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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Ahmad Fauzan Kadmin
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering Engineering

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Implementation of Algorithm for Vehicle Anti-Collision Alert System in FPGA Aiman Zakwan Jidin; Lim Siau Li; Ahmad Fauzan Kadmin
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 2: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (434.116 KB) | DOI: 10.11591/ijece.v7i2.pp775-783

Abstract

Vehicle safety has becoming one of the important issues nowadays, due to the fact the number of road accidents, which cause injuries, deaths and also damages, keeps on increasing. One of the main factors which contribute to these accidents are human's lack of awareness and also carelessness. This paper presents the development and implementation of an algorithm to be utilized for vehicle anti-collision alert system, which may be useful to reduce the occurrence of accidents. This algorithm, which is to be deployed with the front sensors of the vehicle, is capable of alerting any occurrence of sudden slowing or static vehicles ahead, by sensing the rate of distance change. Furthermore, it also triggers an alert if the driver is breaching the safe distance from the vehicle ahead. This algorithm has been successfully implemented in Altera DE0 FPGA and its functionality was validated via hardware experimental tests.
A new function of stereo matching algorithm based on hybrid convolutional neural network Mohd Saad Hamid; Nurulfajar Abd Manap; Rostam Affendi Hamzah; Ahmad Fauzan Kadmin; Shamsul Fakhar Abd Gani; Adi Irwan Herman
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp223-231

Abstract

This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.
Assessing corn quality classification with deep-learning module Monther Yousef; Zamani Md Sani; Ahmad Fauzan Kadmin; Mohammed Ahmed Salem; Masrullizam Mat Ibrahim; Hatem T. M. Duhair
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4096-4104

Abstract

This study presents a new classification framework for corn quality assessment using the you only look once version 8 (YOLOv8) deep learning architecture. Traditional corn-sorting methods are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study proposes the corn quality classification (CQC) algorithm, an artificial intelligence (AI)-driven approach for automatically recognizing and categorizing pest- and disease-related defects. The proposed model uses YOLOv8, which integrates a convolutional neural network (CNN) backbone for feature extraction and object detection. The system was trained on labelled corn images to classify them into three categories: healthy, water rot, and bug. The model achieved an overall classification accuracy of 92.7%, with class-specific accuracies of 88.9% for healthy, 70% for water rot, and 100% for bug. Key performance metrics included a recall of 93%, an F1-score of 62%, a mean average precision (mAP) of 92.7%, and an intersection over union (IoU) of 52.3%. Additionally, the F1-confidence and recall-confidence curves provided further insights into the model’s performance. The proposed classification framework and CQC algorithm demonstrate the feasibility of AI-powered agricultural monitoring systems, offering a reliable and efficient approach to corn quality assessment. By enabling consistent and accurate classification, the proposed system has the potential to reduce labor requirements and support precision agriculture practices for improved crop quality management.
Co-Authors Adi Irwan Herman Aiman Zakwan Jidin Hatem T. M. Duhair Lim Siau Li Masrullizam Mat Ibrahim Mohammed Ahmed Salem Mohd Saad Hamid Monther Yousef Nurulfajar Abd Manap Rostam Affendi Hamzah Shamsul Fakhar Abd Gani Zamani Md Sani