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All Journal Bulletin of Electrical Engineering and Informatics
Alaa Taima Albu-Salih
Al-Qadisiyah University
Electrical & Electronics Engineering

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Convolutional neural network for color images classification Nora Ahmed Mohammed; Mohammed Hamzah Abed; Alaa Taima Albu-Salih
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3730

Abstract

Artificial intelligent and application of computer vision are an exciting topic in last few years, and its key for many real time applications like video summarization, image retrieval and image classifications. One of the most trend method in deep learning is a convolutional neural network, used for many applications of image processing and computer vision. In this work convolutional neural networks CNN model proposed for color image classification, the proposed model build using MATLAB tools of deep learning. In addition, the suggested model tested on three different datasets, with different size. The proposed model achieved highest result of accuracy, precision and sensitivity with the largest dataset and it was as following: accuracy is 0.9924, precision is 0.9947 and sensitivity is 0.9931, compare with other models.
A clustering approach to improve VANETs performance Hayder Ayad Khudhair; Alaa Taima Albu-Salih; Mustafa Qahtan Alsudani; Hassan Falah Fakhruldeen
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5086

Abstract

Vehicular ad-hoc network (VANET) is a technique that uses cars moved in cities or highways as nodes in wireless networks. Each car in these networks works as a router and allows cars in the range to communicate with each other. As a result of this movement, some cars will become out of range, but these networks can connect to the internet and the cars in these networks can connect to each other. This research proposes a unique clustering strategy to improve the performance of these networks by making their clusters more stable. One of the biggest problems these networks face is traffic data, which consumes network resources. Agent based modeling (ABM) evaluates better networks. The evaluation showed that the proposed strategy surpasses earlier techniques in reachability and throughput, but ad hoc on-demand distance vector (AODV) (on-demand/reactive) outperforms it in total traffic received since our hybrid approach needs more traffic than AODV.
Co-Authors Hassan Falah Fakhruldeen Hayder Ayad Khudhair Mohammed Hamzah Abed Mustafa Qahtan Alsudani Nora Ahmed Mohammed