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Publications (10 of 57) Show all publications
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M. & Hamed, H. F. A. (2020). Deep Convolutional Neural Networks: Foundations and Applications in Medical Imaging (1sted.). In: Mahmoud Hassaballah, Ali Ismail Awad (Ed.), Deep Learning in Computer Vision: Principles and Applications (pp. 233-260). CRC Press
Open this publication in new window or tab >>Deep Convolutional Neural Networks: Foundations and Applications in Medical Imaging
2020 (English)In: Deep Learning in Computer Vision: Principles and Applications / [ed] Mahmoud Hassaballah, Ali Ismail Awad, CRC Press, 2020, 1st, p. 233-260Chapter in book (Other academic)
Place, publisher, year, edition, pages
CRC Press, 2020 Edition: 1st
Series
Digital Imaging and Computer Vision Series
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-78218 (URN)10.1201/9781351003827-9 (DOI)9781351003827 (ISBN)
Available from: 2020-03-26 Created: 2020-03-26 Last updated: 2020-03-26Bibliographically approved
Hassaballah, M. & Awad, A. I. (Eds.). (2020). Deep Learning in Computer Vision: Principles and Applications (1sted.). CRC Press
Open this publication in new window or tab >>Deep Learning in Computer Vision: Principles and Applications
2020 (English)Collection (editor) (Other academic)
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.

Place, publisher, year, edition, pages
CRC Press, 2020. p. 338 Edition: 1st
Series
Digital Imaging and Computer Vision Series
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-78216 (URN)10.1201/9781351003827 (DOI)9781351003827 (ISBN)
Available from: 2020-03-26 Created: 2020-03-26 Last updated: 2020-03-26Bibliographically approved
Hodoň, M., Furtak, J., Fahrnberger, G. & Awad, A. I. (2020). Editorial on Innovative Network Systems and Applications together with the Conference on Information Systems Innovations for Community Services. Concurrency and Computation
Open this publication in new window or tab >>Editorial on Innovative Network Systems and Applications together with the Conference on Information Systems Innovations for Community Services
2020 (English)In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634Article in journal, Editorial material (Other academic) Epub ahead of print
Place, publisher, year, edition, pages
John Wiley & Sons, 2020
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-77827 (URN)10.1002/cpe.5698 (DOI)000513946000001 ()2-s2.0-85079634577 (Scopus ID)
Available from: 2020-02-25 Created: 2020-02-25 Last updated: 2020-04-20
Mamdouh, M., Awad, A. I., Hamed, H. F. A. & Khalaf, A. A. M. (2020). Outlook on Security and Privacy in IoHT: Key Challenges and Future Vision. In: Aboul-Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Diego Oliva, Fahmy M. Tolba (Ed.), Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020): . Paper presented at International Conference on Artificial Intelligence and Computer Vision (AICV2020); April 8–9, 2020; Cairo, Egypt (pp. 721-730). Springer
Open this publication in new window or tab >>Outlook on Security and Privacy in IoHT: Key Challenges and Future Vision
2020 (English)In: Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) / [ed] Aboul-Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Diego Oliva, Fahmy M. Tolba, Springer, 2020, p. 721-730Conference paper, Published paper (Other academic)
Abstract [en]

The Internet of Things (IoT) security and privacy have received considerable research attention due to the IoT applicability in various domains. IoT systems have several applications, such as smart homes, smart cities, e-Health, industry, agriculture, and environmental monitoring. One of the most important applications is the Internet of Healthcare Things (IoHT) because it helps humans and patients obtain rapid diagnoses, remote monitoring, and home rehabilitation. IoHT security can be classified into four categories: applications, architecture, communication, and data security. This paper presents a short, but focused, review on IoHT security and privacy. It also explores recent security algorithms and protocols that are used to secure personal patient data, clinicians, and healthcare information. The future vision of IoHT challenges and countermeasures is given at the end of this study. Blockchain healthcare technology provides secure digital payment and privileged data access.

Place, publisher, year, edition, pages
Springer, 2020
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; Volume 1153
Keywords
Security, Privacy, IoHT, Threats, Blockchain
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-78220 (URN)10.1007/978-3-030-44289-7_67 (DOI)978-3-030-44288-0 (ISBN)978-3-030-44289-7 (ISBN)
Conference
International Conference on Artificial Intelligence and Computer Vision (AICV2020); April 8–9, 2020; Cairo, Egypt
Available from: 2020-03-26 Created: 2020-03-26 Last updated: 2020-03-26Bibliographically approved
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. .. & Hamed, H. F. .. (2019). A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned. Magnetic Resonance Imaging, 61, 300-318
Open this publication in new window or tab >>A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned
2019 (English)In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 61, p. 300-318Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Brain tumor diagnosis, Computer-aided methods, MRI images, Tumor detection, Tumor segmentation, Tumor classification, Traditional machine learning techniques, Deep learning techniques
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-74505 (URN)10.1016/j.mri.2019.05.028 (DOI)000479327400035 ()31173851 (PubMedID)2-s2.0-85067489010 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-06-03 (svasva)

Available from: 2019-06-13 Created: 2019-06-13 Last updated: 2019-08-29Bibliographically approved
Hameed, M. A., Hassaballah, M., Aly, S. & Awad, A. I. (2019). An Adaptive Image Steganography Method Based on Histogram of Oriented Gradient and PVD-LSB Techniques. IEEE Access, 7, 185189-18204
Open this publication in new window or tab >>An Adaptive Image Steganography Method Based on Histogram of Oriented Gradient and PVD-LSB Techniques
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 185189-18204Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Data hiding, steganography, pixel value differencing, least significant bit, histogram of oriented gradient, HOG
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-77281 (URN)10.1109/ACCESS.2019.2960254 (DOI)000510021700063 ()2-s2.0-85077970123 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-01-02 (johcin)

Available from: 2020-01-02 Created: 2020-01-02 Last updated: 2020-04-16Bibliographically approved
Awad, A. I. & Hassaballah, M. (2019). Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images. Applied Sciences, 9(22), Article ID 4914.
Open this publication in new window or tab >>Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
2019 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 9, no 22, article id 4914Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
computer vision, biometrics, cattle identification, bag-of-visual-words, muzzle print images
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-76740 (URN)10.3390/app9224914 (DOI)000502570800191 ()2-s2.0-85075233197 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-11-19 (johcin)

Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2020-02-25Bibliographically approved
Abd-Ellah, M. K., Awad, A. I., Hamed, H. F. A. & Khalaf, A. A. M. (2019). Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images. In: IEEE-ICM 2019 CAIRO-EGYPT: The 31st International Conference on Microelectronics. Paper presented at 31st IEEE International Conference on Microelectronics (ICM2019), December 15-18, 2019, Cairo, Egypt (pp. 304-307). IEEE
Open this publication in new window or tab >>Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images
2019 (English)In: IEEE-ICM 2019 CAIRO-EGYPT: The 31st International Conference on Microelectronics, IEEE, 2019, p. 304-307Conference paper, Published paper (Other academic)
Abstract [en]

Although most brain tumor diagnosis studies have focused on tumor segmentation and localization operations, few studies have focused on tumor detection as a time- and effort-saving process. This study introduces a new network structure for accurate glioma tumor detection and classification using two parallel deep convolutional neural networks (PDCNNs). The proposed structure is designed to identify the presence and absence of a brain tumor in MRI images and classify the type of tumor images as high-grade gliomas (HGGs, i.e., glioblastomas) or low-grade gliomas (LGGs). The introduced PDCNNs structure takes advantage of both global and local features extracted from the two parallel stages. The proposed structure is not only accurate but also efficient, as the convolutional layers are more accurate because they learn spatial features, and they are efficient in the testing phase since they reduce the number of weights, which reduces the memory usage and runtime. Simulation experiments were accomplished using an MRI dataset extracted from the BraTS 2017 database. The obtained results show that the proposed parallel network structure outperforms other detection and classification methods in the literature.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International Conference on Microelectronics, ICM
Keywords
Computer-aided diagnosis, brain tumor detection, deep learning, convolutional neural networks, glioma classification
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-78648 (URN)10.1109/ICM48031.2019.9021872 (DOI)2-s2.0-85082101535 (Scopus ID)
Conference
31st IEEE International Conference on Microelectronics (ICM2019), December 15-18, 2019, Cairo, Egypt
Note

ISBN för värdpublikation: 978-1-7281-4058-2

Available from: 2020-04-24 Created: 2020-04-24 Last updated: 2020-04-24Bibliographically approved
Awad, A. I., Furnell, S., Hassan, A. M. & Tryfonas, T. (2019). Special issue on security of IoT-enabled infrastructures in smart cities. Ad hoc networks, 92, Article ID 101850.
Open this publication in new window or tab >>Special issue on security of IoT-enabled infrastructures in smart cities
2019 (English)In: Ad hoc networks, ISSN 1570-8705, E-ISSN 1570-8713, Vol. 92, article id 101850Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Computer Systems
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-73123 (URN)10.1016/j.adhoc.2019.02.007 (DOI)
Available from: 2019-03-06 Created: 2019-03-06 Last updated: 2019-12-09Bibliographically approved
Elrawy, M. F., Awad, A. I. & Hamed, H. F. A. (2018). Intrusion detection systems for IoT-based smart environments: a survey. Journal of Cloud Computing - Advances, Systems and Applications, 7(21), 1-20
Open this publication in new window or tab >>Intrusion detection systems for IoT-based smart environments: a survey
2018 (English)In: Journal of Cloud Computing - Advances, Systems and Applications, ISSN 2192-113X, Vol. 7, no 21, p. 1-20Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Intrusion detection systems, Internet-of-Things, Smart environments
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-71908 (URN)10.1186/s13677-018-0123-6 (DOI)000452332000001 ()2-s2.0-85057804579 (Scopus ID)
Note

Validerad;2019;Nivå 2;2018-12-06 (svasva)

Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-02-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3800-0757

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