Luca Parisi

Orcid: 0000-0002-5865-8708

According to our database1, Luca Parisi authored at least 15 papers between 2016 and 2023.

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

Timeline

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Links

On csauthors.net:

Bibliography

2023
Innovative feature-driven machine learning and deep learning for finance, education, and healthcare.
Neural Comput. Appl., June, 2023

Holistic similarity-based prediction of phosphorylation sites for understudied kinases.
Briefings Bioinform., March, 2023

2022
Syncretic Feature Selection for Machine Learning-Aided Prognostics of Hepatitis.
Neural Process. Lett., 2022

Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson's disease and COVID-19.
Expert Syst. Appl., 2022

Neuroevolutionary intelligent system to aid diagnosis of motor impairments in children.
Appl. Intell., 2022

2021
M-ar-K-Fast Independent Component Analysis.
CoRR, 2021

2020
Evolutionary Denoising-Based Machine Learning for Detecting Knee Disorders.
Neural Process. Lett., 2020

A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis.
Neural Comput. Appl., 2020

Evolutionary feature transformation to improve prognostic prediction of hepatitis.
Knowl. Based Syst., 2020

hyper-sinh: An Accurate and Reliable Function from Shallow to Deep Learning in TensorFlow and Keras.
CoRR, 2020

QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics.
CoRR, 2020

m-arcsinh: An Efficient and Reliable Function for SVM and MLP in scikit-learn.
CoRR, 2020

2018
Decision support system to improve postoperative discharge: A novel multi-class classification approach.
Knowl. Based Syst., 2018

Feature-driven machine learning to improve early diagnosis of Parkinson's disease.
Expert Syst. Appl., 2018

2016
Towards a Software Product Line for Machine Learning Workflows: Focus on Supporting Evolution.
Proceedings of the 10th Workshop on Models and Evolution co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), 2016


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