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All Journal International Journal of Electrical and Computer Engineering 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.
Co-Authors Ahmed Al-Ajeli Eman S. Al-shamery Haydar A. Marhoon Rafid Sagban