Papers by Prof.Omar Almomani
World Electric Vehicle Journal
Recently, technologies for electric mobility have developed rapidly. Since the introduction and s... more Recently, technologies for electric mobility have developed rapidly. Since the introduction and spread of Electric Vehicles (EVs), several studies have attempted to investigate the benefits and risks that impact on the growth of the EV market by evaluating data gathered from various drivers. However, some variables were disregarded such as: Public Involvement, Knowledge of EVs, Perceived Risk, Behavioural Intention, and EV acceptance. These variables are considered vital when analysing the intention to use EVs. Therefore, this study compiles the above mentioned variables to evaluate their effect on the intention to use EVs in Jordan. 501 collected responses were examined using the Smart PLS-Structural Equation Model algorithm. In general, the analysis revealed high levels of EV acceptance. The study proposed twelve direct relationship hypotheses. Out of these hypotheses, ten hypotheses were supported and two were rejected. The final conclusions are that an increase in public involve...
Advances in intelligent systems and computing, 2020
This paper presents the components of a newly developed Malaysian SMEs-Software Process Improveme... more This paper presents the components of a newly developed Malaysian SMEs-Software Process Improvement model (MSME-SPI) that can assess SMEs software development industry in managing and improving their software processes capability. The MSME-SPI is developed in response to practitioner needs that were highlighted in an empirical study with the Malaysian SME software development industry. After the model development, there is a need for independent feedback to show that the model meets its objectives. Consequently, the validation phase is performed by involving a group of software process improvement experts in examining the MSME-SPI model components. Besides, the effectiveness of the MSME-SPI model is validated using an expert panel. Three criteria were used to evaluate the effectiveness of the model namely: usefulness, verifiability, and structure. The results show the model effective to be used by SMEs with minor modifications. The validation phase contributes towards a better understanding and use of the MSME-SPI model by the practitioners in the field.
International Journal of Current Engineering and Technology, Apr 30, 2016
No doubt that text classification is an important research area in information retrieval. In fact... more No doubt that text classification is an important research area in information retrieval. In fact there are many researches about text classification in English language. A few researchers in general talk about text classification using Arabic data set. This research applies three well known classification algorithm. Algorithm applied are K-Nearest neighbour (K-NN), C4.5 and Rocchio algorithm. These well-known algorithms are applied on in-house collected Arabic data set. Data set used consists from 1400 documents belongs to 8 categories. Results show that precision and recall values using Rocchio classifier and K-NN are better than C4.5. This research makes a comparative study between mentioned algorithms. Also this study used a fixed number of documents for all categories of documents in training and testing phase.
Intelligent Automation and Soft Computing, 2023
World Electric Vehicle Journal
Technologies for automated driving have advanced rapidly in recent years. Autonomous Vehicles (AV... more Technologies for automated driving have advanced rapidly in recent years. Autonomous Vehicles (AVs) are one example of these recent technologies that deploy elements such as sensors or processing units to assist the driver. The effective integration of these vehicles into public roads depends on the drivers’ acceptance and how they adjust to this new generation of vehicles. This study investigated the acceptance and willingness of Jordanians to purchase AVs in Jordan. The ordinal logit model was deployed to determine the factors attributed to individual acceptance of AVs, such as the cost, security, privacy, along with the environmental impact, among others. The findings of a national survey conducted on 582 Jordanians to assess their perception about AVs revealed that Jordanians were generally interested in using AVs. However, their decisions about purchasing AVs are influenced by several factors. The results indicated that the cost of AVs greatly influences purchasing decisions, t...
International Journal of Emerging Trends in Engineering Research, Jul 5, 2021
Classification network traffic are becoming ever more relevant in understanding and addressing se... more Classification network traffic are becoming ever more relevant in understanding and addressing security issues in Internet applications. Virtual Private Networks (VPNs) have become one famous communication forms on the Internet. In this study, a new model for traffic classification into VPN or non-VPN is proposed. XGBoost algorithm is used to rank features and to build the classification model. The proposed model overwhelmed other classification algorithms. The proposed model achieved 91.6% accuracy which is the highest registered accuracy for the selected dataset. To illustrate the merit of the proposed model, a comparison was made with sixteen different classification algorithms.
paper evaluates the performance of the OLSR pro-active protocol with and without backup routes un... more paper evaluates the performance of the OLSR pro-active protocol with and without backup routes under varying node densities and with different speed movements in the network. Additionally, this paper assists in ascertaining the effect of varying node densities on the connectivity's life between mobile nodes in the network. Hence, it showed the affect of a local recovery mechanism resulted in achieving a significant improvement in network performance by seeking a long life backup path between source and destination for low/high density nodes. Real time applications are required to be supported by mobile ad hoc networks. This is because of the free movement for the mobile nodes from one area to another without any notification via frequent paths. The real time applications traffics are considered a sensitive application, and it is the most affected by failure through the occurrence of delay and loss of packets. It is, therefore, not suitable for use by players. In mobile ad hoc ne...
Mobile Ad-hoc Network (MANET) characterized with high mobility and very limited resources. Such n... more Mobile Ad-hoc Network (MANET) characterized with high mobility and very limited resources. Such network requires a very high reliable routing protocol to be compatible with its limitations. In position-based routing protocols for MANET, each node chooses the next relay node for packet routing solely from neighbourhood stored in its neighbours' matrix (NLM). The lifetime of neighbors' entry in NLM matrix relates to beacon interval and timeout interval. Inaccurate information of NLM matrix may lead to a wrong selection decision, which can have devastating consequences on MANET resources. Thus, the freshness of the information in a node's NLM matrix is in a high demand. This paper presents an intelligent dynamic fuzzy logic controller refreshment period of entries in neighbourhood matrices (IFPE) scheme. The IFPE algorithm utilizes neighbour's Residual Lifetime of Links (RLT) in the fuzzy logic controller as an input, and the called neighbour expire entry lifetime (ELT) as an output. Simulation results show that IFPE algorithm keeps neighbourhood matrices consistent, which achieve considerable improvement for position-based routing protocols performance. https://sites.google.com/site/ijcsis/
Symmetry, 2020
The network intrusion detection system (NIDS) aims to identify virulent action in a network. It a... more The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecti...
Packet loss can have a destructive effect on the reconstructed video which makes the presentation... more Packet loss can have a destructive effect on the reconstructed video which makes the presentation displeasing to human eyes. with the availability of Forward Error Correction (FEC), the error of packet losses in streaming applications for both video and audio data can be better managed and controlled.. Different FEC packet sizes can cause variation in packet loss and packet loss ratio. Selecting appropriate packet size is important. In this paper we examine how the FEC packet size effects on the packet loss and packet loss ratio. Ns2 simulator is used to evaluate FEC packet size and find optimal FEC packet size.
International Journal of Computer Trends and Technology, 2014
ABSTRACT Text classification is one of the most important tasks in data mining. This paper invest... more ABSTRACT Text classification is one of the most important tasks in data mining. This paper investigates different variations of vector space models (VSMs) using KNN algorithm. The bases of our comparison are the most popular text evaluation measures. The Experimental results against the Saudi data sets reveal that Cosine outperformed Dice and Jaccard coefficients.
Video streaming applications over the Internet is suffering many challenges and packet loss is on... more Video streaming applications over the Internet is suffering many challenges and packet loss is one of the main challenges. This is a result of best-effort services provided by existing IP networks, which does not guarantee packet delivery. Therefore, Forward Error ...
International Journal of Advanced Trends in Computer Science and Engineering
Denial of Services (DoS) Attack is one of the most advanced attacks targeting cybercriminals. The... more Denial of Services (DoS) Attack is one of the most advanced attacks targeting cybercriminals. The DoS attack is designed to reduce the performance of network devices by performing their intended functions. In addition, the confidentiality, reliability and quality of data can be compromised by DoS attacks. In this paper, a new model is introduced that detects network traffic and varies type of application layer DoS attacks. The proposed model usesStackNet architecture which consists of three-layer that works in the feed-forward method. The results showed that the proposed model had a high accuracy level of 99.3% in the measurement of application-layerDoS attacks.
Sustainability
The Internet of Things (IoT) is a technology that allows machines to communicate with each other ... more The Internet of Things (IoT) is a technology that allows machines to communicate with each other without the need for human interaction. Usually, IoT devices are connected via a network. A wide range of network technologies are required to make the IoT concept operate successfully; as a result, protocols at various network layers are used. One of the most extensively used network layer routing protocols is the Routing Protocol for Low Power and Lossy Networks (RPL). One of the primary components of RPL is the trickle timer method. The trickle algorithm directly impacts the time it takes for control messages to arrive. It has a listen-only period, which causes load imbalance and delays for nodes in the trickle algorithm. By making the trickle timer method run dynamically based on hop count, this research proposed a novel way of dealing with the difficulties of the traditional algorithm, which is called the Elastic Hop Count Trickle Timer Algorithm. Simulation experiments have been im...
Journal of Software: Evolution and Process
Computers, Materials & Continua
Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attack... more Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each feature set. At layer 2 of the proposed model, the Optimized Genetic Algorithm (GA) is used to select one feature set based on the priority value. Modifications are done on standard GA to perform optimization and to fit the proposed model. The Optimized GA is used in the training phase to assign a priority value for each feature set. Also, the priority values are categorized into three categories: high, medium, and low. Besides, the Optimized GA is used in the testing phase to select a feature set based on its priority. The feature set with a high priority will be given a high priority to be selected. At the end of phase 2, an update for feature set priority may occur based on the selected features priority and the calculated F-Measures. The proposed model can learn and modify feature sets priority, which will be reflected in selecting features. For evaluation purposes, two well-known datasets are used in these experiments. The first dataset is UNSW-NB15, the other dataset is the NSL-KDD. Several evaluation criteria are used, such as precision, recall, and F-Measure. The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.
2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)
this paper presents a multi objectives genetic algorithm approach for the Ad-hoc On-Demand Distan... more this paper presents a multi objectives genetic algorithm approach for the Ad-hoc On-Demand Distance Vector (AODV) Routing Protocol for Wireless Sensor Network (WSN). AODV used individual routing metric in the form of minimum-hop count and this led to generate two problems: First, utilizing shortest path all time that can be overloaded in the selected path which produce to unbalanced energy depletion and traffic congestion. Secondly, routing during short path and weak link quality is more harmful than over long path strong link quality as it can suffer retransmissions and packet drops. The proposed algorithm will overcome these problems by using Genetic algorithm in which composite multi metric routing criterion are used by integrating three parameters energy factor, traffic load and hop factor the protocol called MGAOVD. Routing based multiple criterions can be combined into a individual criterion to get better performance. The MGAOVD proposed protocol implemented using simulation environments using NS2 simulation. Results from MGAOVD proposeed algorithm show outperform compare to original AODV in terms of increase packet delivery ratio, decreased energy consumption, decreased end-to-end delay, and decreased overhead.
Studies in Big Data
Mobile devices and applications are prone to different kinds of cyber threats and attacks that af... more Mobile devices and applications are prone to different kinds of cyber threats and attacks that affect their users’ privacy. Therefore, there is critical need to understand all cyber threats characteristics in order to prevent their risks. However, most of cyber threats classifications are usually limited and based on one or two criteria in the classification process of threats. In addition, the current frameworks did not present an exhaustive list of cyber threats on mobile devices and applications. According to above reasons, this study proposes an exhaustive framework for mobile devices and applications-cyber security threat classifications, which includes most cyber threats classification and principles. The main purpose of our framework is to systematically identify cyber security threats, show their potential impacts, draw the mobile users’ attention to those threats, and enable them to take protective actions as appropriate.
Security and Communication Networks
Biometric based access control is becoming increasingly popular in the current era because of its... more Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. This eliminates identity recognition manual work and enables automated processing. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a patter...
Computers, Materials & Continua, 2021
Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting ... more Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls. The features selection approach plays an important role in constructing effective network IDS. Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy. Therefore, this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack. The proposed model has two objectives; The first one is to reduce the number of selected features for Network IDS. This objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid model. The algorithms used in this paper are particle swarm optimization (PSO), multiverse optimizer (MVO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), firefly algorithm (FFA), and bat algorithm (BAT). The second objective is to detect the generic attack using machine learning classifiers. This objective was met through employing the support vector machine (SVM), C4.5 (J48) decision tree, and random forest (RF) classifiers. UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model. UNSW-NB15 dataset has nine attacks type. The generic attack is the highest among them. Therefore, the proposed model aims to identify generic attacks. My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model. In terms of features reduction for the classification, my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy, sensitivity and F-measure of all features, whereas MVO-BAT model reduces features to 24 features with the same accuracy, sensitivity and F-measure of all features for all classifiers.
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Papers by Prof.Omar Almomani