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Wamidh Jalil Mazher
Southern Technical University

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Impact of pointing error on SISO/MISO drones swarm-based free space optical system in weak turbulence regime Abdullah Jameel Mahdi; Wamidh Jalil Mazher; Osman Nuri Ucan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp918-926

Abstract

Applying the drone-based free space optical (FSO) technology is recent in communication systems. The FSO technology hashigh-security features dueto narrow beamwidth, insusceptible to interferences, free license and landline connection is not appropriate. However, these advantages face many obstacles that affect the system's performance, such as random weather conditions and misalignment. The pointing error Hpis one of the critical factors of the channel gain H. The related parameters of the Hp factor: the pointing error angles θr and the path length Z, were manipulated to extract the applicable values at various receiver diameter values. The proposed system has two topologies: single input single output (SISO) and multiple input single output (MISO), flying in weak atmospheric turbulence. The simulation was done using MATLAB software 2020. The average bit error rate (ABER) for the system versus signal-to-noise ratio (SNR) were verified and analyzed. The results showed that at θr=10−3rad, Z increased in the range 10~100m for each one-centimeter increase of DR. At θr=10−2rad, the applicable Z was nearly 10% of the link distance Z when θr=10−3rad was applied. Consequently, an increase in θr must correspond decrease in Z and vice versa to maintain the system at high performance.
Modified Harris Hawks optimizer for feature selection and support vector machine kernels Hadeel Tariq Ibrahim; Wamidh Jalil Mazher; Enas Mahmood Jassim
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp942-953

Abstract

The support vector machine (SVM), one of the most effective learning algorithms, has many real-world applications. The kernel type and its parameters have a significant impact on the SVM algorithm's effectiveness and performance. In machine learning, choosing the feature subset is a crucial step, especially when working with high-dimensional data sets. These crucial criteria were treated independently in the majority of earlier studies. In this research, we suggest a hybrid strategy based on the Harris Hawk optimization (HHO) algorithm. HHO is one of the lately suggested metaheuristic algorithms that has been demonstrated to be used more efficiently in facing some optimization problems. The suggested method optimizes the SVM model parameters while also locating the optimal features subset. We ran the proposed approach HHO-SVM on real biomedical datasets with 17 types of cancer for Iraqi patients in 2010-2012. The experimental results demonstrate the supremacy of the proposed HHO-SVM in terms of three performance metrics: feature selection accuracy, runtime, and number of selected features. The suggested method is contrasted with four well-known algorithms for verification: firefly (FF) algorithm, genetic algorithm (GA), grasshopper optimization algorithm (GOA), and particle swarm algorithm (PSO). The implementation of the proposed HHO-SVM approach reveals 99.967% average accuracy.
Co-Authors Abdullah Jameel Mahdi Enas Mahmood Jassim Hadeel Tariq Ibrahim Osman Nuri Ucan