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ORCIDDesta 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
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2020 – today
- 2024
[j9]Ashish Rauniyar
, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård, Ulas Bagci, Danda B. Rawat, Vladimir Vlassov:
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. IEEE Internet Things J. 11(5): 7374-7398 (2024)- [c14]Debesh Jha, Ashish Rauniyar, Desta Haileselassie Hagos, Vanshali Sharma, Nikhil Kumar Tomar, Zheyuan Zhang, Ilkin Isler, Gorkem Durak, Michael B. Wallace, Cemal Yazici, Tyler M. Berzin, Koushik Biswas, Ulas Bagci:
Practical and Ethical Considerations for Generative AI in Medical Imaging. FAIMI/EPIMI@MICCAI 2024: 176-187 - [i7]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) - [i6]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) - [i5]Desta Haileselassie Hagos, Danda B. Rawat:
Neuro-Symbolic AI for Military Applications. CoRR abs/2408.09224 (2024) - 2023
- [c13]Desta Haileselassie Hagos, Earl Tankard, Manish Bhurtel, Danda B. Rawat:
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, Abhishek Srivastava, Desta Haileselassie Hagos, Nikhil Kumar Tomar, Vanshali Sharma, Elif Keles, Zheyuan Zhang, Ugur Demir, Ahmet Topcu, Anis Yazidi, Jan Erik Håkegård, Ulas Bagci:
Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles. CoRR abs/2304.11530 (2023) - 2022
- [j8]Desta Haileselassie Hagos, Theofilos Kakantousis, Sina Sheikholeslami, Tianze Wang, Vladimir Vlassov, Amir Hossein Payberah, Moritz Meister, Robin Andersson, Jim Dowling:
Scalable Artificial Intelligence for Earth Observation Data Using Hopsworks. Remote. Sens. 14(8): 1889 (2022) - [j7]Desta Haileselassie Hagos, Danda B. Rawat:
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, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård:
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, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård, Ulas Bagci, Danda B. Rawat, Vladimir Vlassov:
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. CoRR abs/2208.03392 (2022) - [i1]Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård:
COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics. CoRR abs/2208.04704 (2022) - 2021
- [j6]Desta Haileselassie Hagos, Anis Yazidi, Øivind Kure, Paal E. Engelstad:
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, Theofilos Kakantousis, Vladimir Vlassov, Sina Sheikholeslami, Tianze Wang, Jim Dowling, Claudia Paris, Daniele Marinelli, Giulio Weikmann, Lorenzo Bruzzone, Salman Khaleghian, Thomas Krämer, Torbjørn Eltoft, Andrea Marinoni, Despina-Athanasia Pantazi, George Stamoulis, Dimitris Bilidas, George Papadakis, Georgios M. Mandilaras, Manolis Koubarakis, Antonis Troumpoukis, Stasinos Konstantopoulos, Markus Muerth, Florian Appel, Andrew Fleming, Andreas Cziferszky:
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, Paal E. Engelstad, Anis Yazidi, Øivind Kure:
General TCP State Inference Model From Passive Measurements Using Machine Learning Techniques. IEEE Access 6: 28372-28387 (2018) - [j3]Ashish Rauniyar, Desta Haileselassie Hagos, Manish Shrestha:
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
Coauthor Index
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last updated on 2024-11-04 20:40 CET by the dblp team
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