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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Raaid Alubady
University of Babylon
Computer Science & IT Electrical & Electronics Engineering

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Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care Rafid Sagban; Haydar A. Marhoon; Raaid Alubady
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6655-6663

Abstract

Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.
Improving spam email detection using hybrid feature selection and sequential minimal optimisation Ahmed Al-Ajeli; Raaid Alubady; Eman S. Al-Shamery
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp535-542

Abstract

Communication by email is counted as a popular manner through which users can exchange information. The email could be abused by spammers to spread suspicious content to the Internet users. Thus, the need to an effective way to detect spam emails are becoming clear to keep this information safe from malicious access. Many methods have been developed to address such a problem. In this paper, a machine learning technique is applied to detect spam emails. In this technique, a detection system based on sequential minimal optimization (SMO) is built to classify emails into two categories: spam and non-spam (ham). Each email is represented by a set of features extracted from its textual content. A hybrid feature selection is developed to choose a subset of these features based on their importance in process of the detection. This subset is then input into the SMO algorithm to make the detection decision. The use of such a technique provides an efficient protective mechanism to control spams. The experimental results show that the performance of the proposed method is promising compared with the existing methods.
Develop a new handling method for selfish nodes in mobile ad-hoc networks Sanaa Jafaar Hassan Al-Shakarchi; Raaid Alubady
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4895

Abstract

Mobile ad-hoc networks (MANETs) have been a crucial element of next-generation wireless networking technologies during the last decade. Because they allow users to access information and communicate with each other without infrastructure. Selfishness is one of the numerous undesirable behaviors that MANET network nodes may exhibit since this selfish node attempts to safeguard its own resources while accessing the services of other nodes and consuming their resources. Hence, a potential that the network's overall performance may degrade. This study developed a new method named detection, reintroduced, and collaborative of selfish node (DRCSN) that proposed detecting selfish nodes based on two factors: energy and the communication ratio (CR) and handling the rate of selfish nodes. Thus, selfish nodes were exploited to the maximum degree and significantly improve network performance. DRCSN was implemented inside ad-hoc on-demand distance vector (AODV) protocol. The test scenarios were implemented using the network simulator-2 (NS-2); many scenarios were created according to two important network parameters: the number of nodes and movement nodes. The proposed method improved the MANET's performance by increasing both the throughput and packet delivery ratio in the network in addition to that it reduced retransmission rate, delay, and power consumption compared to the related methods.
Co-Authors Ahmed Al-Ajeli Eman S. Al-shamery Haydar A. Marhoon Rafid Sagban Sanaa Jafaar Hassan Al-Shakarchi