Mikhail Y. Shalaginov

Affiliations:
  • Massachusetts Institute of Technology (MIT), Department of Materials Science & Engineering, Cambridge, MA, USA


According to our database1, Mikhail Y. Shalaginov authored at least 14 papers between 2016 and 2023.

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

Timeline

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Bibliography

2023
Injury Risk Prediction in Soccer Using Machine Learning.
Proceedings of the International Conference on Machine Learning and Applications, 2023

Investigation of Racial Bias in Property Crime Prediction by Machine Learning Models.
Proceedings of the International Conference on Machine Learning and Applications, 2023

2022
Class Activation Mapping Enhanced AlexNet Convolutional Neural Networks for Early Diagnosis of Alzheimer's Disease.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

A Machine-Learning Approach for Predicting Depression Through Demographic and Socioeconomic Features.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

A Deep Convolutional Neural Network For Diagnosis of Diabetic Retinopathy.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

COVID-19 Impact on Mental Health Analysis based on Reddit Comments.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

Real-time Detection of Acute Lymphoblastic Leukemia Cells Using Deep Learning.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

Scoliosis Detection with Convolutional Neural Networks.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

2021
Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces.
CoRR, 2021

Comparison of Media Sources for COVID-19 by Machine Learning Sentiment Analysis.
Proceedings of the International Symposium on Networks, Computers and Communications, 2021

2020
A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks.
CoRR, 2020

2019
Generative Multi-Functional Meta-Atom and Metasurface Design Networks.
CoRR, 2019

A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks.
CoRR, 2019

2016
Subwavelength optics with hyperbolic metamaterials: Waveguides, scattering, and optical topological transitions.
Proceedings of the 18th International Conference on Transparent Optical Networks, 2016


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