Motasem Al Smadi

Year: 2018

Academic Degree: PhD

Supervisor Type: Co-supervisor

Supervisor State: Graduated

Multi-objectivization Evolutionary Approach for Ab Initio Protein Prediction

Qusay M. Alzubi

Year: 2018

Academic Degree: PhD

Supervisor Type: Co-supervisor

Supervisor State: Graduated

Hybridizing Binary Grey Wolf Optimization with Particles Swarm Optimization Based intrusion Detection System

Taief A. Hamdi

Year: 2018

Academic Degree: Master

Supervisor Type: Co-supervisor

Supervisor State: Graduated

Enhanced Grey Wolf Algorithm as Feature Selection Mechanism to Improve the Accuracy of Intrusion Detection System

Ahmad Mohd Aziz Hussein

Year: 2018

Academic Degree: PhD

Supervisor Type: Co-supervisor

Supervisor State: Graduated

Hybridizing an improved flower pollination algorithm with the profile technique and genetic algorithm for multiple sequence alignment

PhD

Apr 2, 2011 - Sep 30, 2017

PhD at School of Computer Sciences, Universiti Sains Malaysia

Master

Jan 1, 2009 - Jul 30, 2011

MSc at School of Arts and Sciences, Universiti Utara Malaysia

Bachelor

Sep 10, 2003 - Jul 30, 2007

Bachelor of Science from College of Computer Sciences and Mathematics/ Mosul University

Northern technical University, Technical Engineering College for Computer and AI / Mosul, Department of Cyber Security and Cloud Computing Techniques Engineering, [Assistant Dean for Scientific and Student Affairs]

Aug 1, 2024 - Present

Leading, Monitoring, Research engagements, Supervision

Northern technical University, Technical Engineering College for Computer and AI / Mosul, Department of Cyber Security and Cloud Computing Techniques Engineering, [Lecturer]

Feb 22, 2023 - Present

Teaching Introduction to Programing, OOP, Linux administration, writing
research papers, performing workshops and seminars for faculty
members.

Nineveh University, Faculty of Information Technology (IT), software department, visitor lecture. [visitor lecture.]

Mar 1, 2022 - Jan 1, 2023

Teaching data Structure in python, files processing.

Universiti Sains Malaysia, National Advance IPv6 Center, Teaching Fellow [Teaching Fellow]

Jan 1, 2018 - Dec 31, 2019

Teaching Internet Governance, Internet of Things for postgraduate master’s degree student; Supervisor Assistant for 4 PhD students and 1 master student.

Universiti Sains Malaysia, School of Computer Sciences, [Graduate Research Assistant]

Jan 1, 2016 - Jun 30, 2016

Research, Experimental Tests, Evaluation and Writing Research Reports
for the project” MEMBRANE COMPUTING BASED MODELS FOR IMAGE
ANALYSIS (1001/PKOMP/811290)”.

Universiti Sains Malaysia, School of Computer Sciences, Graduate Research Assistant [Graduate Research Assistant]

Jan 1, 2015 - Jun 30, 2015

Research, Experimental Tests, Evaluation and Publishing papers for
project: “Protein 3D Structure Prediction Using Hybrid Artificial Bee
Colony Algorithm.

Universiti Sains Malaysia, School of Computer Sciences, Graduate Research Assistant [Graduate Research Assistant]

Jan 1, 2014 - Nov 30, 2014

Research, Experimental Tests, Evaluation and Writing Research Papers
for:
“BIO-INSPIRED OPTIMIZATION METHOD FOR FEATURE SELECTION OF MASS
SPECTOMETERY ANALYSIS IN BIOMARKER IDENTIFICATION OF OVARIAN
CANCER (203/PKOMP/6711268)”.

Universiti Utara Malaysia, School of Computing, Research Assistant [Research Assistant]

Nov 1, 2012 - Feb 28, 2013

Research, Experimental Tests and Evaluation for:
Manpower Planning Optimization Using Artificial Bee Colony Algorithm.
A Swarm Intelligence Algorithm to Price Options.
Optimization Algorithm for Data Grids.

Universiti Utara Malaysia, School of Computing, Research Assistant [Research Assistant]

Nov 1, 2010 - Feb 28, 2011

Developing Analysis Applications, Writing Reports for Research
Purposes.

Improving CNTs properties using computational intelligence algorithms

Feb 22, 2024

Journal International Journal of Materials and Product Technology

publisher inder science online

DOI https://doi.org/10.1504/IJMPT.2024.136839

Volume 68

Carbon nanotubes (CNTs) have emerged in various applications due to their outstanding characteristics. The most common technique for producing CNTs with high yield and quality is known as chemical vapour deposition (CVD). However, manufacturers rely on conventional experimental studies to produce CNTs, which raise issues such as time, cost, and dealing with toxic materials. Alternatively, modelling and optimisation using metaheuristic algorithms are suggested to address these issues. This paper uses response surface methodology (RSM) for modelling work, while four metaheuristic algorithms are employed for optimisation. The regression and mathematical models, correlations, and significant CNTs process parameters are identified, analysed, and validated using RSM. The optimisation process and result are validated using different performance measure metrics and supported by other researchers. The CNTs yield and quality values improvement percentages in this paper are up to 36.45% compared to the referred original work.

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Pervasive computing of adaptable recommendation system for head-up display in smart transportation

Sep 1, 2022

Journal Computers and Electrical Engineering

publisher Elsevier

DOI https://doi.org/10.1016/j.compeleceng.2022.108204

Volume 102

Pervasive computing aims to simplify our lives by efficiently managing information in different fields such as transportation, and healthcare. Smart transportation has become an integral part of our modern society and is attractive for pervasive computing. Head-Up Display (HUD) assists users in locating and identifying objects and humans by establishing volatile contact with them. HUD is aided by computer vision (CV) techniques and used in smart transportation for human assistance. An Adaptable Recommendation System (ARS) using an analytical CV (ACV) in smart transportation is introduced to improve the swiftness in detecting objects in a multi-layer smart city environment. The proposed system is backhauled using deep, short-term memory networks to identify and verify the layers' correctness in detecting the target with a reduced time factor. The application's design concentrates on enlightening HUD for end-user recommendations. The HUD applications with the recommended system achieve less time, error, and computations.

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RETRACTED: Affirmative data analytics based data processing method for 6G wireless network applications

Jun 21, 2022

Journal Transactions on Emerging Telecommunications Technologies

publisher wiley

DOI https://doi.org/10.1002/ett.4583

Volume 35

Sixth generation (6G) wireless network infrastructure makes use of terahertz communication interfaces and latency service. The growth of real-time applications and service support increases the data handling capacity and processing requirements. The data processing rate must be comparatively high with the available resources to meet the users' quality of service (QoS) requirements. This article proposes an affirmative data analytics (ADA) method to improve data processing consistency in 6G wireless networks. The consistency of the ADA method relies on the 6G service features in the service-rendering environment. The affirmation process is provided using support vector machine (SVM) learning to achieve consistency in handling diverse-attributed data. Then the attribute and association between data and services are achieved with best-fit processing time and minimum complexity. The performance of the proposed ADA is verified for heterogeneous service applications in a wireless network using the metrics analysis time (723.629 ms), complexity (11.311%), and response time (1.034 s).21

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A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization

Feb 22, 2020

Journal The Journal of Supercomputing

publisher Springer Nature

DOI https://doi.org/10.1007/s11227-019-03083-2

Volume 76

Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efficacy in solving various types of real-world optimization problems. However, it is impossible to find an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. ABC is one of the most popular algorithms in solving different kinds of optimization problems. However, it has a weak local search performance where the equation of solution search in ABC performs good exploration, but poor exploitation. Besides, it has a fast convergence and can therefore be trapped in the local optima for some complex multimodal problems. In order to address such issues, this paper proposes a novel hybrid ABC with outstanding local search algorithm called β-hill climbing (βHC) and denoted by ABC–βHC. The aim is to improve the exploitation mechanism of the standard ABC. The proposed algorithm was experimentally tested with parameters tuning process and validated using selected benchmark functions with different characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using different common measurement metrics. The result showed that the proposed ABC–βHC had faster convergence in most benchmark functions and outperformed eight algorithms including the original ABC in terms of all the selected measurement metrics. For more validation, Wilcoxon’s rank sum statistical test was conducted, and the p values were found to be mostly less than 0.05, which demonstrates that the superiority of the proposed ABC–βHC is statistically significant.

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Threats against information privacy and security in social networks: A review

Jan 17, 2020

Journal International Conference on Advances in Cyber Security

publisher Springer Singapore

DOI https://doi.org/10.1007/978-981-15-2693-0_26

Volume 1132

This review paper is an attempt to cover the arising threats against information privacy and security in the attractive Social Network environment that represents a rich mine of user personal data. First, the paper discusses the information privacy, while many researches have been found in the relevant literature with respect to privacy in Social Networks, more efforts are needed especially on data leakages that happen to each entity including Social Network users, service providers, third and external parties, and how data linkages can produce useful information to these parties. Second, the paper discusses the information security focusing on the social engineering threats, while many efforts have been found in the relevant literature with respect to social engineering in the Internet in general, only few attempts cover the topic in the Social Network environment. In this paper, threats of fake accounts, identity theft, and spear phishing are discussed specifically in the Social Networks. Furthermore, the paper presents the roles of Social Network users and service providers to protect information privacy and prevent threats against information security. This review paper is an attempt to become a guideline to current information privacy and security threats in Social Network environment, and to pave the way for the researchers to investigate more solutions for these threats in future works.

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Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm

Nov 9, 2019

Journal Journal of Ambient Intelligence and Humanized Computing

publisher Springer Nature

DOI https://doi.org/10.1007/s12652-019-01569-8

Volume 11

The rapid development of information technology leads to increasing the number of devices connected to the Internet. Besides, the amount of network attacks also increased. Accordingly, there is an urgent demand to design a defence system proficient in discovering new kinds of attacks. One of the most effective protection systems is intrusion detection system (IDS). The IDS is an intelligent system that monitors and inspects the network packets to identify the abnormal behavior. In addition, the network packets comprise many attributes and there are many attributes that are irrelevant and repetitive which degrade the performance of the IDS system and overwhelm the system resources. A feature selection technique helps to reduce the computation time and complexity by selecting the optimum subset of features. In this paper, an enhanced anomaly-based IDS model based on multi-objective grey wolf optimisation (GWO) algorithm was proposed. The GWO algorithm was employed as a feature selection mechanism to identify the most relevant features from the dataset that contribute to high classification accuracy. Furthermore, support vector machine was used to estimate the capability of selected features in predicting the attacks accurately. Moreover, 20% of NSL–KDD dataset was used to demonstrate effectiveness of the proposed approach through different attack scenarios. The experimental result revealed that the proposed approach obtains classification accuracy of (93.64%, 91.01%, 57.72%, 53.7%) for DoS, Probe, R2L, and U2R attack respectively. Finally, the proposed approach was compared with other existing approaches and achieves significant result.

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Energy efficient multi-hop path in wireless sensor networks using an enhanced genetic algorithm

Oct 1, 2019

Journal Information Sciences

publisher Elsevier

DOI https://doi.org/10.1016/j.ins.2019.05.094

Volume 500

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Application of bat algorithm in carbon nanotubes growing process parameters optimization

May 17, 2019

Journal Intelligent and Interactive Computing: Proceedings of IIC 2018

publisher Springer Singapore

DOI https://doi.org/10.1142/S0129183115501090

Since their discovery, carbon nanotubes (CNTs) have become a subject of intense research for their potential use in various applications. Chemical vapor deposition (CVD) process is the most common method used to grow CNTs. However, the growing process suffers many difficulties in finding the optimal process parameters. Applying computational intelligence methods is a possible solution for optimization problems to reduce using conventional methods and experimental runs. In this work, a combination between bat algorithm (BA) and response surface methodology (RSM) is proposed to solve CNTs process parameters optimization problem. The study aims to maximize the CNTs yield percentage for mass production in two different datasets. BA search process is based on the objective function developed by RSM which represents the prediction mathematical model of growing process parameters. The optimized parameters from datasets are reaction temperature, reaction time, catalyst weight, and methane partial pressure. The algorithm search process was conducted with parameters tuning at different setting values to improve the algorithm’s performance and CNTs yield value. Different evaluation metrics were applied to compare the experimental results. The results have shown that BA has an efficient search performance and obtained better CNTs yield result than RSM in one of the datasets with 21% improvement of CNTs yield value. Besides, BA has shown a fast and stable convergence. Finally, the result was validated and found reliable to be used in real laboratory experiments.

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Intrusion detection system based on a modified binary grey wolf optimisation

Feb 27, 2019

Journal Neural computing and applications

publisher Springer London

DOI https://doi.org/10.1007/s00521-019-04103-1

Volume 32

One critical issue within network security refers to intrusion detection. The nature of intrusion attempts appears to be nonlinear, wherein the network traffic performance is unpredictable, and the problematic space features are numerous. These make intrusion detection systems (IDSs) a challenge within the research arena. Hence, selecting the essential aspects for intrusion detection is crucial in information security and with that, this study identified the related features in building a computationally efficient and effective intrusion system. Accordingly, a modified feature selection (FS) algorithm called modified binary grey wolf optimisation (MBGWO) is proposed in this study. The proposed algorithm is based on binary grey wolf optimisation to boost the performance of IDS. The new FS algorithm selected an optimal number of features. In order to evaluate the proposed algorithm, the benchmark of NSL-KDD network intrusion, which was modified from 99-data set KDD cup to assess issues linked with IDS, had been applied in this study. Additionally, the support vector machine was employed to classify the data set effectively. The proposed FS and classification algorithms enhanced the performance of the IDS in detecting attacks. The simulation outcomes portrayed that the proposed algorithm enhanced the accuracy of intrusion detection up to 99.22% and reduction in the number of features from 41 to 14.

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Neural network training using hybrid particlemove artificial bee colony algorithm for pattern classification

Nov 6, 2017

Journal Journal of Information and Communication Technology (JICT)

publisher 2 (2017): , December 2017

DOI https://doi.org/10.1142/S0129183115501090

Volume 16

The Artificial Neural Networks Training (ANNT) process is an optimization problem of the weight set which has inspired researchers for a long time. By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks. Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima. Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues. Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. However, hybrid algorithms are also a fundamental concern in the optimization field, which aim to cumulate the advantages of different algorithms into one algorithm. In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application. The performance of the HPABC algorithm was investigated on four benchmark pattern-classification datasets and the results were compared with other algorithms. The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT. HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy.

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A hybrid artificial bee colony algorithm for numerical function optimization

Oct 10, 2015

Journal International Journal of Modern Physics C

publisher World Scientific

DOI https://doi.org/10.1142/S0129183115501090

Volume 26

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

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A comparison between artificial bee colony and particle swarm optimization algorithms for protein structure prediction problem

Nov 7, 2013

Journal Neural Information Processing: 20th International Conference

publisher Springer Berlin Heidelberg

DOI https://doi.org/10.1007/978-3-642-42042-9_42

Volume 8227

Protein Structure Prediction (PSP) is a well known problem for Bioinformatics scientists. It was considered as a NP-hard problem. Swarm Intelligence is a branch of evolutionary algorithm, is commonly used for PSP problem. The Artificial Bees Colony (ABC) optimization algorithm is inspired from the honey bees food foraging behavior and the Particle Swarm Optimization (PSO) algorithm which also simulate the process of the birds’ foraging behavior are both used to solve the PSP problem. This paper investigates the performance of the two algorithms when being applied on an experimental short sequence protein called Met-enkaphlin in order to predict its 3D structure. The results illustrates clearly the power of the PSO search strategy and outperforms the ABC in terms of Time, Avg.NFE and success rate values by 70%, 73%, 3.6% respectively. However, the ABC results were more stable than the PSO in

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Artificial bee colony optimization algorithm with crossover operator for protein structure prediction

Aug 28, 2013

publisher Springer Berlin Heidelberg

DOI https://doi.org/10.1007/978-3-642-42042-9_42

Volume 378

Swarm intelligence systems are mainly introduced based on the behavior and the interactions of the insects locally with their communities and also with their environments. Artificial Bees Colony (ABC) Optimization algorithm, inspired from the honey bees’ food foraging behavior, is an optimization method used for bioinformatics problems where the Protein Structure Prediction (PSP) is considered as one of these problems. For a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors, etc does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This paper presents a modified ABC algorithm, where a crossover operator from the Genetic Algorithm (GA) has been added to the original ABC algorithm. To solve the PSP problem, a conformation with the lowest free energy is the target of the ABC search.

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ANGLE BASED PROTEIN TERTIARY STRUCTURE PREDICTION USING BEES OPTIMIZATION ALGORITHM

Jul 24, 2013

publisher Universiti Utara Malaysia

The expand development in the area of scientific researches, especially on biological field, leads to the emergence and discovery of many new chemical compounds. Proteins are one of the hot topics and main concern among the biological subjects for the researchers; due to its complexity, diversity and it participate in each biological structure. In order to perform their function they tend to fold into their tertiary structure. There are two main ways to determine their structure, one is by laboratory experiment which is very expensive and time subsuming, and the second is by computation. In computation way the operation is known as optimization problem and the optimum solution is to find the conformation with the lowest free energy. In this project, angles based control with Bees Optimization search algorithm were adopted to search with guidance the protein conformational space in order to find the optimum solution. The experiment was conducted on short sequence protein (Met-enkephaline) which has been used in previous researches. The prototype system was built using Visual C# 2008 to fulfill the protein 3D structure prediction requirements. WEKA program application was used for main chain angles (Phi and Psi) data classification. The experiment shows a good accuracy in term of prediction and lowest free energy identification.

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The First International Conference on Advances in Cybersecurity (ACeS) 2019

Jul 30, 2019 - Aug 1, 2019

Publisher Springer Singapore

DOI https://doi.org/10.1007/978-981-15-2693-0

Country Malaysia

Location Penang, Malaysia

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