• 3D face recognition based on machine learning

      Qatawneh, S.; Ipson, Stanley S.; Qahwaji, Rami S.R.; Ugail, Hassan; 34000 (2008)
      3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity.
    • 3D modeling of magnetic field lines using SOHO/MDI magnetogram images

      Colak, Tufan; Qahwaji, Rami S.R.; Ipson, Stanley S.; Ugail, Hassan (2009-06-11)
      Solar images, along with other observational data, are very important for solar physicists and space weather researchers aiming to understand the way the Sun works and affects Earth. In this study a 3D modelling technique for visualizing solar magnetic field lines using solar images is presented. Photospheric magnetic field footpoints are detected from magnetogram images and using negative and positive magnetic footpoints, dipole pairs are associated according to their proximity. Then, 3D field line models are built using the calculated dipole coordinates, and mapped to detected pairs after coordinate transformations. Final 3D models are compared to extreme ultraviolet images and existing models and the results of visual comparisons are presented.
    • Analysis of cloud-based e-government services acceptance in Jordan: challenges and barriers

      Alkhwaldi, A.F.A.; Kamala, Mumtaz A.; Qahwaji, Rami S.R. (2018-06)
      There is increasing evidence that the Cloud Computing services have become a strategic direction for governments' IT work by the dawn of the third-millennium. The inevitability of this computing technology has been recognized not only in the developed countries like the UK, USA and Japan, but also in the developing countries like the Middle East region and Malaysia, who have launched migrations towards Cloud platforms for more flexible, open, and collaborative public services. In Jordan, the cloud-based e-government project has been deemed as one of the high priority areas for the government agencies. In spite of its phenomenal evolution, various governmental cloud-based services still facing adoption challenges of e-government projects like technological, human-aspects, social, and financial which need to be treated and considered carefully by any government agency contemplating its implementation. While there have been extensive efforts to investigate the e-government adoption from the citizens' perspective using different theories and models, none have paid adequate attention to the security issues. This paper explores the different perspectives of the extent in which these challenges inhibit the acceptance and use of cloud computing in Jordanian public sector. In addition to examining the effect of these challenges on the participants’ security perception. The empirical evidence provided a total of 220 valid responses to our online questionnaire from Jordanian citizens including IT- staff from different government sectors. Based on the data analysis some significant challenges were identified. The results can help the policy makers in the public sector to guide successful acceptance and adoption of cloud-based e-government services in Jordan.
    • Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images

      Colak, Tufan; Qahwaji, Rami S.R. (Springer, 16/12/2007)
      This paper presents a hybrid system for automatic detection and McIntosh-based classification of sunspot groups on SOHO/MDI white-light images using active-region data extracted from SOHO/MDI magnetogram images. After sunspots are detected from MDI white-light images they are grouped/clustered using MDI magnetogram images. By integrating image-processing and neural network techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. Our results show that the automated grouping and classification of sunspots is possible with a high success rate when compared to the existing manually created catalogues. In addition, our system can detect and classify sunspot groups in their early stages, which are usually missed by human observers.
    • Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

      Qahwaji, Rami S.R.; Colak, Tufan; Al-Omari, M.; Ipson, Stanley S. (2008-02-06)
      In this work, machine learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CMEs catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flares and CMEs data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Different properties are extracted from all the associated (A) and not-associated (NA) flares representing the intensity, flare duration, duration of decline and duration of growth. Cascade Correlation Neural Networks (CCNN) are used in our work. The flare properties are converted to numerical formats that are suitable for CCNN. The CCNN will predict if a certain flare is likely to initiate a CME after input of its properties. Intensive experiments using the Jack-knife techniques are carried out and it is concluded that our system provides an accurate prediction rate of 65.3%. The prediction performance is analysed and recommendation for enhancing the performance are provided.
    • The Automated Prediction of Solar Flares from SDO Images Using Deep Learning

      Abed, Ali K.; Qahwaji, Rami S.R.; Abed, A. (2021-04-15)
      In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).
    • Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares

      Colak, Tufan; Qahwaji, Rami S.R. (AGU, 04/06/2009)
      The importance of real-time processing of solar data especially for space weather applications is increasing continuously. In this paper, we present an automated hybrid computer platform for the short-term prediction of significant solar flares using SOHO/Michelson Doppler Imager images. This platform is called the Automated Solar Activity Prediction tool (ASAP). This system integrates image processing and machine learning to deliver these predictions. A machine learning-based system is designed to analyze years of sunspot and flare data to create associations that can be represented using computer-based learning rules. An imaging-based real-time system that provides automated detection, grouping, and then classification of recent sunspots based on the McIntosh classification is also created and integrated within this system. The properties of the sunspot regions are extracted automatically by the imaging system and processed using the machine learning rules to generate the real-time predictions. Several performance measurement criteria are used and the results are provided in this paper. Also, quadratic score is used to compare the prediction results of ASAP with NOAA Space Weather Prediction Center (SWPC) between 1999 and 2002, and it is shown that ASAP generates more accurate predictions compared to SWPC.
    • An automatic corneal subbasal nerve registration system using FFT and phase correlation techniques for an accurate DPN diagnosis

      Al-Fahdawi, Shumoos; Qahwaji, Rami S.R.; Al-Waisy, Alaa S.; Ipson, Stanley S. (2015)
      Confocal microscopy is employed as a fast and non-invasive way to capture a sequence of images from different layers and membranes of the cornea. The captured images are used to extract useful and helpful clinical information for early diagnosis of corneal diseases such as, Diabetic Peripheral Neuropathy (DPN). In this paper, an automatic corneal subbasal nerve registration system is proposed. The main aim of the proposed system is to produce a new informative corneal image that contains structural and functional information. In addition a colour coded corneal image map is produced by overlaying a sequence of Cornea Confocal Microscopy (CCM) images that differ in their displacement, illumination, scaling, and rotation to each other. An automatic image registration method is proposed based on combining the advantages of Fast Fourier Transform (FFT) and phase correlation techniques. The proposed registration algorithm searches for the best common features between a number of sequenced CCM images in the frequency domain to produce the formative image map. In this generated image map, each colour represents the severity level of a specific clinical feature that can be used to give ophthalmologists a clear and precise representation of the extracted clinical features from each nerve in the image map. Moreover, successful implementation of the proposed system and the availability of the required datasets opens the door for other interesting ideas; for instance, it can be used to give ophthalmologists a summarized and objective description about a diabetic patient’s health status using a sequence of CCM images that have been captured from different imaging devices and/or at different times
    • Automatic Detection and Verification of Solar Features

      Qahwaji, Rami S.R.; Colak, Tufan (2006)
      A fast hybrid system for the automated detection and verification of active regions (plages) and filaments in solar images is presented in this paper. The system combines automated image processing with machine learning. The imaging part consists of five major stages. The solar disk is detected in the first stage, using a morphological hit-miss transform, watershed transform and Filling algorithm. An image-enhancement technique is introduced to remove the limb-darkening effect and intensity filtering is implemented followed by a modified region-growing technique to detect the regions of interest (RoI). The algorithms are tested on H- and CA II K3-line solar images that are obtained from Meudon Observatory, covering the period from July 2, 2001 till August 4, 2001. The detection algorithm is fast and it achieves false acceptance rate (FAR) error rate of 67% and false rejection rate (FRR) error rate of 3% for active regions, and FAR error rate of 19% and FRR error rate of 14% for filaments, when compared with the manually detected filaments in the synoptic maps. The detection performance is enhanced further using a neural network (NN), which is trained on statistical features extracted from the RoI and non-RoI. With the use of this combination the FAR has dropped to 2% for active regions and 4% for filaments.© 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 199-210, 2005
    • Automatic Short-Term Solar Flare Prediction Using Machine Learning and Sunspot Associations.

      Qahwaji, Rami S.R.; Colak, Tufan (Springer, 2007)
      In this paper, a machine-learning-based system that could provide automated short-term solar flare prediction is presented. This system accepts two sets of inputs: McIntosh classification of sunspot groups and solar cycle data. In order to establish a correlation between solar flares and sunspot groups, the system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots with their corresponding flares based on their timing and NOAA numbers. The McIntosh classification for every relevant sunspot is extracted and converted to a numerical format that is suitable for machine learning algorithms. Using this system we aim to predict whether a certain sunspot class at a certain time is likely to produce a significant flare within six hours time and if so whether this flare is going to be an X or M flare. Machine learning algorithms such as Cascade-Correlation Neural Networks (CCNNs), Support Vector Machines (SVMs) and Radial Basis Function Networks (RBFN) are optimised and then compared to determine the learning algorithm that would provide the best prediction performance. It is concluded that SVMs provide the best performance for predicting whether a McIntosh classified sunspot group is going to flare or not but CCNNs are more capable of predicting the class of the flare to erupt. A hybrid system that combines a SVM and a CCNN is suggested for future use.
    • Automatic sunspots detection on SODISM solar images

      Alasta, Amro F.; Algamudi, Abdulrazag; Qahwaji, Rami S.R.; Ipson, Stanley S.; Nagem, Tarek A. (2017)
      The surface of the sun often shows visible sunspots which are located in magnetically active regions of the Sun, and whose number is an indicator of the Sun’s magnetic activity. The detection and classification of sunspots are useful techniques in the monitoring and prediction of solar activity. The automated detection of sunspots from digital images is complicated by their irregularities in shape and variable contrast and intensity compared with their surrounding area. The main aim of this paper is to detect sunspots using images from the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite and calculate their filling factors. A comparison over time with sunspot numbers obtained using images from the SOHO satellite is also presented.
    • A Comparison between Vector Algorithm and CRSS Algorithms for Indoor Localization using Received Signal Strength

      Obeidat, Huthaifa A.N.; Dama, Yousif A.S.; Abd-Alhameed, Raed A.; Hu, Yim Fun; Qahwaji, Rami S.R.; Noras, James M.; Jones, Steven M.R. (2016)
      A comparison is presented between two indoor localization algorithms using received signal strength, namely the vector algorithm and the Comparative Received Signal Strength (CRSS) algorithm. Signal values were obtained using ray tracing software and processed with MATLAB to ascertain the effects on localization accuracy of radio map resolution, number of access points and operating frequency. The vector algorithm outperforms the CRSS algorithm, which suffers from ambiguity, although that can be reduced by using more access points and a higher operating frequency. Ambiguity is worsened by the addition of more reference points. The vector algorithm performance is enhanced by adding more access points and reference points while it degrades with increasing frequency provided that the statistical mean of error increased to about 60 cm for most studied cases.
    • A comparison of flare forecasting methods, I: results from the “All-clear” workshop

      Barnes, G.; Leka, K.D.; Schrijver, C.J.; Colak, Tufan; Qahwaji, Rami S.R.; Ashamari, Omar; Yuan, Y.; Zhang, J.; McAteer, R.T.J.; Bloomfield, D.S.; et al. (2016-10)
      Solar flares produce radiation which can have an almost immediate effect on the near-Earth environ- ment, making it crucial to forecast flares in order to mitigate their negative effects. The number of published approaches to flare forecasting using photospheric magnetic field observations has prolifer- ated, with varying claims about how well each works. Because of the different analysis techniques and data sets used, it is essentially impossible to compare the results from the literature. This problem is exacerbated by the low event rates of large solar flares. The challenges of forecasting rare events have long been recognized in the meteorology community, but have yet to be fully acknowledged by the space weather community. During the interagency workshop on “all clear” forecasts held in Boulder, CO in 2009, the performance of a number of existing algorithms was compared on common data sets, specifically line-of-sight magnetic field and continuum intensity images from MDI, with consistent definitions of what constitutes an event. We demonstrate the importance of making such systematic comparisons, and of using standard verification statistics to determine what constitutes a good prediction scheme. When a comparison was made in this fashion, no one method clearly outperformed all others, which may in part be due to the strong correlations among the parameters used by different methods to characterize an active region. For M-class flares and above, the set of methods tends towards a weakly positive skill score (as measured with several distinct metrics), with no participating method proving substantially better than climatological forecasts.
    • A comparison of flare forecasting methods. II. Benchmarks, metrics and performance results for operational solar flare forecasting systems

      Leka, K.D.; Park, S-H.; Kusano, K.; Andries, J.; Barnes, G.; Bingham, S.; Bloomfield, D.S.; McCloskey, A.E.; Delouille, V.; Falconer, D.; et al. (2019-08)
      Solar flares are extremely energetic phenomena in our Solar System. Their impulsive, often drastic radiative increases, in particular at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand solar flares to the point of being able to forecast them. As data and algorithms improve dramatically, questions must be asked concerning how well the forecasting performs; crucially, we must ask how to rigorously measure performance in order to critically gauge any improvements. Building upon earlier-developed methodology (Barnes et al. 2016, Paper I), international representatives of regional warning centers and research facilities assembled in 2017 at the Institute for Space-Earth Environmental Research, Nagoya University, Japan to – for the first time – directly compare the performance of operational solar flare forecasting methods. Multiple quantitative evaluation metrics are employed, with focus and discussion on evaluation methodologies given the restrictions of operational forecasting. Numerous methods performed consistently above the “no skill” level, although which method scored top marks is decisively a function of flare event definition and the metric used; there was no single winner. Following in this paper series we ask why the performances differ by examining implementation details (Leka et al. 2019, Paper III), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in (Park et al. 2019, Paper IV). With these works, this team presents a well-defined and robust methodology for evaluating solar flare forecasting methods in both research and operational frameworks, and today’s performance benchmarks against which improvements and new methods may be compared.
    • A comparison of flare forecasting methods. III. Systematic behaviors of operational solar flare forecasting systems

      Leka, K.D.; Park, S-H.; Kusano, K.; Andries, J.; Barnes, G.; Bingham, S.; Bloomfield, D.S.; McCloskey, A.E.; Delouille, V.; Falcomer, D.; et al. (2019-08)
      A workshop was recently held at Nagoya University (31 October – 02 November 2017), sponsored by the Center for International Collaborative Research, at the Institute for Space-Earth Environmental Research, Nagoya University, Japan, to quantitatively compare the performance of today’s operational solar flare forecasting facilities. Building upon Paper I of this series (Barnes et al. 2016), in Paper II (Leka et al. 2019) we described the participating methods for this latest comparison effort, the evaluation methodology, and presented quantitative comparisons. In this paper we focus on the behavior and performance of the methods when evaluated in the context of broad implementation differences. Acknowledging the short testing interval available and the small number of methods available, we do find that forecast performance: 1) appears to improve by including persistence or prior flare activity, region evolution, and a human “forecaster in the loop”; 2) is hurt by restricting data to disk-center observations; 3) may benefit from long-term statistics, but mostly when then combined with modern data sources and statistical approaches. These trends are arguably weak and must be viewed with numerous caveats, as discussed both here and in Paper II. Following this present work, we present in Paper IV a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms; Park et al. 2019). Hence, most importantly, with this series of papers we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies.
    • A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-day Forecasting Patterns

      Park, S.H.; Leka, K.D.; Kusano, K.; Andries, J.; Barnes, G.; Bingham, S.; Bloomfield, D.S.; McCloskey, A.E.; Delouille, V.; Falconer, D.; et al. (2020-02-19)
      A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active." Building on earlier studies in this series in which we describe the methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multiday periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods and, conversely, correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event, and event/no-event) and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day 1 remains high/low on day 2, even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line.
    • Corneal confocal microscopy detects a reduction in corneal endothelial cells and nerve fibres in patients with acute ischemic stroke

      Khan, A.; Kamran, S.; Akhtar, N.; Ponirakis, G.; Al-Muhannadi, H.; Petropoulos, I.N.; Al-Fahdawi, Shumoos; Qahwaji, Rami S.R.; Sartaj, F.; Babu, B.; et al. (2018-11)
      Endothelial dysfunction and damage underlie cerebrovascular disease and ischemic stroke. We undertook corneal confocal microscopy (CCM) to quantify corneal endothelial cell and nerve morphology in 146 patients with an acute ischemic stroke and 18 age-matched healthy control participants. Corneal endothelial cell density was lower (P<0.001) and endothelial cell area (P<0.001) and perimeter (P<0.001) were higher, whilst corneal nerve fbre density (P<0.001), corneal nerve branch density (P<0.001) and corneal nerve fbre length (P=0.001) were lower in patients with acute ischemic stroke compared to controls. Corneal endothelial cell density, cell area and cell perimeter correlated with corneal nerve fber density (P=0.033, P=0.014, P=0.011) and length (P=0.017, P=0.013, P=0.008), respectively. Multiple linear regression analysis showed a signifcant independent association between corneal endothelial cell density, area and perimeter with acute ischemic stroke and triglycerides. CCM is a rapid non-invasive ophthalmic imaging technique, which could be used to identify patients at risk of acute ischemic stroke.
    • Deep learning technology for predicting solar flares from (Geostationary Operational Environmental Satellite) data

      Nagem, Tarek A.M.; Qahwaji, Rami S.R.; Ipson, Stanley S.; Wang, Z.; Al-Waisy, Alaa S. (2018)
      Solar activity, particularly solar flares can have significant detrimental effects on both space-borne and grounds based systems and industries leading to subsequent impacts on our lives. As a consequence, there is much current interest in creating systems which can make accurate solar flare predictions. This paper aims to develop a novel framework to predict solar flares by making use of the Geostationary Operational Environmental Satellite (GOES) X-ray flux 1-minute time series data. This data is fed to three integrated neural networks to deliver these predictions. The first neural network (NN) is used to convert GOES X-ray flux 1-minute data to Markov Transition Field (MTF) images. The second neural network uses an unsupervised feature learning algorithm to learn the MTF image features. The third neural network uses both the learned features and the MTF images, which are then processed using a Deep Convolutional Neural Network to generate the flares predictions. To the best of our knowledge, this work is the first flare prediction system that is based entirely on the analysis of pre-flare GOES X-ray flux data. The results are evaluated using several performance measurement criteria that are presented in this paper.
    • Detecting edges in noisy face database images

      Qahwaji, Rami S.R. (2003)
      No Abstract
    • Detection of Dust Storms Using MODIS Reflective and Emissive Bands

      El-Ossta, Esam E.A.; Qahwaji, Rami S.R.; Ipson, Stanley S. (2013-05-02)
      Dust storms are one of the natural phenomena, which have increased in frequency in recent years in North Africa, Australia and northern China. Satellite remote sensing is the common method for monitoring dust storms but its use for identifying dust storms over sandy ground is still limited as the two share similar characteristics. In this study, an artificial neural network (ANN) is used to detect dust storm using 46 sets of data acquired between 2001 and 2010 over North Africa by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites. The ANN uses image data generated from Brightness Temperature Difference (BTD) between bands 23 and 31 and BTD between bands 31 and 32 with three bands 1, 3, and 4, to classify individual pixels on the basis of their multiple-band values. In comparison with the manually detection of dust storms, the ANN approach gave better result than the Thermal Infrared Integrated Dust Index approach for dust storms detection over the Sahara. The trained ANN using data from the Sahara desert gave an accuracy of 0.88 when tested on data from the Gobi desert and managed to detect 90 out of the 96 dust storm events captured worldwide by Terra and Aqua satellites in 2011 that were classified as dusty images on NASA Earth Observatory.