Osama Alomari

Osama Alomari
Istanbul Gelisim Üniversitesi · Computer engineering

Phd in computer science (Artificial Intelligence)

About

52
Publications
88,563
Reads
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1,142
Citations
Citations since 2017
51 Research Items
1123 Citations
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20172018201920202021202220230100200300400
Additional affiliations
January 2012 - April 2012
Universiti Kebangsaan Malaysia
Position
  • Research Assistant
Education
January 2010 - January 2012
Universiti Kebangsaan Malaysia
Field of study
  • Artificial intelligence

Publications

Publications (52)
Article
The butterfly optimization algorithm (BOA) is a recent successful metaheuristic swarm-based optimization algorithm. The BOA has attracted scholars’ attention due to its extraordinary features. Such as the few adaptive parameters to handle and the high balance between exploration and exploitation. Accordingly, the BOA has been extensively adapted fo...
Article
Full-text available
The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is...
Article
Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive r...
Article
Full-text available
In this paper, the learning process of multilayer perceptron (MLP) neural network is boosted using hybrid metaheuristic optimization algorithms. Normally, the learning process in MLP requires suitable settings of its weight and bias parameters. In the original version of MLP, the gradient descent algorithm is used as a learner in MLP which suffers...
Article
Full-text available
In this work, a comprehensive review of the multi-objective grey wolf optimizer (MOGWO) is provided. In multi-objective optimization (MO), more than one objective function must be considered at the same time. To deal with such problems, a priori or a posteriori MOGWO variants have been proposed in the literature. In the a priori model, the multi-ob...
Article
Full-text available
Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, si...
Article
Full-text available
In this paper, a modified version of the Multi-objective Grey Wolf Optimizer (MGWO), known as linked-based GWO (LMGWO), is proposed for the Appliances Energy Scheduling Problem (AESP). The proposed LMGWO is utilized by combining the MGWO searching mechanism with a novel strategy, called neighbourhood selection strategy, to improve local exploitatio...
Article
Full-text available
The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on the scalp at different places. However, selecting...
Article
Full-text available
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techni...
Article
Full-text available
Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these sign...
Article
Full-text available
The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimizati...
Article
In evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized for evolutionary algorithms to improve their diversit...
Article
Full-text available
Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, meas...
Article
Full-text available
Electroencephalogram signals (EEG) have provided biometric identification systems with great capabilities. Several studies have shown that EEG introduces unique and universal features besides specific strength against spoofing attacks. Essentially, EEG is a graphic recording of the brain’s electrical activity calculated by sensors (electrodes) on t...
Article
Wireless sensor networks (WSNs) are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data, and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol. Energy dissipation is one of the most challenging issues due to the limited...
Article
Full-text available
The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature sele...
Article
Full-text available
The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further an...
Article
The continuous innovation and progression in hardware, software and communication technologies helped the expansion and accelerated growth in Internet of Things based drone networks (IoD), for the devices, applications and people to communicate and share data. IoD can enhance comfort in many applications including, daily life, commercial, and milit...
Conference Paper
Full-text available
Fault diagnosis of anomalies in induction motors is essential to ensure industry safety. This paper presents a new hybrid Invasive Weed Optimization and Machine Learning approach for fault diagnosis in an induction motor. The vibration signal provides a lot of information about the motor's operating conditions. Therefore, the vibration signal of th...
Chapter
Full-text available
Harris Hawks optimization (HHO) is a recent population-based optimization algorithm that has been recently proposed to address several different problems. Sometimes, poor exploitation (intensification) ability influences the performance of Harris Hawks optimization. This chapter proposes a new hybridization strategy, namely, hybrid Harris Hawks opt...
Chapter
Full-text available
Feature selection (FS) is one of the data mining methods that is used to select the most relevant subsets of features from a stored data while eliminating the irrelevant ones and yet minimising the error rate of the selected subsets of features. Based on that, FS is considered as a two objectives minimisation problem. FS can be filter-based or wrap...
Article
In this paper, the β-hill climbing optimizer is hybridized with the flower pollination algorithm (FPA) as a local refinement operator for global optimization problems. The proposed method is called HyFPAβ-hc. Such hybridization aims to enhance the balance between exploration and exploitation processes during the search, thus improving the quality o...
Article
DNA microarray technology is the fabrication of a single chip to contain a thousand genetic codes. Each microarray experiment can analyze many thousands of genes in parallel. The outcomes of the DNA microarray is a table/matrix, called gene expression data. Pattern recognition algorithms are widely applied to gene expression data to differentiate b...
Article
Nowadays, predicting solar radiation is widely increased to maximize the efficiency of solar systems globally. Meteorological data from metrological stations is used to implement the intelligent prediction systems. Unfortunately, uncertainty in the used solar variables and the selected prediction models would increase the difficulties in using inte...
Article
This study uses a new maximum power point tracking (MPPT) algorithm for Thermoelectric Generator (TEG) devices. The MPPT algorithm appears as an essential solution due to the nature and the variation characteristics of the TEG devices under certain conditions. In this paper, the power differentials‐maximum power point tracking (PD‐MPPT) algorithm i...
Article
Full-text available
In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same clust...
Article
Full-text available
Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARS-CoV-19 through existing data that reveal the SARS's cause. Artificial intelligence (AI) learning models can be created to predict drug structures that can be used to treat COVID-19. Despite the effectively demonstrated repurposed drugs,...
Article
Full-text available
Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARS-CoV-19 through existing data that reveal the SARS’s cause. Artificial intelligence (AI) learning models can be created to predict drug structures that can be used to treat COVID-19. Despite the effectively demonstrated repurposed drugs,...
Article
Full-text available
Recent Coronavirus (COVID-19) is one of the respiratory diseases, and it is known as fast infectious ability. This dissemination can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. Reverse transcription-polymerase chain reaction (RT-PCR) is known as one of the primary diagnostic t...
Article
In this paper, a new metaheuristic algorithm called JAYA algorithm has been adapted for feature selection. Feature selection is a typical problem in machine learning and data mining domain concerned with determining the subset of high discriminative features from the irrelevant, noisy, redundant, and high-dimensional features. JAYA algorithm is ini...
Article
Full-text available
Artificial Intelligence (AI) and Software Engineering are considered as significant fields to solve various problems. However, there are weaknesses in certain problem-solving in each field. Thus, this paper is a broad-based review of using artificial intelligence (AI) to improve software engineering (SE), and vice versa. As well as it intends to re...
Article
The mobile cloud computing (MCC) refers to an infrastructure that integrates cloud computing and mobile computing, and it has changed a great deal, the service provisioning of applications, which requires to get the data processed after collection from vast sensor and Internet-of-Things-based network. The ever increasing number of handheld mobile g...
Article
Recently, electroencephalogram (EEG) signal presents a great potential for a new biometric system to deal with a cognitive task. Several studies defined the EEG with uniqueness features, universality, and natural robustness that can be used as a new track to prevent spoofing attacks. The EEG signals are the graphical recording of the brain electric...
Article
Gene expression data are expected to make a great contribution in the producing of efficient cancer diagnosis and prognosis. Gene expression data are coded by large measured genes, and only of a few number of them carry precious information for different classes of samples. Recently, several researchers proposed gene selection methods based on meta...
Article
Full-text available
Cuckoo search algorithm (CSA) is considered one of the promising metaheuristic algorithms applied to solve numerous problems in different fields. Although it employed the Levy flight to guide the search process. But, it has drawbacks, such as utilization of global search; in certain cases, this technique may surround local optima. Also, the results...
Article
Full-text available
In the modern life, the authentication technique for any system is considered as one of the most important and challenging tasks. Therefore, many researchers have developed traditional authentication systems to deal with our digital society. Recently, several studies showed that the brain electrical activity or electroencephalogram (EEG) signals co...
Article
Full-text available
In the modern life, the authentication technique for any system is considered as one of the most important challenges task which must careful consideration. Therefore, many researchers have developed traditional authentication systems to deal with our digital world. Recently, The Biometric techniques have been successfully provided a high level of...
Article
Full-text available
This paper proposed a new gene selection method based on modified Minimum Redundancy Maximum Relevancy (MRMR) as a filtering approach and hybrid bat algorithm with β-hill climbing as an efficient wrapper approach. The gene selection is a process of selecting the discriminative genes that aid in the development of efficient cancer diagnosis and clas...
Conference Paper
Full-text available
Since past three decades, the world is transformed into digital society, where every individual is living with a unique digital identifier. The main purpose of this identifier is to distinguish from others as well as to deal with digital machines which are surrounding the world. Recently, many researchers proved that the brain electrical activity o...
Conference Paper
Full-text available
Since the past decades, the world has been transformed into a digital society, where every individual is living with a unique identifier. The primary purpose of this id is to distinguish from others and to deal with digital machines which are surrounding the world. Recently, many researchers showed that the brain electrical activity or electroencep...
Article
Full-text available
The microarray technology facilitates biologist in monitoring the activity of thousands of genes (features) in one experiment. This technology generates gene expression data, which are significantly applicable for cancer classification. However, gene expression data consider as high- dimensional data which consists of irrelevant, redundant, and noi...
Article
Full-text available
Intrusion Detection (ID) is the most significant component in Network Security System as it is responsible to detect several types of attacks. Having a high quality intrusion detection system (IDS) is in high demand. The IDS commonly deals with a large amount of data traffic, which involves irrelevant and redundant features. The feature selection i...

Questions

Questions (12)
Question
I have applied genetic algorithm with SVM as feature selection method for high dimensional datasets (i.e gene expression data). The algorithm was evaluated based on classification  accuracy using 10 folds cross validation.
There exist state-of-the-art methods which are applied on the same datasets. For Fair comparison, can i  reported there results and compare with them directly on my research. Note that all of them used same validation (i.e 10 folds cross validation), its mean  that It means the input data has different set on each experiment. Is this mean that comparison  will not be fair ???
Should I have re-implemented  the methods in the literature to do a fair comparison.
Thanks in advance
Question
I used bat algorithm for feature selection and SVM classifer with 10 fold-cross validation  for evaluation on  multi-class datasets, but i see Accuracy measurment  not sufficient  for comparison. Thus, i am thinking to add  sensitivity, specificity , and AUC  measurments, but i didnt see any  research work/paper on multi-class datasets   reported them  into experiment results . However, for binary class dataset the researcher reported theses measurments (i.e  sensitivity, specificity , and AUC)..
please if its possivple to add such measument for multiclass datasets..
how we can estmiate AUC??
 can i estimate its from confusion matrix ??
What facors should involved in AUC  claculations ??
Thanks in advance.
Question
We can plot ROC curve for binary classification, Is it possible to plot a ROC curve for a multiclass classification? if yes, how?
Thanks in advance
Question
I have run gain ratio attributeSelection on weka and also run gain ratio attributeSelection from Java machine learning library
The results in term of attribute score and order is quiet different , is this related to diversity discretization method used in both ?
 For example,
Gain ratio with discretization method A:
0.509 1671
0.432 765
0.425 249
0.396 625
0.393 493
0.369 1423
0.345 1772
0.344 267
0.344 245
0.335 1042
0.33 513
Gain ratio with discretization method B :
765 0.291676685
493 0.280632207
1411 0.272024568
1423 0.264074648
249 0.24425128
897 0.238272511
1967 0.205044156
1772 0.203703632
1843 0.201867619
245 0.201800878
Question
I am working on microarray datasets. This type of dataset is continuous especially Colon. If i want to apply feature selection such as chisquare , IG , etc..when i upload colon dataset to perform feature selection, both chi square and other algorithms seems inactive,Therefore, is it necessary to normalize all dataset between [0,1] before feature selection. From ChiSquaredAttributeEval code it seems that its dicretize/ binarize the dataset prior FS.
What the data format appropriate to these feature selection algorithms ?
Thanks
Question
I am working on form ensemble univariate feature selection method from different metric, such that if i have these filter methods( IG, Ginin index, Gain ratio, and Symmetrical Uncertainty). I will choose just one of them due to all of them are based on entropy.
1-Please provide me of group of univariate feature selector based on different metric such as  (probability distributions, information theory, distance, consistency  etc.).
2- what class of pca relate to?
Thanks in advance
Question
Classifier generate model based on candidate feature subset and then the model evaluate on testing dataset.
 When apply the prediction model for testing purpose, the test data can be (again using only selected features)  OR must be on the whole features ?
For Example:
Model generate on feature subset (X1,X3,X7,X8), thereby , testing data will input in the model for just (X1,X3,X7,X8) OR all dataset features (i.e . X1,X2,X3,X4,X5,X6,X7,X8,X9,X10).
Question
Am targeted to improve maximum relevance minimum redundancy ( MRMR ) for gene selection problem , but MRMR coded in matlab language which am not familiar with it. please if you have MRMR in java code pass to me. 
Your help is highly appreciated 

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Projects

Projects (6)
Project
CHIO is a new nature-inspired human-based optimization algorithm. It is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. This project will include the Code of CHIO The final copy of the paper The CHIO-related papers. The authors are fully agreed to use the CODE of CHIO and if you need any help in adapting CHIO for your problems, all authors are ready to help. Author list: Mohammed Azmi Al-Betar (mohbetarATbau.edu.jo, m.albetarATajman.ac.ae) Zaid Abdi Alkareem Alyasseri (zaid.alyasseriATuokufa.edu.iq) Mohammed A. Awadallah (ma.awadallahATalaqsa.edu.ps) Iyad Abu Doush (idoushATauk.edu.kw)
Project
Given that EEG channel selection can be considered a complex optimization problem, this project proposes an optimum EEG channel selection method by using a metaheuristic approach. Where the proposed approach can determine the optimal subset of channels. The radial basis function-kernel support vector machine (RBF-SVM) classifier for personal identification is used to measure the accuracy of the channels selected.