Machine learning-and statistical-based voice analysis of Parkinson's disease patients: A survey
The preliminary diagnosis and evaluation of the presence and/or severity of Parkinson's
disease is crucial in controlling the progress of the disease. Real-time, non-invasive …
disease is crucial in controlling the progress of the disease. Real-time, non-invasive …
Optimal Machine Learning-and Deep Learning-driven algorithms for predicting the future value of investments: A systematic review and meta-analysis
L Parisi, ML Manaog - Engineering Applications of Artificial Intelligence, 2025 - Elsevier
The COVID-19 pandemic and the increasing competitive landscape have led asset
management companies to consider investing in applying Artificial Intelligence (AI)-driven …
management companies to consider investing in applying Artificial Intelligence (AI)-driven …
[HTML][HTML] hyper-sinh: An accurate and reliable function from shallow to deep learning in TensorFlow and Keras
This paper presents the 'hyper-sinh', a variation of the m-arcsinh activation function suit-able
for Deep Learning (DL)-based algorithms for supervised learning, including Convolutional …
for Deep Learning (DL)-based algorithms for supervised learning, including Convolutional …
Real-Time neural classifiers for sensor faults in three phase induction motors
Induction motors can be modeled in different ways for correct operation and control, one of
these is the-representation, this model has six state variables that can be monitored: rotor …
these is the-representation, this model has six state variables that can be monitored: rotor …
Real-time neural classifiers for sensor and actuator faults in three-phase induction motors
The main steps involved in a fault-tolerant control (FTC) scheme are the detection of failures,
isolation and reconfiguration of control. Fault detection and isolation (FDI) is a topic of …
isolation and reconfiguration of control. Fault detection and isolation (FDI) is a topic of …
Speech features-based Parkinson's disease classification using combined SMOTE-ENN and binary machine learning
Purpose Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases
in the global context. The presently available detecting process of PD is costly and labour …
in the global context. The presently available detecting process of PD is costly and labour …
Neuroevolutionary intelligent system to aid diagnosis of motor impairments in children
An early detection of motor impairments in children is essential to improve self-care.
Nevertheless, it may not be straightforward to conduct all required assessments physically in …
Nevertheless, it may not be straightforward to conduct all required assessments physically in …
Syncretic feature selection for machine learning-aided prognostics of hepatitis
Despite recent advances in Machine Learning (ML)-based applications for clinical decision
making, there is no objective method that can assist physicians in discriminating clinically …
making, there is no objective method that can assist physicians in discriminating clinically …
ParkinSense: A Tri-Sensory AI-Powered Framework for Early Detection of Parkinson's Disease
M Akshaya, E Joy - International Conference on Information …, 2024 - Springer
Parkinson's disease (PD) is a brain dysfunction condition that affects thousands of people
across the globe. Early detection of Parkinson's disease is crucial for effective treatment and …
across the globe. Early detection of Parkinson's disease is crucial for effective treatment and …
A Comprehensive Voice Data Analysis for Parkinson's Disease Prediction via Machine Learning Techniques
This work aims to develop a Machine Learning-based system to classify patients with
Parkinson's disease. The existing systems have limitations, including limited generalizability …
Parkinson's disease. The existing systems have limitations, including limited generalizability …