Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES)
Hassan, Mohamed
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
FAST LOCAL FLOW-BASED METHOD USING PARALLEL MULTI-CORE CPUS ARCHITECTURE Moneim, Wafaa Abdel; Salem, Rashed; Hassan, Mohamed
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 9, No 3: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v9.i3.pp%p

Abstract

Large graphs are available in everywhere such as social networks, chemistry, web link analysis, biology, image processing, and computer networks. Traditional methods of clustering are not suitable to solve this problem due to the computation is very costly. This problem is solved by local graph clustering using a given vertex set as input without working on the complete graph to detect a good cluster. SimpleLocal is introduced and analyzed for locally-biased graph-based learning. This algorithm detects a best conductance cuts close to seed vertices set. In this paper, a new Parallel SimpleLocal (PSL) system is proposed using multi-core CPUs. OMP parallel library is utilized to parallelize the first and second stages of 3StageFlow algorithm where the SimpleLocal algorithm is used it for enhancing the runtime. The experiments are performed on two applications which are image segmentation and community detection. From the experiments, the proposed method improves the runtimes with 72.75% using 4-cores and 81.01% when using 8-cores over the sequential single core
Apple fruits categorizing based on deep convolutional neural network techniques Hussain, Nashaat; Zaki, Gihan; Hassan, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3695-3702

Abstract

For a variety of reasons, including the high degree of similarity between varieties of the same type of fruit, the requirement to train the technique on a large amount of data, and the type and number of features suitable for application, the use of computer vision techniques in the classification of fruits still faces many challenges. Additionally, the technique's effectiveness and speed both need to be improved. Deep conventional neural network (DCNN) approaches were required for all of these reasons. A proposed CNN model is described in this work. The suggested methodology is intended to quickly and accurately categorize thirteen groups of apple fruits. The proposed technique was based on training and testing the model on a maximum number of images of apple fruits, by increasing the number of database images tenfold, after augmentation was performed on the images. The technology also relied on good tuning of the hyperparameters. To further ensure the efficiency of training, validation was performed on 20% of the database. All results that demonstrate the high efficiency of the proposed model were reviewed. The results of the proposal were compared with the results of four related techniques. The results showed the great advantage of the proposed technology at all levels.
Co-Authors Hussain, Nashaat Moneim, Wafaa Abdel Salem, Rashed Zaki, Gihan