Milad Salem

Orcid: 0000-0002-6703-6839

According to our database1, Milad Salem authored at least 14 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Functional microRNA-targeting drug discovery by graph-based deep learning.
Patterns, January, 2024

2023
ProtEC: A Transformer Based Deep Learning System for Accurate Annotation of Enzyme Commission Numbers.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023

2022
AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning.
BMC Bioinform., December, 2022

MolData, a molecular benchmark for disease and target based machine learning.
J. Cheminformatics, 2022

2021
A Universal Automated Data-Driven Modeling Framework for Truck Traffic Volume Prediction.
IEEE Access, 2021

2020
Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.
Frontiers Artif. Intell., 2020

Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks.
Big Data Cogn. Comput., 2020

TranScreen: Transfer Learning on Graph-Based Anti-Cancer Virtual Screening Model.
Big Data Cogn. Comput., 2020

2019
Utilizing Transfer Learning and Homomorphic Encryption in a Privacy Preserving and Secure Biometric Recognition System.
Comput., 2019

RazorNet: Adversarial Training and Noise Training on a Deep Neural Network Fooled by a Shallow Neural Network.
Big Data Cogn. Comput., 2019

2018
Leveraging Image Representation of Network Traffic Data and Transfer Learning in Botnet Detection.
Big Data Cogn. Comput., 2018

An Experimental Evaluation of Fault Diagnosis from Imbalanced and Incomplete Data for Smart Semiconductor Manufacturing.
Big Data Cogn. Comput., 2018

Anomaly Generation Using Generative Adversarial Networks in Host-Based Intrusion Detection.
Proceedings of the 9th IEEE Annual Ubiquitous Computing, 2018

ECG Arrhythmia Classification Using Transfer Learning from 2- Dimensional Deep CNN Features.
Proceedings of the 2018 IEEE Biomedical Circuits and Systems Conference, 2018


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