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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.

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

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.

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)
##### Note

Available from: 2020-01-02 Created: 2020-01-02 Last updated: 2020-01-02Bibliographically 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.

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)2-s2.0-85075233197 (Scopus ID)
##### Note

Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-12-09Bibliographically 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
Elsevier, 2019
Computer Systems
##### Research subject
Information systems
##### Identifiers
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.

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

Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-02-13Bibliographically approved
Munodawafa, F. & Awad, A. I. (2018). Security risk assessment within hybrid data centers: a case study of delay sensitive applications. Journal of Information Security and Applications, 43, 61-72
Open this publication in new window or tab >>Security risk assessment within hybrid data centers: a case study of delay sensitive applications
2018 (English)In: Journal of Information Security and Applications, ISSN 2214-2134, E-ISSN 2214-2126, Vol. 43, p. 61-72Article in journal (Refereed) Published
##### Abstract [en]

Data centers are considered a critical form of infrastructure in today’s information society. They are at the core of the cloud computing and services revolution, which has changed the business models on how organizations deal with IT infrastructure costs. The hybrid data center architecture incorporates both legacy and fully virtualized infrastructures. On the one hand, the composite infrastructure has improved resource utilization and consolidation by adding flexibility and scalability factors, making the data center more cost effective and more agile. On the other hand, the hybrid infrastructure has imposed a new set of security challenges that need to be brought into focus. The lack of resource availability can be a great risk for delay sensitive applications such as voice over IP (VoIP) and online gaming when cloud computing is the deployment model. This study addresses the emerging risk problem by conducting a comprehensive security risk assessment using the NIST national vulnerability database (NVD) combined with EBIOS risk analysis and evaluation methodology. This study focuses on resource availability problem emanating from delay variations and queuing mechanisms in virtualized systems and its impact on delay sensitive applications. The study argues for the existence of availability risk within the hybrid data center infrastructure, which can deteriorate the performance of delay sensitive applications. Security remedial and countermeasures to the identified security risks are suggested in an extended discussion at the end of the study.

Elsevier, 2018
##### Keywords
Data centers, Virtualization, Security risk assessment, Infrastructure flexibility and scalability, Network resources availability, Delay sensitive applications
##### National Category
Information Systems, Social aspects
##### Research subject
Information systems
##### Identifiers
urn:nbn:se:ltu:diva-71366 (URN)10.1016/j.jisa.2018.10.008 (DOI)000451090000007 ()2-s2.0-85055587072 (Scopus ID)
##### Note

Available from: 2018-10-29 Created: 2018-10-29 Last updated: 2019-04-02Bibliographically approved
Khaled Abd-Ellah, M., Awad, A. I., Khalaf, A. A. M. & Hamed, H. F. A. (2018). Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2018, Article ID 97.
Open this publication in new window or tab >>Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks
2018 (English)In: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, Vol. 2018, article id 97Article in journal (Refereed) Published
##### Abstract [en]

Brain tumour is a serious disease, and the number of people who are dying due to brain tumours is increasing. Manual tumour diagnosis from magnetic resonance images (MRIs) is a time consuming process and is insufficient for accurately detecting, localizing, and classifying the tumour type. This research proposes a novel two-phase multi-model automatic diagnosis system for brain tumour detection and localization. In the first phase, the system structure consists of preprocessing, feature extraction using a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) approach. The purpose of the first system phase is to detect brain tumour by classifying the MRIs into normal and abnormal images. The aim of the second system phase is to localize the tumour within the abnormal MRIs using a fully designed five-layer region-based convolutional neural network (R-CNN). The performance of the first phase was assessed using three CNN models, namely, AlexNet, Visual Geometry Group (VGG)-16, and VGG-19, and a maximum detection accuracy of 99.55% was achieved with AlexNet using 349 images extracted from the standard Reference Image Database to Evaluate Response (RIDER) Neuro MRI database. The brain tumour localization phase was evaluated using 804 3D MRIs from the Brain Tumor Segmentation (BraTS) 2013 database, and a DICE score of 0.87 was achieved. The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature. The obtained results also demonstrate the superiority of the proposed system concerning both tumour detection and localization.

Springer, 2018
##### Keywords
Brain tumour diagnosis, MRI segmentation, Tumour detection and localization, Convolutional neural networks (CNNs)
##### National Category
Information Systems, Social aspects
##### Research subject
Information systems
##### Identifiers
urn:nbn:se:ltu:diva-71054 (URN)10.1186/s13640-018-0332-4 (DOI)000446234200001 ()2-s2.0-85054149973 (Scopus ID)
##### Note

Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-22Bibliographically approved
Hassan, A. M. & Awad, A. I. (2018). Urban Transition in the Era of the Internet of Things: Social Implications and Privacy Challenges. IEEE Access, 6, 36428-36440
Open this publication in new window or tab >>Urban Transition in the Era of the Internet of Things: Social Implications and Privacy Challenges
2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 36428-36440Article in journal (Refereed) Published
##### 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.

IEEE, 2018
##### National Category
Information Systems, Social aspects
##### Research subject
Information systems
##### Identifiers
urn:nbn:se:ltu:diva-69008 (URN)10.1109/ACCESS.2018.2838339 (DOI)000439022000103 ()2-s2.0-85047198293 (Scopus ID)
##### Note

Available from: 2018-05-31 Created: 2018-05-31 Last updated: 2018-08-16Bibliographically approved
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M. & Hamed, H. F. A. (2017). Design and implementation of a computer-aided diagnosis system for brain tumor classification. In: 2016 28th International Conference on Microelectronics (ICM): . Paper presented at 28th International Conference on Microelectronics (ICM), Cairo, Egypt, 17-20 Dec. 2016 (pp. 73-76). , Article ID 7847911.
Open this publication in new window or tab >>Design and implementation of a computer-aided diagnosis system for brain tumor classification
2017 (English)In: 2016 28th International Conference on Microelectronics (ICM), 2017, p. 73-76, article id 7847911Conference paper, Published paper (Refereed)
##### 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.

##### National Category
Information Systems, Social aspects
##### Research subject
Information systems
##### Identifiers
urn:nbn:se:ltu:diva-61988 (URN)10.1109/ICM.2016.7847911 (DOI)000399706600019 ()2-s2.0-85014917541 (Scopus ID)978-1-5090-5721-4 (ISBN)
##### Conference
28th International Conference on Microelectronics (ICM), Cairo, Egypt, 17-20 Dec. 2016
Available from: 2017-02-14 Created: 2017-02-14 Last updated: 2018-03-09Bibliographically approved
Grzenda, M., Furtak, J., Legierski, J. & Awad, A. I. (2017). Network Architectures, Security, and Applications: An Introduction. In: Maciej Grzenda, Ali Ismail Awad, Janusz Furtak, Jarosław Legierski (Ed.), Advances in Network Systems: Architectures, Security, and Applications (pp. 1-10). Berlin: Springer Berlin/Heidelberg
Open this publication in new window or tab >>Network Architectures, Security, and Applications: An Introduction
2017 (English)In: Advances in Network Systems: Architectures, Security, and Applications / [ed] Maciej Grzenda, Ali Ismail Awad, Janusz Furtak, Jarosław Legierski, Berlin: Springer Berlin/Heidelberg, 2017, p. 1-10Chapter in book (Refereed)
##### 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

##### Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2017
##### Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 461
##### National Category
Information Systems, Social aspects
##### Research subject
Information systems
##### Identifiers
urn:nbn:se:ltu:diva-61302 (URN)10.1007/978-3-319-44354-6_1 (DOI)2-s2.0-85007502875 (Scopus ID)978-3-319-44352-2 (ISBN)978-3-319-44354-6 (ISBN)
Available from: 2017-01-02 Created: 2017-01-02 Last updated: 2018-03-09Bibliographically approved
##### Identifiers
ORCID iD: orcid.org/0000-0002-3800-0757

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