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Desta Haileselassie Hagos
Person information
- affiliation: Howard University, Department of Electrical Engineering and Computer Science, Washington, DC, USA
- affiliation (former): KTH Royal Institute of Technology, Department of Computer Science, Stockholm, Sweden
- affiliation (former, PhD 2020): University of Oslo, Department of Computer Science, Norway
- affiliation (former): University Graduate Center (UNIK), Oslo, Norway
- affiliation (former): Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Luleå, Sweden
2020 – today
- 2024
- [j11]Ashish Rauniyar
, Desta Haileselassie Hagos, Debesh Jha
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. IEEE Internet Things J. 11(5): 7374-7398 (2024) - [j10]Desta Haileselassie Hagos
Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives. IEEE Trans. Artif. Intell. 5(12): 5873-5893 (2024) - [j9]Desta Haileselassie Hagos
Neuro-Symbolic AI for Military Applications. IEEE Trans. Artif. Intell. 5(12): 6012-6026 (2024) - [c15]Earl Tankard, Desta Haileselassie Hagos, Danda B. Rawat:
Dynamic Model Selection for Asynchronous Federated Learning in IoBT Scenarios. IEEE Big Data 2024: 8025-8034 - [c14]Debesh Jha, Ashish Rauniyar, Desta Haileselassie Hagos, Vanshali Sharma, Nikhil Kumar Tomar, Zheyuan Zhang, Ilkin Isler, Gorkem Durak, Michael B. Wallace
Practical and Ethical Considerations for Generative AI in Medical Imaging. FAIMI/EPIMI@MICCAI 2024: 176-187 - [i8]Danda B. Rawat, Hassan El Alami, Desta Haileselassie Hagos:
Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectives. CoRR abs/2405.04718 (2024) - [i7]Desta Haileselassie Hagos, Rick Battle, Danda B. Rawat:
Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives. CoRR abs/2407.14962 (2024) - [i6]Desta Haileselassie Hagos, Danda B. Rawat:
Neuro-Symbolic AI for Military Applications. CoRR abs/2408.09224 (2024) - [i5]Desta Haileselassie Hagos, Hassan El Alami, Danda B. Rawat:
AI-Driven Human-Autonomy Teaming in Tactical Operations: Proposed Framework, Challenges, and Future Directions. CoRR abs/2411.09788 (2024) - 2023
- [c13]Desta Haileselassie Hagos, Earl Tankard, Manish Bhurtel
Optimal Asynchronous Federated Learning for the Internet of Battlefield Things (IoBT). ICMLA 2023: 1553-1559 - [c12]Desta Haileselassie Hagos, Earl Tankard, Danda B. Rawat:
A Scalable Asynchronous Federated Learning for Privacy-Preserving Real-Time Surveillance Systems. INFOCOM Workshops 2023: 1-6 - [i4]Debesh Jha, Ashish Rauniyar
Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles. CoRR abs/2304.11530 (2023) - 2022
- [j8]Desta Haileselassie Hagos
Scalable Artificial Intelligence for Earth Observation Data Using Hopsworks. Remote. Sens. 14(8): 1889 (2022) - [j7]Desta Haileselassie Hagos
Recent Advances in Artificial Intelligence and Tactical Autonomy: Current Status, Challenges, and Perspectives. Sensors 22(24): 9916 (2022) - [c11]Tianze Wang, Amir Hossein Payberah, Desta Haileselassie Hagos, Vladimir Vlassov:
Accelerate Model Parallel Deep Learning Training Using Effective Graph Traversal Order in Device Placement. DAIS 2022: 114-130 - [c10]Ashish Rauniyar
COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics. SAM 2022: 216-220 - [i3]Tianze Wang, Amir Hossein Payberah, Desta Haileselassie Hagos, Vladimir Vlassov:
Accelerate Model Parallel Training by Using Efficient Graph Traversal Order in Device Placement. CoRR abs/2201.09676 (2022) - [i2]Ashish Rauniyar
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. CoRR abs/2208.03392 (2022) - [i1]Ashish Rauniyar
COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics. CoRR abs/2208.04704 (2022) - 2021
- [j6]Desta Haileselassie Hagos
A Machine-Learning-Based Tool for Passive OS Fingerprinting With TCP Variant as a Novel Feature. IEEE Internet Things J. 8(5): 3534-3553 (2021) - [j5]Desta Haileselassie Hagos
ExtremeEarth Meets Satellite Data From Space. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 9038-9063 (2021) - 2020
- [b1]Desta Haileselassie Hagos:
Discovering the Dynamic Complexity of TCP Using Machine Learning and Deep Learning Techniques. University of Oslo, Norway, 2020 - [c9]Desta Haileselassie Hagos, Martin Løland, Anis Yazidi, Øivind Kure, Paal E. Engelstad:
Advanced Passive Operating System Fingerprinting Using Machine Learning and Deep Learning. ICCCN 2020: 1-11
2010 – 2019
- 2019
- [c8]Desta Haileselassie Hagos, Paal E. Engelstad, Anis Yazidi, Carsten Griwodz:
A Deep Learning Approach to Dynamic Passive RTT Prediction Model for TCP. IPCCC 2019: 1-10 - [c7]Desta Haileselassie Hagos, Paal E. Engelstad, Anis Yazidi:
Classification of Delay-based TCP Algorithms From Passive Traffic Measurements. NCA 2019: 1-10 - 2018
- [j4]Desta Haileselassie Hagos
General TCP State Inference Model From Passive Measurements Using Machine Learning Techniques. IEEE Access 6: 28372-28387 (2018) - [j3]Ashish Rauniyar
A Crowd-Based Intelligence Approach for Measurable Security, Privacy, and Dependability in Internet of Automated Vehicles with Vehicular Fog. Mob. Inf. Syst. 2018: 7905960:1-7905960:14 (2018) - [c6]Desta Haileselassie Hagos, Paal E. Engelstad, Anis Yazidi, Øivind Kure:
Towards a Robust and Scalable TCP Flavors Prediction Model from Passive Traffic. ICCCN 2018: 1-11 - [c5]Desta Haileselassie Hagos, Paal E. Engelstad, Anis Yazidi, Øivind Kure:
Recurrent Neural Network-Based Prediction of TCP Transmission States from Passive Measurements. NCA 2018: 1-10 - [c4]Desta Haileselassie Hagos, Paal E. Engelstad, Anis Yazidi, Øivind Kure:
A machine learning approach to TCP state monitoring from passive measurements. Wireless Days 2018: 164-171 - 2017
- [c3]Desta Haileselassie Hagos, Anis Yazidi, Øivind Kure, Paal E. Engelstad:
Enhancing Security Attacks Analysis Using Regularized Machine Learning Techniques. AINA 2017: 909-918 - 2016
- [j2]Desta Haileselassie Hagos:
Software-Defined Networking for Scalable Cloud-based Services to Improve System Performance of Hadoop-based Big Data Applications. Int. J. Grid High Perform. Comput. 8(2): 1-22 (2016) - [j1]Desta Haileselassie Hagos:
The performance of network-controlled mobile data offloading from LTE to WiFi networks. Telecommun. Syst. 61(4): 675-694 (2016) - 2013
- [c2]Samuel Idowu, Desta Haileselassie Hagos, Welderufael Berhane Tesfay, Abiola Famurewa, Juwel Rana, Kåre Synnes:
NexTrend: Context-Aware Music-Relay Corridors Using NFC Tags. IMIS 2013: 573-578 - [c1]Desta Haileselassie Hagos, Rüdiger Kapitza:
Study on performance-centric offload strategies for LTE networks. WMNC 2013: 1-10
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