Yazar "Alomari, Osama Ahmad" seçeneğine göre listele
Listeleniyor 1 - 15 / 15
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Amassing the Security: An ECC-Based Authentication Scheme for Internet of Drones(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141, 2021) Hussain, Sajid; Chaudhry, Shehzad Ashraf; Alomari, Osama Ahmad; Alsharif, Mohammed H.; Khan, Muhammad Khurram; Kumar, NeerajThe 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 military/rescue operations in smart cities. However, this growth in infrastructure smartness is also subject to new security threats and the countermeasures require new customized solutions for IoD. Many schemes to secure IoD environments are proposed recently; however, some of those were proved as insecure and some degrades the efficiency. In this article, using elliptic curve cryptography, we proposed a new authentication scheme to secure the communication between a user and a drone flying in some specific flying zone. The security of the proposed scheme is solicited using formal Random oracle method along with a brief discussion on security aspects provided by proposed scheme. Finally, the comparisons with some related and latest schemes is illustrated.Öğe Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2(VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY, 2020) Alsharif, Mohammed H.; Alsharif, Yahia H.; Albreem, Mahmoud A. M.; Jahid, Abu; Solyman, Ahmad Amin Ahmad; Yahya, Khalid O. Moh.; Alomari, Osama Ahmad; Hossain, Md. SanwarResearchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARSCoV-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, more repurposed drugs should be recognized. Furthermore, technological advancements have been helpful in the battle against COVID-19. Machine intelligence technology can support this procedure by rapidly determining adequate and effective drugs against COVID-19 and by overcoming any barrier between a large number of repurposed drugs, laboratory/clinical testing, and final drug authorization. This paper reviews the proposed vaccines and medicines for SARSCoV-2 and the current application of AI in drug repurposing for COVID-19 treatment.Öğe Artificial intelligence in software engineering and inverse: review(TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND, 2020) Shehab, Mohammad; Abualigah, Laith; Jarrah, Muath Ibrahim; Alomari, Osama Ahmad; Daoud, Mohammad Sh.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 review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The aim of this review is highlighted in how the previous study benefited from incorporating the advantages of both fields. The researchers and practitioners on AI and SE belong to a wide range of audiences from the domains of optimization, engineering, data mining, clustering, etc., who will benefit from this study and areas for potential future research.Öğe Binary JAYA Algorithm with Adaptive Mutation for Feature Selection(SPRINGER HEIDELBERG, TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY, 2020) Awadallah, Mohammed A.; Al-Betar, Mohammed Azmi; Hammouri, Abdelaziz I.; Alomari, Osama AhmadIn 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 initially proposed for continuous optimization. Due to the binary nature of the feature selection problem, the JAYA algorithm is adjusted using sinusoidal (i.e., S-shape) transfer function. Furthermore, the mutation operator controlled by adaptive mutation rate (Rm) parameter is also utilized to control the diversity during the search. The proposed binary JAYA algorithm with adaptive mutation is called BJAM algorithm. The performance of BJAM algorithm is tested using 22 real-world benchmark datasets, which vary in terms of the number of features and the number of instances. Four measures are used for performance analysis: classification accuracy, number of features, fitness values, and computational times. Initially, a comparison between binary JAYA (BJA) algorithm and the proposed BJAM algorithm is conducted to show the effect of the mutation operator in the convergence behavior. After that, the results produced by the BJAM algorithm are compared against those yielded by ten state-of-the-art methods. Surprisingly, the proposed BJAM algorithm is able to excel other comparative methods in 7 out of 22 datasets in terms of classification accuracy. This can lead to the conclusion that the proposed BJAM algorithm is an efficient algorithm for the problems belonging to the feature selection domain and is pregnant with fruitful results.Öğe Deep learning applications to combat the dissemination of COVID-19 disease: a review(VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY, 2020) Alsharif, Mohammed H.; Alsharif, Yahia H.; Yahya, Khalid O. Moh.; Alomari, Osama Ahmad; Albreem, Mahmoud A. M.; Jahid, AbuRecent 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 (RTPCR) is known as one of the primary diagnostic tools. However, RT-PCR tests are costly and time-consuming; it also requires specific materials, equipment, and instruments. Moreover, most countries are suffering from a lack of testing kits because of limitations on budget and techniques. Thus, this standard method is not suitable to meet the requirements of fast detection and tracking during the COVID-19 pandemic, which motived to employ deep learning (DL)/ convolutional neural networks (CNNs) technology with X-ray and CT scans for efficient analysis and diagnostic. This study provides insight about the literature that discussed the deep learning technology and its various techniques that are recently developed to combat the dissemination of COVID-19 disease.Öğe Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2021) Alomari, Osama Ahmad; Makhadmeh, Sharif Naser; Al-Betar, Mohammed Azmi; Alyasseri, Zaid Abdi Alkareem; Abu Doush, Iyad; Abasi, Ammar Kamal; Awadallah, Mohammed A.; Abu Zitar, RaedDNA 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 between health and cancerous patient samples. However, gene expression data is characterized as a high dimensional data that typically encompassed of redundant, noisy, and irrelevant genes. Datasets with such characteristics pose a challenge to machine learning algorithms. This is because they impede the training and testing process and entail high resource computations that deteriorate the classification performance. In order to avoid these pitfalls, gene selection is needed. This paper proposes a new hybrid filter-wrapper approach using robust Minimum Redundancy Maximum Relevancy (rMRMR) as a filter approach to choose the topranked genes. Modified Gray Wolf Optimizer (MGWO) is used as a wrapper approach to seek further small sets of genes. In MGWO, new optimization operators inspired by the TRIZ-inventive solution are coupled with the original GWO to increase the diversity of the population. To evaluate the performance of the proposed method, nine well-known microarray datasets are tested. The support vector machine (SVM) is employed for the classification task to estimate the goodness of the selected subset of genes. The effectiveness of TRIZ optimization operators in MGWO is evaluated by investigating the convergence behavior of GWO with and without TRIZ optimization operators. Moreover, the results of MGWO are compared with seven state-of-art gene selection methods using the same datasets based on classification accuracy and the number of selected genes. The results show that the proposed method achieves the best results in four out of nine datasets and it obtains remarkable results on the remaining datasets. The experimental results demonstrated the effectiveness of the proposed method in searching the gene search space and it was able to find the best gene combinations.Öğe Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering(Springer International Publishing Ag, 2021) Abualigah, Laith; Abd Elaziz, Mohamed; Shehab, Mohammad; Alomari, Osama Ahmad; Alshinwan, Mohammad; Alabool, Hamzeh; Al-Arabiat, Deemah A.[Abstract Not Available]Öğe A new maximum power point tracking algorithm based on power differentials method for thermoelectric generators(WILEY-HINDAWI, ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON WIT 5HE, ENGLAND, 2021) Yahya, Khalid O. Moh.; Alomari, Osama AhmadThis 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 differentialsmaximum power point tracking (PD-MPPT) algorithm is proposed to control the boost converter by measuring the output power of TEG devices at both the start and finishing points of the power curve along with making a comparison of these two measured power points. The priority is given to the highest power point until the maximum power point is achieved, and Kalman Filter has been applied to eliminate the oscillation generated from the TEG system. This algorithm does not require any extra circuit to measure the short-circuit current or the open-circuit voltage because there is no disconnection between the TEG and the load. The hardware implementation of the power differentials algorithm is demonstrated under steady-state conditions. Moreover, the PD-MPPT is an effective and applicable algorithm applied to grab the maximum power point from the Photovoltaics PVs and TEGs systems. The practical experiment is conducted using the “STM32f429” microcontroller to implement the algorithm. During the experiments, the change in the duty cycles is observed. The experimental results show that the PD-MPPT algorithm performs better under a steady-state and has the ability to track the maximum power point accurately.Öğe A Novel Pairing-Free Lightweight Authentication Protocol for Mobile Cloud Computing Framework(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141, 2021) Irshad, Azeem; Chaudhry, Shehzad Ashraf; Alomari, Osama Ahmad; Yahya, Khalid O. Moh.; Kumar, NeerajThe 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 gadgets has exacerbated the need for robust and efficient authenticated key agreements. We could witness a number of MCC-based multiserver authentication schemes lately to foster the secure adaptation of the technology; however, the demonstrated solutions are either insecure or employing too costly bilinear pairing operations for implementation. In view of limitations, as illustrated in previous studies, we propose a novel pairing-free multiserver authentication protocol for MCC environment based on an elliptic curve cryptosystem that is not only efficient, but also free from security loopholes as demonstrated. The performance evaluation section discusses and distinguishes the findings among latest studies. The strength of the contributed scheme is proved theoretically under formal security model.Öğe Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques(TECH SCIENCE PRESS, 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052, 2022) Fadhel, Hajer Faris; Mahmood, Musaria Karim; Alomari, Osama Ahmad; Elnagar, AshrafWireless 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 power supply at the sensor node. All routing protocols are large consumers of energy, as they represent the main source of energy cost through data exchange operation. Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs. The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy (LEACH), which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation. This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process, thus increasing the network lifespan. This paper proposes the LEACH-CHIO, a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer (CHIO) algorithm. CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process. LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios, which consist of a variable number of nodes ranging from 20 to 100. To evaluate the algorithm performances, three evaluation indicators have been examined, namely, power consumption, number of live nodes, and number of incoming packets. The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.Öğe Person identification using EEG channel selection with hybrid flower pollination algorithm(ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND, 2020) Alyasseri, Zaid Abdi Alkareem; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Alomari, Osama AhmadRecently, 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 electrical activities which can be measured by placing electrodes (channels) in various positions of the scalp. With a large number of channels, some channels have very important information for biometric system while others not. The channel selection problem has been recently formulated as an optimisation problem and solved by optimisation techniques. This paper proposes hybrid optimisation techniques based on binary flower pollination algorithm (FPA) and beta-hill climbing (called FPA beta-hc) for selecting the most relative EEG channels (i.e., features) that come up with efficient accuracy rate of personal identification. Each EEG signals with three different groups of EEG channels have been utilized (i.e., time domain, frequency domain, and time-frequency domain). The FPA beta-hc is measured using a standard EEG signal dataset, namely, EEG motor movement/imagery dataset with a real world data taken from 109 persons each with 14 different cognitive tasks using 64 channels. To evaluate the performance of the FPA beta-hc, five measurement criteria are considered:accuracy (Acc), (ii) sensitivity (Sen), (iii) F-score (F_s), (v) specificity (Spe), and (iv) number of channels selected (No. Ch). The proposed method is able to identify the personals with high Acc, Sen., F_s, Spe, and less number of channels selected. Interestingly, the experimental results suggest that FPA beta-hc is able to reduce the number of channels with accuracy rate up to 96% using time-frequency domain features. For comparative evaluation, the proposed method is able to achieve results better than those produced by binary-FPA-OPF method using the same EEG motor movement/imagery datasets. In a nutshell, the proposed method can be very beneficial for effective use of EEG signals in biometric applications.Öğe Supervised machine learning for smart data analysis in internet of things environment: An overview(Little Lion Scientific, 2020) Alsharif, Mohammed H.; Mosier, William A.; Alomari, Osama Ahmad; Yahya, KhalidMachine learning techniques will contribution to making Internet of Things (IoT) applications that are considered the most significant sources of new data in the coming future more intelligent, where the systems will be able to access raw data from different resources over the network and analyze this information in order to extract knowledge. This study focuses on supervised machine learning techniques that is considered the main pillar of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study. © 2005 – ongoing JATIT & LLS.Öğe Text documents clustering using modified multi-verse optimizer(Institute of Advanced Engineering and Science, 2020) Abasi, Ammar Kamal; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Naim, Syibrah; Awadallah, Mohammed A.; Alomari, Osama AhmadIn this study, a multi-verse optimizer (MVO) is utilised for the text document clustering (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 cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods. Copyright c 2020 Insitute of Advanced Engineeering and Science. All rights reserved.Öğe A TRIZ-inspired bat algorithm for gene selection in cancer classification(ACADEMIC PRESS INC ELSEVIER SCIENCE, 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495, 2020) Al-Betar, Mohammed Azmi; Alomari, Osama Ahmad; Abu-Romman, Saeid M.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 metaheuristic algorithms for analysing and interpreting gene expression data. However, due to large number of selected genes with limited number of patient's samples and complex interaction between genes, many gene selection methods experienced challenges in order to approach the most relevant and reliable genes. Hence, in this paper, a hybrid filter/wrapper, called rMRMR-MBA is proposed for gene selection problem. In this method, robust Minimum Redundancy Maximum Relevancy (rMRMR) as filter to select the most promising genes and an modified bat algorithm (MBA) as search engine in wrapper approach is proposed to identify a small set of informative genes. The performance of the proposed method has been evaluated using ten gene expression datasets. For performance evaluation, MBA is evaluated by studying the convergence behaviour of MBA with and without TRIZ optimisation operators. For comparative evaluation, the results of the proposed rMRMR-MBA were compared against ten state-of-arts methods using the same datasets. The comparative study demonstrates that the proposed method produced better results in terms of classification accuracy and number of selected genes in two out of ten datasets and competitive results on the remaining datasets. In a nutshell, the proposed method is able to produce very promising results with high classification accuracy which can be considered a promising contribution for gene selection domain.Öğe Wind Driven Optimization With Smart Home Battery for Power Scheduling Problem in Smart Home(IEEE, 2021) Makhadmeh, Sharif Naser; Al-Betar, Mohammed Azmi; Abasi, Ammar Kamal; Awadallah, Mohammed A.; Alyasseri, Zaid Abdi Alkareem; Alomari, Osama Ahmad; Abu Doush, IyadThe power scheduling problem in smart home (PSPSH) refers to schedule smart appliances at suitable times in accordance with pricing system(s). Smart appliances can be rearranged and scheduled by moving their operation times from one period to another. Such a process aims to decrease the electricity bill and the power demand at peak periods and improve user satisfaction. Different optimization approaches were proposed to address PSPSH, where metaheuristics are the most common methods. In this paper, wind-driven optimization (WDO) is adapted to handle PSPSH and optimize its objectives. Smart home battery (SHB) is modelled and used to improve the schedules by storing power at off-peak periods and using the stored power at peak periods. In the simulation results, the proposed approach proves its efficiency in reducing electricity bills and improving user satisfaction. In addition, WDO is compared with bacterial foraging optimization algorithm (BFOA) to evaluate and investigate its performance. WDO outperforms BFOA in optimizing PSPSH objectives.
