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  • 1.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Al-Madina Higher Institute for Engineering and Technology.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Khalaf, Ashraf A. M.
    Minia University, Egypt.
    Hamed, Hesham F. A.
    Minia University, Egypt.
    Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine2016Inngår i: Building Sustainable Health Ecosystems: 6th International Conference on Well-Being in the Information Society, WIS 2016, Tampere, Finland, September 16-18, 2016, Proceedings / [ed] Hongxiu Li, Pirkko Nykänen, Reima Suomi, Nilmini Wickramasinghe, Gunilla Widén, Ming Zhan, Springer International Publishing , 2016, s. 151-160Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.

  • 2.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt .
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. Faculty of Engineering, Al-Azhar University, Qena, Egypt.
    Khalaf, Ashraf A. M.
    Minia University, Minia, Egypt .
    Hamed, Hesham F. A.
    Egyptian Russian University, Cairo, Egypt. Minia University, Minia, Egypt .
    Deep Convolutional Neural Networks: Foundations and Applications in Medical Imaging2020Inngår i: Deep Learning in Computer Vision: Principles and Applications / [ed] Mahmoud Hassaballah, Ali Ismail Awad, CRC Press, 2020, 1st, s. 233-260Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 3.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Electronic and Communication Department Al-Madina Higher Institute for Engineering and Technology, Giza.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Khalaf, Ashraf A. M.
    Faculty of Engineering, Minia University.
    Hamed, Hesham F. A.
    Faculty of Engineering, Minia University.
    Design and implementation of a computer-aided diagnosis system for brain tumor classification2017Inngår i: 2016 28th International Conference on Microelectronics (ICM), 2017, s. 73-76, artikkel-id 7847911Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.

  • 4.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Faculty of Engineering, Al-Azhar University, Qena, Egypt.
    Khalaf, Ashraf A.M.
    Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
    Hamed, Hesham F.A.
    Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
    A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned2019Inngår i: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 61, s. 300-318Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.

  • 5.
    Ali, Bako
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Faculty of Engineering, Al Azhar University.
    Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes2018Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, nr 3, artikkel-id 817Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or “things” to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.

  • 6.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Workshop on Emerging Aspects in Information Security2015Annet (Annet (populærvitenskap, debatt, mm))
  • 7.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Communications in Computer and Information Science, Vol. 3812013Annet (Annet (populærvitenskap, debatt, mm))
  • 8.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Computer Networks2011Annet (Annet (populærvitenskap, debatt, mm))
    Abstract [en]

    The International Journal of Computer and Telecommunications Networking (COMNET)

  • 9.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Computers & Security2014Annet (Annet (populærvitenskap, debatt, mm))
  • 10.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Concurrency and Computation: Practice and Experience2016Annet (Annet (populærvitenskap, debatt, mm))
  • 11.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Health Information Science and Systems (HISS)2016Annet (Annet (populærvitenskap, debatt, mm))
  • 12.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Information Sciences2013Annet (Annet (populærvitenskap, debatt, mm))
  • 13.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Intelligent Systems Reference Library, Vol. 702014Annet (Annet (populærvitenskap, debatt, mm))
  • 14.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: International Conference on Mobile and Ubiquitous Systems2013Annet (Annet (populærvitenskap, debatt, mm))
  • 15.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: International Conference on Telecommunications2016Annet (Annet (populærvitenskap, debatt, mm))
  • 16.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: International Conference on Telecommunications2016Annet (Annet (populærvitenskap, debatt, mm))
  • 17.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: International Japan-Egypt Conference on Electronics, Communications and Computers2013Annet (Annet (populærvitenskap, debatt, mm))
  • 18.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: International Journal of Computational Vision and Robotics. Special Issue on: "Advances in Soft Computing Techniques for Image Processing"2013Annet (Annet (populærvitenskap, debatt, mm))
    Abstract [en]

    Vol. 3, No. 4

  • 19.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Pattern Recognition2014Annet (Annet (populærvitenskap, debatt, mm))
    Abstract [en]

    The Journal of the Pattern Recognition Society

  • 20.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Science and Information Conference2014Annet (Annet (populærvitenskap, debatt, mm))
  • 21.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Science and Information Conference2013Annet (Annet (populærvitenskap, debatt, mm))
  • 22.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: SpringerPlus2015Annet (Annet (populærvitenskap, debatt, mm))
  • 23.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Studies in Computational Intelligence, Vol. 6302016Annet (Annet (populærvitenskap, debatt, mm))
  • 24.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: The 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013)2013Annet (Annet (populærvitenskap, debatt, mm))
  • 25.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: The 29th International Conference on Image and Vision Computing New Zealand (IVCNZ 2014)2014Annet (Annet (populærvitenskap, debatt, mm))
  • 26.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: The 30th International Conference on Image and Vision Computing New Zealand ( (IVCNZ 2015)2015Annet (Annet (populærvitenskap, debatt, mm))
  • 27.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: The 31st International Conference Image and Vision Computing New Zealand (IVCNZ 2016)2016Annet (Annet (populærvitenskap, debatt, mm))
  • 28.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: The 49th Hawaii International Conference on System Sciences (HICSS-49)2016Annet (Annet (populærvitenskap, debatt, mm))
  • 29.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Aktivitet: Workshop on Emerging Aspects in Information Security2014Annet (Annet (populærvitenskap, debatt, mm))
  • 30.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Erratum to : Fast Fingerprint Orientation Field Estimation Incorporating General Purpose GPU2016Inngår i: Soft Computing Applications: Proceedings of the 6th International Workshop Soft Computing Applications (SOFA 2014) / [ed] Valentina Emilia Balas; Lakhmi C. Jain; Branko Kovačević, New York: Encyclopedia of Global Archaeology/Springer Verlag, 2016, Vol. 2, s. E1-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Fingerprint is one of the broadly utilized biometric traits for personal identification in both civilian and forensic applications due to its high acceptability, strong security, and low cost. Fingerprint ridge orientation is one of the global fingerprint representations that keeps the holistic ridge structure in a small storage area. The importance of fingerprint ridge orientation comes from its usage in fingerprint singular point detection, coarse level classification, and fingerprint alignment. However, processing time is an important factor in any automatic fingerprint identification system, estimating that ridge orientation image may consume long processing time. This research presents an efficient ridge orientation estimation approach by incorporating a Graphics Processing Unit (GPU) capability to the traditional pixel gradient method. The simulation work shows a significant enhancement in ridge orientation estimation time by 6.41x using a general purpose GPU in comparison to the CPU execution.

  • 31.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Fast Fingerprint Orientation Field Estimation Incorporating General Purpose GPU2016Inngår i: Soft Computing Applications: Proceedings of the 6th International Workshop Soft Computing Applications (SOFA 2014) / [ed] Valentina Emilia Balas; Lakhmi C. Jain; Branko Kovačević, New York: Encyclopedia of Global Archaeology/Springer Verlag, 2016, Vol. 2, s. 891-902Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Fingerprint is one of the broadly utilized biometric traits for personal identification in both civilian and forensic applications due to its high acceptability, strong security, and low cost. Fingerprint ridge orientation is one of the global fingerprint representations that keeps the holistic ridge structure in a small storage area. The importance of fingerprint ridge orientation comes from its usage in fingerprint singular point detection, coarse level classification, and fingerprint alignment. However, processing time is an important factor in any automatic fingerprint identification system, estimating that ridge orientation image may consume long processing time. This research presents an efficient ridge orientation estimation approach by incorporating a Graphics Processing Unit (GPU) capability to the traditional pixel gradient method. The simulation work shows a significant enhancement in ridge orientation estimation time by 6.41x using a general purpose GPU in comparison to the CPU execution.

  • 32.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Fingerprint local invariant feature extraction on GPU with CUDA2013Inngår i: Informatica, ISSN 0350-5596, E-ISSN 1854-3871, Vol. 37, nr 3, s. 279-284Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Driven from its uniqueness, immutability, acceptability, and low cost, fingerprint is in a forefront between biometric traits. Recently, the GPU has been considered as a promising parallel processing technology due to its high performance computing, commodity, and availability. Fingerprint authentication is keep growing, and includes the deployment of many image processing and computer vision algorithms. This paper introduces the fingerprint local invariant feature extraction using two dominant detectors, namely SIFT and SURF, which are running on the CPU and the GPU. The paper focuses on the consumed time as an important factor for fingerprint identification. The experimental results show that the GPU implementations produce promising behaviors for both SIFT and SURF compared to the CPU one. Moreover, the SURF feature detector provides shorter processing time compared to the SIFT CPU and GPU implementations.

  • 33.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    From classical methods to animal biometrics: A review on cattle identification and tracking2016Inngår i: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 123, s. 423-435Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cattle, buffalo and cow, identification has recently played an influential role towards understanding disease trajectory, vaccination and production management, animal traceability, and animal ownership assignment. Cattle identification and tracking refers to the process of accurately recognizing individual cattle and their products via a unique identifier or marker. Classical cattle identification and tracking methods such as ear tags, branding, tattooing, and electrical methods have long been in use; however, their performance is limited due to their vulnerability to losses, duplications, fraud, and security challenges. Owing to their uniqueness, immutability, and low costs, biometric traits mapped into animal identification systems have emerged as a promising trend. Biometric identifiers for beef animals include muzzle print images, iris patterns, and retinal vascular patterns. Although using biometric identifiers has replaced human experts with computerized systems, it raises additional challenges in terms of identifier capturing, identification accuracy, processing time, and overall system operability. This article reviews the evolution in cattle identification and tracking from classical methods to animal biometrics. It reports on traditional animal identification methods and their advantages and problems. Moreover, this article describes the deployment of biometric identifiers for effectively identifying beef animals. The article presents recent research findings in animal biometrics, with a strong focus on cattle biometric identifiers such as muzzle prints, iris patterns, and retinal vascular patterns. A discussion of current challenges involved in the biometric-based identification systems appears in the conclusions, which may drive future research directions.

  • 34.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Introduction to Information Security Foundations and Applications2018Inngår i: Information Security: Foundations, technologies and applications / [ed] Ali Ismail Awad and Michael Fairhurst, Institution of Engineering and Technology, 2018, , s. 432s. 3-9Kapittel i bok, del av antologi (Fagfellevurdert)
  • 35.
    Awad, Ali Ismail
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Andrzej, BialasInstitute of Innovative Technologies EMAG.
    Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS 2017)2017Konferanseproceedings (Fagfellevurdert)
    Abstract [en]

    https://fedcsis.org/2017/insert

  • 36.
    Awad, Ali Ismail
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Furnell, Steven
    Centre for Security, Communications & Network Research, Plymouth University.
    Hassan, Abbas M.
    Faculty of Engineering, Qena, Al-Azhar University, Egypt.
    Tryfonas, Theo
    University of Bristol, United Kingdom.
    Special issue on security of IoT-enabled infrastructures in smart cities2019Inngår i: Ad hoc networks, ISSN 1570-8705, E-ISSN 1570-8713, Vol. 92, artikkel-id 101850Artikkel i tidsskrift (Fagfellevurdert)
  • 37.
    Awad, Ali Ismail
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Faculty of Engineering, Al-Azhar University, Qena, Egypt. Centre for Security, Communications and Network Research, University of Plymouth, Plymouth, UK.
    Hassaballah, M.
    Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt.
    Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images2019Inngår i: Applied Sciences, E-ISSN 2076-3417, Vol. 9, nr 22, artikkel-id 4914Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images. 

  • 38.
    Awad, Ali Ismail
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hassanien, Aboul Ella
    Cairo University.
    Erratum: Impact of Some Biometric Modalities on Forensic Science2014Inngår i: Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, Switzerland: Springer International Publishing , 2014, s. E1-Kapittel i bok, del av antologi (Fagfellevurdert)
  • 39.
    Awad, Ali Ismail
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hassanien, Aboul Ella
    Faculty of Computers and Information, Cairo University.
    Impact of Some Biometric Modalities on Forensic Science2014Inngår i: Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, Springer International Publishing , 2014, s. 47-62Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Recently, forensic science has had many challenges in many different types of crimes and crime scenes, vary from physical crimes to cyber or computer crimes. Accurate and efficient human identification or recognition have become crucial for forensic applications due to the large diversity of crime scenes, and because of the increasing need to accurately identify criminals from the available crime evidences. Biometrics is an emerging technology that provides accurate and highly secure personal identification and verification systems for civilian and forensic applications. The positive impact of biometric modalities on forensic science began with the rapid developments in computer science, computational intelligence, and computing approaches. These advancements have been reflected in the biometric modality capturing process, feature extraction, feature robustness, and features matching. A complete and automatic biometric identification or recognition systems have been built accordingly. This chapter presents a study of the impacts of using some biometric modalities in forensic applications. Although biometrics identification replaces human work with computerized and automatic systems in order to achieve better performance, new challenges have arisen. These challenges lie in biometric system reliability and accuracy, system response time, data mining and classification, and protecting user privacy. This chapter sheds light on the positive and the negative impacts of using some biometric modalities in forensic science. In particular, the impacts of fingerprint image, facial image, and iris patterns are considered. The selected modalities are covered preliminarily before tackling their impact on forensic applications. Furthermore, an extensive look at the future of biometric modalities deployment in forensic applications is covered as the last part of the chapter.

  • 40.
    Charif, Bilal
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Business and Government Organizations' Adoption of Cloud Computing2014Inngår i: Intelligent Data Engineering and Automated Learning – IDEAL 2014: 15th International Conference, Salamanca, Spain, September 10-12, 2014, Proceedings / [ed] Emilio Corchado; José A. Lozano; Héctor Quintián; Hujun Yin, Switzerland: Springer International Publishing , 2014, s. 492-501Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Cloud computing is very much accepted and acknowledged worldwide as a promising computing paradigm. On-demand provisioning based on a pay-per-use business model helps reduce costs through sharing computing and storage resources. Although cloud computing has become popular, some business and government organizations are still lagging behind in adopting cloud computing. This study reports the status of cloud utilization among business and government organizations, and the concerns of organizations regarding the adoption of cloud computing. The study shows that some government agencies are lagging behind in cloud computing use, while others are leading the way. Security is identified as the major reason for delay in adopting cloud computing. The outcomes of the data analysis process prove that some security measures such as encryption, access authentication, antivirus protection, firewall, and service availability are required by clients for adoption of cloud computing in the future.

  • 41.
    Charif, Bilal
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Towards smooth organisational adoption of cloud computing: a customer-provider security adaptation2016Inngår i: Computer fraud & security, ISSN 1361-3723, E-ISSN 1873-7056, Vol. 2016, nr 2, s. 7-15Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cloud computing is daily becoming more and more accepted as a promising computing paradigm. For some years now, cloud computing has been in common use for such applications as Google apps (email, documents, etc), MSN Messenger (instant messaging), Skype (voice communications), and Flickr (image sharing). The idea of offering cloud computing facilities as a public utility began as early as the 1960s with John McCarthy.1, 2, 3 and 4 Organisations and universities offered distributed computing starting in the late 1970s through dial-up access.5 Grid computing was introduced in the early 1990s with the idea of providing access to shared computing power similar to the way electricity is shared through the electric power grid. In addition, open source platforms were first introduced by Eucalyptus, OpenNebula, and Nimbus for deploying private and hybrid clouds.6, 7 and 8Although cloud computing has been available for some time, there is still some organisational resistance to its adoption, not least because of security concerns.Bilal Charif, of Luleå University of Technology, Sweden and Ali Ismail Awad of Luleå University and Al Azhar University, Egypt show that a number of organisations have no internal security responsibilities, nor do they have proper information security policies such as business and disaster recovery plans, all of which makes cloud adoption difficult. In contrast, cloud computing offers recovery plans for small and medium-sized organisations that will often otherwise not be implemented.

  • 42.
    Elrawy, Mohamed Faisal
    et al.
    Department of Electronics and Communication Engineering, MUST University, 6th of October, Egypt; Institute of Public Administration, Abha, Asir, Saudi Arabia.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Faculty of Engineering, Al-Azhar University, Qena, P.O. Box 83513, Egypt.
    Hamed, Hesham F. A.
    Faculty of Engineering, Minia University, Minia, Egypt.
    Intrusion detection systems for IoT-based smart environments: a survey2018Inngår i: Journal of Cloud Computing - Advances, Systems and Applications, ISSN 2192-113X, Vol. 7, nr 21, s. 1-20Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

  • 43.
    Elrawy, Mohammed Faisal
    et al.
    MUST University, 6th of October.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hamed, Hesham F. A.
    Minia University, Egypt.
    Flow-based Features for a Robust Intrusion Detection System Targeting Mobile Traffic2016Inngår i: 23rd International Conference on Telecommunications (ICT): Thessaloniki, 16-18 May 2016, Piscataway, NJ: IEEE Communications Society, 2016, artikkel-id 7500483Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The security risks and threats that impact wired and wireless networks are now applicable to mobile telecommunication networks. Threat detection systems should be more intelligent because threats are becoming more dangerous. An intrusion detection system (IDS) is a potential network security solution for protecting the confidentiality, integrity, and availability of user data and information resources. A fast and effective IDS for mobile networks that does not violate the user's privacy or the network's QoS is required. This paper offers a set of flow-based features that can be utilized for mobile network traffic as a prerequisite for a privacy-aware and QoS-robust IDS. The principal component analysis (PCA) method was used for reduction of the features. Twelve features in six groups, which represent the user data in mobile traffic, were extracted and evaluated for IDSs. The evaluation process achieved a F-measure weighted average equal to 0.834, and the experimental time was equal to 12.9 seconds. The accomplished measurements have demonstrated the applicability of the proposed set of features.

  • 44.
    Grzenda, Maciej
    et al.
    Faculty of Mathematics and Information Science, Warsaw University of Technology,, Research and Development Center.
    Furtak, Janusz
    Military University of Technology, Warszawa.
    Legierski, Jarosław
    Faculty of Mathematics and Information Science, Warsaw University of Technology, Warszawa.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Network Architectures, Security, and Applications: An Introduction2017Inngår i: Advances in Network Systems: Architectures, Security, and Applications / [ed] Maciej Grzenda, Ali Ismail Awad, Janusz Furtak, Jarosław Legierski, Berlin: Springer Berlin/Heidelberg, 2017, s. 1-10Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Owing to the ever growing communication systems, modern networks currently encompass a wide range of solutions and technologies, including wireless and wired networks and provide basis for network systems from multiple partly overlapping domains such as the Internet of Things (IoT), cloud services, and network applications. This appears in numerous active research areas with particular attention paid to the architecture and security of network systems. In parallel, novel applications are developed, in some cases strongly linked to rapidly developing network-based data acquisition and processing frameworks. This chapter presents a general introduction to the topics of network architectures, security, and applications in addition to short descriptions of the chapters included in this volume

  • 45.
    Hameed, Mohamed Abdel
    et al.
    Department of Computer Science, Faculty of Computers and Information, Luxor University, Luxor, Egypt.
    Hassaballah, M.
    Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt.
    Aly, Saleh
    Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    An Adaptive Image Steganography Method Based on Histogram of Oriented Gradient and PVD-LSB Techniques2019Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 185189-18204Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Pixel value differencing (PVD) and least significant bit substitution (LSB) are two widely used schemes in image steganography. These two methods do not consider different content in a cover image for hiding the secret data. The content of most digital images has different edge directions in each pixel, and the local object shape or appearance is mostly characterized by the distribution of its intensity gradients or edge directions. Exploiting these characteristics for embedding various secret information in different edge directions will eliminate sequential embedding and improve robustness. Thus, a histogram of oriented gradient (HOG) algorithm is proposed to find the dominant edge direction for each $2\times 2$ block of cover images. Blocks of interest (BOIs) are determined adaptively based on the gradient magnitude and angle of the cover image. Then, the PVD algorithm is used to hide secret data in the dominant edge direction, while the LSB substitution is utilized in the other two remaining pixels. Extensive experiments using various standard images reveal that the proposed scheme provides high embedding capacity and better visual quality compared with several other PVD- and LSB-based methods. Moreover, it resists various steganalysis techniques, such as pixel difference histogram and RS analysis.

  • 46.
    Hassaballah, Mahmoud
    et al.
    South Valley University, Qena, Egypt .
    Awad, Ali IsmailLuleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. Faculty of Engineering, Al-Azhar University, Qena, Egypt.
    Deep Learning in Computer Vision: Principles and Applications2020Collection/Antologi (Annet vitenskapelig)
    Abstract [en]

    Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

  • 47.
    Hassaballah, Mahmoud
    et al.
    Image and Video Processing Lab, Faculty, South Valley University.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Detection and Description of Image Features: An Introduction2016Inngår i: Image Feature Detectors and Descriptors: Foundations and Applications, Encyclopedia of Global Archaeology/Springer Verlag, 2016, s. 1-8Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Detection and description of image features play a vital role in various application domains such as image processing, computer vision, pattern recognition, and machine learning. There are two type of features that can be extracted from an image content; namely global and local features. Global features describe the image as a whole and can be interpreted as a particular property of the image involving all pixels; while, the local features aim to detect keypoints within the image and describe regions around these keypoints. After extracting the features and their descriptors from images, matching of common structures between images (i.e., features matching) is the next step for these applications. This chapter presents a general and brief introduction to topics of feature extraction for a variety of application domains. Its main aim is to provide short descriptions of the chapters included in this book volume.

  • 48.
    Hassan, Abbas M.
    et al.
    Department of Architecture, Faculty of Engineering, Al Azhar University .
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Electrical Engineering Department, Faculty of Engineering, Al Azhar University.
    Urban Transition in the Era of the Internet of Things: Social Implications and Privacy Challenges2018Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 6, s. 36428-36440Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Internet of Things (IoT) could become an important aspect of urban life in the next decade. In the IoT paradigm, various information and communication technologies (ICTs) are used in concert to substantially reduce urban problems. Smart cities and ubiquitous cities will adopt ICTs in the urban development process; however, IoT-based cities will experience considerably stronger effects than those that adopt conventional ICTs. IoT cities allow urban residents and “things”to be connected to the Internet by virtue of the extension of the Internet Protocol from IPv4 to IPv6 and of cutting-edge device and sensor technology. Therefore, the urban transition resulting from the influence of IoT may be a critical issue. The privacy-related vulnerabilities of IoT technologies may negatively affect city residents. Furthermore, disparities in the spread of IoT systems across different countries may allow some countries to subvert the privacy of other countries' citizens. The aim of this paper is to identify the potential prospects and privacy challenges that will emerge from IoT deployment in urban environments. This paper reviews the prospects of and barriers to IoT implementation at the regional, city, and residential scales from the perspectives of security and privacy. The IoT technology will be a continual presence in life in general and in urban life in particular. However, the adoption of the IoT paradigm in cities will be complicated due to the inherent presence of unsecured connections. Moreover, the IoT systems may rob people of some of their humanity, infringing on their privacy, because people are also regarded as “things”in the IoT paradigm. Given the social trepidation surrounding IoT implementation, local and international associations related to IoT privacy, and legislation and international laws, are needed to maintain the personal right to privacy and to satisfy the demands of institutional privacy in urban contexts.

  • 49.
    Hemanth, D. Jude
    Department of Electronics and Communication Engineering, Karunya University.
    Editorial: Special Issue on: Advances in Soft Computing Techniques for Image Processing2013Inngår i: International Journal of Computational Vision and Robotics, ISSN 1752-9131, Vol. 3, nr 4, s. 249-250Artikkel i tidsskrift (Fagfellevurdert)
  • 50.
    Hodoň, Michal
    et al.
    Departmentof Technical Cybernetics, University of Žilina, Žilina, Slovakia.
    Furtak, Janusz
    Institut of Teleinfomatics and Automation, Military University of Technology, Warsaw, Poland.
    Fahrnberger, Güenter
    Department of Communication Networks, University of Hagen, Hagen, Germany.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Editorial on Innovative Network Systems and Applications together with the Conference on Information Systems Innovations for Community Services2020Inngår i: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634Artikkel i tidsskrift (Annet vitenskapelig)
12 1 - 50 of 59
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