| 1. |
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| 2. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images
- 2019
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Ingår i: IEEE-ICM 2019 CAIRO-EGYPT. - : IEEE. ; , s. 304-307
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 3. |
- Awad, Ali Ismail, et al.
(författare)
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Preface
- 2021
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Ingår i: Security in Cyber-Physical Systems. - : Springer Nature. ; , s. v-ix
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Awad, Ali Ismail, et al.
(författare)
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Preface
- 2022
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Ingår i: Security and Privacy in the Internet of Things: Architectures, Techniques, and Applications. - : John Wiley & Sons. ; , s. xix-xxiii
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 5. |
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| 6. |
- Baker, Thar, et al.
(författare)
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Enabling Technologies for Energy Cloud
- 2021
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 152, s. 108-110
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- We are thrilled and delighted to present this special issue, which emphasises on the novel area of Enabling Technologies for Energy Cloud. This guest editorial provides an overview of all articles accepted for publication in this special issue.
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| 7. |
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Deep Learning in Computer Vision : Principles and Applications
- 2020. - 1st
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 8. |
- Hemanth, D. Jude, et al.
(författare)
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Editorial
- 2013
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Ingår i: International Journal of Computational Vision and Robotics. - 1752-9131. ; 3:4, s. 249-250
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 9. |
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| 10. |
- Mamdouh, Moustafa, et al.
(författare)
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Outlook on Security and Privacy in IoHT : Key Challenges and Future Vision
- 2020
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Ingår i: Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). - Cham : Springer. - 9783030442880 - 9783030442897 ; , s. 721-730
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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