Hossam Hawash
Orcid: 0000-0001-9925-3232
According to our database1,
Hossam Hawash
authored at least 32 papers
between 2020 and 2024.
Collaborative distances:
Collaborative distances:
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Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
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Bibliography
2024
Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey.
Ann. Oper. Res., August, 2024
Generalizable Segmentation of COVID-19 Infection From Multi-Site Tomography Scans: A Federated Learning Framework.
IEEE Trans. Emerg. Top. Comput. Intell., February, 2024
DeepSecDrive: An explainable deep learning framework for real-time detection of cyberattack in in-vehicle networks.
Inf. Sci., February, 2024
Next generation of computer vision for plant disease monitoring in precision agriculture: A contemporary survey, taxonomy, experiments, and future direction.
Inf. Sci., 2024
Privacy-preserved learning from non-i.i.d data in fog-assisted IoT: A federated learning approach.
Digit. Commun. Networks, 2024
2023
Digital Twin for Optimization of Slicing-Enabled Communication Networks: A Federated Graph Learning Approach.
IEEE Commun. Mag., October, 2023
Fed-ESD: Federated learning for efficient epileptic seizure detection in the fog-assisted internet of medical things.
Inf. Sci., June, 2023
MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.
Inf. Sci., April, 2023
FV-Seg-Net: Fully Volumetric Network for Accurate Segmentation of COVID-19 Lesions From Chest CT Scans.
IEEE Trans. Ind. Informatics, March, 2023
Dataset, January, 2023
Privacy-Preserved Generative Network for Trustworthy Anomaly Detection in Smart Grids: A Federated Semisupervised Approach.
IEEE Trans. Ind. Informatics, 2023
MT-nCov-Net: A Multitask Deep-Learning Framework for Efficient Diagnosis of COVID-19 Using Tomography Scans.
IEEE Trans. Cybern., 2023
Efficient and Lightweight Convolutional Networks for IoT Malware Detection: A Federated Learning Approach.
IEEE Internet Things J., 2023
2022
Studies in Computational Intelligence 997, Springer, ISBN: 978-3-030-89024-7, 2022
IEEE Trans. Intell. Transp. Syst., 2022
Privacy-Preserved Cyberattack Detection in Industrial Edge of Things (IEoT): A Blockchain-Orchestrated Federated Learning Approach.
IEEE Trans. Ind. Informatics, 2022
Federated Threat-Hunting Approach for Microservice-Based Industrial Cyber-Physical System.
IEEE Trans. Ind. Informatics, 2022
IEEE Trans. Fuzzy Syst., 2022
STLF-Net: Two-stream deep network for short-term load forecasting in residential buildings.
J. King Saud Univ. Comput. Inf. Sci., 2022
Interval type-2 fuzzy temporal convolutional autoencoder for gait-based human identification and authentication.
Inf. Sci., 2022
Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey.
Inf. Sci., 2022
H2HI-Net: A Dual-Branch Network for Recognizing Human-to-Human Interactions From Channel-State Information.
IEEE Internet Things J., 2022
Deep Learning for Heterogeneous Human Activity Recognition in Complex IoT Applications.
IEEE Internet Things J., 2022
Toward Privacy Preserving Federated Learning in Internet of Vehicular Things: Challenges and Future Directions.
IEEE Consumer Electron. Mag., 2022
2021
Deep-IFS: Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment.
IEEE Trans. Ind. Informatics, 2021
Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans.
Pattern Recognit. Lett., 2021
FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection.
Knowl. Based Syst., 2021
RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions.
Inf. Sci., 2021
Energy-Net: A Deep Learning Approach for Smart Energy Management in IoT-Based Smart Cities.
IEEE Internet Things J., 2021
Semi-Supervised Spatiotemporal Deep Learning for Intrusions Detection in IoT Networks.
IEEE Internet Things J., 2021
IEEE Internet Things J., 2021
2020
DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing.
IEEE Access, 2020