Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Abdullah Bade
Universiti Malaysia Sabah
Computer Science & IT Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images Stefanus Kieu Tao Hwa; Mohd Hanafi Ahmad Hijazi; Abdullah Bade; Razali Yaakob; Mohammad Saffree Jeffree
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (333.062 KB) | DOI: 10.11591/ijai.v8.i4.pp429-435

Abstract

Tuberculosis (TB) is a disease caused by Mycobacterium Tuberculosis. Detection of TB at an early stage reduces mortality. Early stage TB is usually diagnosed using chest x-ray inspection. Since TB and lung cancer mimic each other, it is a challenge for the radiologist to avoid misdiagnosis. This paper presents an ensemble deep learning for TB detection using chest x-ray and Canny edge detected images. This method introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers. The first set of features were extracted from the original x-ray images, while the second set of features were extracted from the edge detected image. To evaluate the proposed approach, two publicly available datasets were used. The results show that the proposed ensemble method produced the best accuracy of 89.77%, sensitivity of 90.91% and specificity of 88.64%. This indicates that using different types of features extracted from different types of images can improve the detection rate.
Hybrid Collision Culling by Bounding Volumes Manipulation in Massive Rigid Body Simulation Norhaida Mohd Suaib; Abdullah Bade; Dzulkifli Mohamad
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 6: June 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Collision detection is an important aspect in many real-time simulation environments.  Due to its nature of high Computation involved, collision detection can contribute to the bottleneck on the system involving large number of interacting objects.  This paper focuses on finding options to efficiently cull away object pairs that are not likely to collide in large-scale dynamic rigid-body simulations involving n-body objects.  The main idea is to perform time critical computing concept by manipulation of potential bounding volume techniques.  In order to take advantage of a fast collision test and a more accurate result, a hybrid collision culling approach based on sphere - or-Dops was used.  Based on initial results, this approach shows a potential adaptation to a massive rigid body simulation. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2656
Velocity Perception: Collision Handling Technique for Agent Avoidance Behavior Nazreen Abdullasim; Ahmad Hoirul Basori; Md Sah Hj Salam; Abdullah Bade
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 4: April 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Collision avoidance behavior is always about maintaining free collision between virtual objects. It is also about generating evasion routing for the agents in virtual environment such as in crowd simulation. It consists of three processes which are construction of Field of Vision, Collision handling and collision response. Constructing field of vision is always a daunting task and always in enigma for the designer because it is subjected towards agent’s perception which is varies to each of them. There are few attempts on designing field of vision based on the agent’s dynamic focus toward its surrounding. Therefore, we present a top down approach study from crowd simulation modeling until the collision handling level in order to identify the suitable crowd modeling for our approach. Hence, at the end of this paper we will be able to discuss the possible techniques for constructing agent’s field of vision and analyze its potential in crowd simulation environment. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2599
Co-Authors Ahmad Hoirul Basori Dzulkifli Mohamad Md Sah Hj Salam Mohammad Saffree Jeffree Mohd Hanafi Ahmad Hijazi Nazreen Abdullasim Norhaida Mohd Suaib Razali Yaakob Stefanus Kieu Tao Hwa