journal Bulletin of Electrical Engineering and Informatics
Mohd Ikmal Fitri Maruzuki
Universiti Teknologi MARA
Electrical & Electronics Engineering

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Systematic literature review: application of deep learning processing technique for fig fruit detection and counting Ahmad Shukri Firdhaus Kamaruzaman; Adi Izhar Che Ani; Mohammad Afiq Hamdani Mohammad Farid; Siti Juliana Abu Bakar; Mohd Ikmal Fitri Maruzuki; Samsul Setumin; Mokh. Sholihul Hadi
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4455

Abstract

Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.
Performance evaluation of generative adversarial networks for generating mugshot images from text description Nur Nabilah Bahrum; Samsul Setumin; Nor Azlan Othman; Mohd Ikmal Fitri Maruzuki; Mohd Firdaus Abdullah; Adi Izhar Che Ani
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5895

Abstract

The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.
Co-Authors Adi Izhar Che Ani Ahmad Shukri Firdhaus Kamaruzaman Mohammad Afiq Hamdani Mohammad Farid Mohd Firdaus Abdullah Mokh Sholihul Hadi Nor Azlan Othman Nur Nabilah Bahrum Samsul Setumin Siti Juliana Abu Bakar