Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Emad Alsuwat"'
1
Akademický článek
Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network
Autor: Suhare Solaiman, Emad Alsuwat, Rajwa Alharthi
Publikováno v: Applied System Innovation, Vol 6, Iss 4, p 68 (2023)
In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone
Externí odkaz: https://doaj.org/article/d66138e7d3cc4b0d821c3428f38e3804
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2
Akademický článek
Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map
Autor: Majed Alsafyani, Fahad Alhomayani, Hatim Alsuwat, Emad Alsuwat
Publikováno v: Sensors, Vol 23, Iss 3, p 1415 (2023)
Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of co
Externí odkaz: https://doaj.org/article/16c28f554a0d49638f5f7d92eff8322a
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5
Adversarial data poisoning attacks against the PC learning algorithm
Autor: Csilla Farkas, Marco Valtorta, Emad Alsuwat, Hatim Alsuwat
Publikováno v: International Journal of General Systems. 49:3-31
Data integrity is a key component of effective Bayesian network structure learning algorithms, namely PC algorithm, design and use. Given the role that integrity of data plays in these outcomes, th...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::575ede364c04f95cb2135548f03c9446
https://doi.org/10.1080/03081079.2019.1630401
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6
Detecting COVID-19 Utilizing Probabilistic Graphical Models
Autor: Hatim Alsuwat, Sabah Alzahrani, Emad Alsuwat
Publikováno v: International Journal of Advanced Computer Science and Applications. 12
Probabilistic graphical models are employed in a variety of areas such as artificial intelligence and machine learning to depict causal relations among sets of random variables. In this research, we employ probabilistic graphical models in the form o
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b9bb57eeae0c2d2665ce56b09998b5a9
https://doi.org/10.14569/ijacsa.2021.0120692
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7
Detecting Adversarial Attacks in the Context of Bayesian Networks
Autor: Marco Valtorta, Emad Alsuwat, John R. Rose, Csilla Farkas, Hatim Alsuwat
Publikováno v: Lecture Notes in Computer Science
33th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec)
33th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), Jul 2019, Charleston, SC, United States. pp.3-22, ⟨10.1007/978-3-030-22479-0_1⟩
Data and Applications Security and Privacy XXXIII ISBN: 9783030224783
DBSec
Part 1: Attacks; International audience; In this research, we study data poisoning attacks against Bayesian network structure learning algorithms. We propose to use the distance between Bayesian network models and the value of data conflict to detect
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f6a070ee75b86006826bca7f5252659
https://hal.inria.fr/hal-02384585/document
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8
Data Dependencies Preserving Shuffle in Relational Database
Autor: Emad Alsuwat, Tieming Geng, Hatim Alsuwat, Chin-Tser Huang, Csilla Farkas
Publikováno v: ICDIS
This paper addresses the problem that database shuffling algorithms do not preserve data dependencies. We introduce an approach for preserving functional dependencies and data-driven associations during database shuffle. We use Boyce-Codd Normal Form
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c6f6b860083b46ff055e1cdf718ba0a8
https://doi.org/10.1109/icdis.2019.00034
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9
Modeling Concept Drift in the Context of Discrete Bayesian Networks
Autor: Emad Alsuwat, Hatim Alsuwat, John R. Rose, Marco Valtorta, Csilla Farkas
Publikováno v: KDIR
Concept drift is a significant challenge that greatly influences the accuracy and reliability of machine learning models. There is, therefore, a need to detect concept drift in order to ensure the validity of learned models. In this research, we stud
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a970f9228bb6954eaacb22df0afa73f8
https://doi.org/10.5220/0008384702140224
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10
Cyber Attacks Against the PC Learning Algorithm
Autor: Emad Alsuwat, Marco Valtorta, Csilla Farkas, Hatim Alsuwat
Publikováno v: ECML PKDD 2018 Workshops ISBN: 9783030134525
Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML
Data integrity is a key requirement for correct machine learning applications, such as Bayesian network structure learning algorithms. This research studies how an adversary could corrupt the PC structure learning algorithm by inserting fake data. We
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::18ca7058349073463f62c2f7f6ae1a3f
https://doi.org/10.1007/978-3-030-13453-2_13
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