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ORCIDSadi Alawadi
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2020 – today
- 2024
[j17]Sadi Alawadi
, Khalid Alkharabsheh, Fahed Alkhabbas, Victor R. Kebande, Feras M. Awaysheh, Fabio Palomba, Mohammed Awad:
FedCSD: A Federated Learning Based Approach for Code-Smell Detection. IEEE Access 12: 44888-44904 (2024)- [j16]Hussam N. Fakhouri, Sadi Alawadi, Feras M. Awaysheh, Faten Hamad:
Novel hybrid success history intelligent optimizer with Gaussian transformation: application in CNN hyperparameter tuning. Clust. Comput. 27(3): 3717-3739 (2024) - [j15]Hussam N. Fakhouri, Feras M. Awaysheh, Sadi Alawadi, Mohannad Alkhalaileh, Faten Hamad:
Four vector intelligent metaheuristic for data optimization. Computing 106(7): 2321-2359 (2024) - [j14]Sadi Alawadi, Addi Ait-Mlouk, Salman Toor, Andreas Hellander:
Toward efficient resource utilization at edge nodes in federated learning. Prog. Artif. Intell. 13(2): 101-117 (2024) - [c16]Oscar Cederlund, Sadi Alawadi, Feras M. Awaysheh:
LLMRAG: An Optimized Digital Support Service using LLM and Retrieval-Augmented Generation. FMEC 2024: 54-62 - [c15]Victor Arvidsson, Sadi Alawadi, Martin Boldt, Ola Angelsmark, Fanny Söderlund:
A Novel Approach for Intrusion Detection using Online Federated Learning on Streaming Data. FMEC 2024: 114-121 - [c14]Jonne van Dreven, Abbas Cheddad, Sadi Alawadi, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt:
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations. FMEC 2024: 130-137 - [c13]Gustav Nilsson, Martin Boldt, Sadi Alawadi:
The Role of the Data Quality on Model Efficiency: An Exploratory Study on Centralised and Federated Learning. FMEC 2024: 253-260 - [i7]Jonne van Dreven, Abbas Cheddad, Sadi Alawadi, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt:
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations. CoRR abs/2408.14499 (2024) - 2023
- [j13]Sultan M. Al Khatib, Khalid Alkharabsheh, Sadi Alawadi:
Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study. Inf. Softw. Technol. 155: 107120 (2023) - [c12]Marc Haller, Christian Lenz, Robin Nachtigall, Feras M. Awaysheh, Sadi Alawadi:
Handling Non-IID Data in Federated Learning: An Experimental Evaluation Towards Unified Metrics. DASC/PiCom/CBDCom/CyberSciTech 2023: 762-770 - [c11]Hussam N. Fakhouri, Sadi Alawadi, Feras M. Awaysheh, Faten Hamad, Sawsan Al-Zubi, Mohammad Aladwan:
An Overview of using of Artificial Intelligence in Enhancing Security and Privacy in Mobile Social Networks. FMEC 2023: 42-51 - [c10]Fahed Alkhabbas, Sadi Alawadi, Majed Ayyad, Romina Spalazzese, Paul Davidsson:
ART4FL: An Agent-Based Architectural Approach for Trustworthy Federated Learning in the IoT. FMEC 2023: 270-275 - [c9]Hiroki Kaminaga, Feras M. Awaysheh, Sadi Alawadi, Liina Kamm:
MPCFL: Towards Multi-party Computation for Secure Federated Learning Aggregation. UCC 2023: 19 - [i6]Addi Ait-Mlouk, Sadi Alawadi, Salman Toor, Andreas Hellander:
FedBot: Enhancing Privacy in Chatbots with Federated Learning. CoRR abs/2304.03228 (2023) - [i5]Sadi Alawadi, Khalid Alkharabsheh, Fahed Alkhabbas, Victor R. Kebande, Feras M. Awaysheh, Fabio Palomba:
FedCSD: A Federated Learning Based Approach for Code-Smell Detection. CoRR abs/2306.00038 (2023) - [i4]Sadi Alawadi, Addi Ait-Mlouk, Salman Toor, Andreas Hellander:
Toward efficient resource utilization at edge nodes in federated learning. CoRR abs/2309.10367 (2023) - [i3]Alexander Galozy, Sadi Alawadi, Victor R. Kebande, Slawomir Nowaczyk:
Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study. CoRR abs/2310.00221 (2023) - 2022
- [j12]Khalid Alkharabsheh, Sadi Alawadi, Victor R. Kebande, Yania Crespo, Manuel Fernández Delgado, José A. Taboada:
A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class. Inf. Softw. Technol. 143: 106736 (2022) - [j11]Khalid Alkharabsheh, Sadi Alawadi, Karam Ignaim, Nabeel Zanoon, Yania Crespo, M. Esperanza Manso, José Ángel Taboada González:
Prioritization of god class design smell: A multi-criteria based approach. J. King Saud Univ. Comput. Inf. Sci. 34(10 Part B): 9332-9342 (2022) - [j10]Fahed Alkhabbas, Mohammed Alsadi, Sadi Alawadi, Feras M. Awaysheh, Victor R. Kebande, Mahyar Tourchi Moghaddam:
ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems. Sensors 22(18): 6842 (2022) - [j9]Feras M. Awaysheh, Mohammad Aladwan, Mamoun Alazab, Sadi Alawadi, José Carlos Cabaleiro, Tomás F. Pena:
Security by Design for Big Data Frameworks Over Cloud Computing. IEEE Trans. Engineering Management 69(6): 3676-3693 (2022) - [c8]Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi, Mattias Åkesson, Prashant Singh, Ola Spjuth, Salman Toor, Andreas Hellander:
Scalable federated machine learning with FEDn. CCGRID 2022: 555-564 - [c7]Feras M. Awaysheh, Sadi Alawadi, Sawsan Al-Zubi:
FLIoDT: A Federated Learning Architecture from Privacy by Design to Privacy by Default over IoT. FMEC 2022: 1-6 - [i2]Addi Ait-Mlouk, Sadi Alawadi, Salman Toor, Andreas Hellander:
FedQAS: Privacy-aware machine reading comprehension with federated learning. CoRR abs/2202.04742 (2022) - 2021
- [j8]Victor R. Kebande, Sadi Alawadi, Feras M. Awaysheh, Jan A. Persson:
Active Machine Learning Adversarial Attack Detection in the User Feedback Process. IEEE Access 9: 36908-36923 (2021) - [j7]Khalid Alkharabsheh, Sadi Alawadi, Yania Crespo, M. Esperanza Manso, José A. Taboada:
Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection. IEEE Access 9: 145191-145211 (2021) - [j6]Victor R. Kebande, Nickson M. Karie, Kim-Kwang Raymond Choo, Sadi Alawadi:
Digital forensic readiness intelligence crime repository. Secur. Priv. 4(3) (2021) - [j5]Sheunesu M. Makura, H. S. Venter, Victor R. Kebande, Nickson M. Karie, Richard Adeyemi Ikuesan, Sadi Alawadi:
Digital forensic readiness in operational cloud leveraging ISO/IEC 27043 guidelines on security monitoring. Secur. Priv. 4(3) (2021) - [j4]Victor R. Kebande, Feras M. Awaysheh, Richard Adeyemi Ikuesan, Sadi Alawadi, Mohammad Dahman Alshehri:
A Blockchain-Based Multi-Factor Authentication Model for a Cloud-Enabled Internet of Vehicles. Sensors 21(18): 6018 (2021) - [c6]Sadi Alawadi, Victor R. Kebande, Yuji Dong, Joseph Bugeja, Jan A. Persson, Carl Magnus Olsson:
A Federated Interactive Learning IoT-Based Health Monitoring Platform. ADBIS (Short Papers) 2021: 235-246 - [c5]Victor R. Kebande, Richard Adeyemi Ikuesan, Feras M. Awaysheh, Sadi Alawadi:
Towards a Learning-enabled Virtual Sensor Forensic Architecture Compliant with Edge Intelligence. IDSTA 2021: 154-161 - [i1]Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi, Mattias Åkesson, Desislava Stoyanova, Ola Spjuth, Salman Toor, Andreas Hellander:
Scalable federated machine learning with FEDn. CoRR abs/2103.00148 (2021) - 2020
- [j3]Mohammad Aladwan, Feras M. Awaysheh, Sadi Alawadi, Mamoun Alazab, Tomás F. Pena, José Carlos Cabaleiro:
TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud. IEEE Trans. Ind. Informatics 16(9): 6203-6213 (2020) - [c4]Victor R. Kebande, Sadi Alawadi, Joseph Bugeja, Jan A. Persson, Carl Magnus Olsson:
Leveraging Federated Learning & Blockchain to counter Adversarial Attacks in Incremental Learning. IOT Companion 2020: 2:1-2:5 - [c3]Fahed Alkhabbas, Sadi Alawadi, Romina Spalazzese, Paul Davidsson:
Activity recognition and user preference learning for automated configuration of IoT environments. IOT 2020: 3:1-3:8
2010 – 2019
- 2019
- [j2]Sadi Alawadi, Manuel Fernández Delgado, David Mera, Senén Barro:
Polynomial Kernel Discriminant Analysis for 2D visualization of classification problems. Neural Comput. Appl. 31(8): 3515-3531 (2019) - [j1]Manuel Fernández Delgado, Manisha Sanjay Sirsat, Eva Cernadas, Sadi Alawadi, Senén Barro, Manuel Febrero-Bande:
An extensive experimental survey of regression methods. Neural Networks 111: 11-34 (2019) - [c2]Fabio Paternò, Sadi Alawadi:
Towards Intelligent Personalization of IoT Platforms. IUI Workshops 2019 - 2017
- [c1]Sadi Alawadi, David Mera, Manuel Fernández Delgado, José A. Taboada:
Comparative Study of Artificial Neural Network Models for Forecasting the Indoor Temperature in Smart Buildings. Smart-CT 2017: 29-38
Coauthor Index
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