A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders DOI Creative Commons
Cosmina-Mihaela Roșca, Adrian Stancu

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(10), P. 5442 - 5442

Published: May 13, 2025

This research aims to explore the interdisciplinary connection between field of neurology and artificial intelligence (AI) through machine learning (ML) algorithms. The central objective is evaluate current state in Neuro-ML identify gaps literature that require additional approaches. To achieve this objective, 10 analyses were introduced analyze distribution articles based on keywords, countries, years, publishers, ML algorithms used context neurological diseases. Surveys also conducted diseases most frequently studied Thus, it was found Alzheimer’s disease (37 for Support Vector Regression—SVR; 31 Random Forest—RF), Parkinson’s (46 SVM 48 RF), multiple sclerosis (9 SVM) are Neuro-ML. study analyzes Alzheimer’s, Parkinson’s, detail by focusing diagnosis. overall results highlight an increase researchers’ interest applying neurology, with models such as (597 articles), Artificial Neural Network (525 RF (457 articles) being used. highlighted three major gaps: underrepresentation rare diseases, lack standardization evaluating performance models, exploration greater implementation difficulty, Extreme Gradient Boosting Multilayer Perceptron. value analysis metrics demonstrates ability correctly classify neuro-degenerative high accuracy some cases (for example, 97.46% diagnosis), but there may still be improvements. Future directions include exploring investigating underutilized algorithms, developing standardized protocols which will facilitate comparison across different studies.

Language: Английский

Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas DOI Creative Commons
Cosmina-Mihaela Roșca, Mădălina Cărbureanu, Adrian Stancu

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4390 - 4390

Published: April 16, 2025

Air quality (AQ) is one of the most important urban environment indicators for life. The paper proposes a software solution predicting and forecasting air index (AQI) in areas. study integrates pollutant factors (CO, NO2, SO2, PM2.5), meteorological parameters (temperature, humidity, wind speed), traffic data to determine quality. For this purpose, 19 predictive models were developed compared: 12 machine learning algorithms, 7 deep learning, 1 model based on structural component analysis. Random Forest Regression model, customized within study, achieved best results, with an R2 score 99.59%, MAE 0.22%, MAPE 0.68%, OP (Overall Precision) 95.61%. It was subsequently validated unseen recorded mean deviation 0.58%. short-term AQI (5 days), AQIF 71.62%, 0.4%, 0.9%. proposed integrated into web application IoT infrastructure real-time alert mechanisms. Future directions include expanding dataset optimizing hyperparameters increase accuracy, as well integrating PM10 O3 factors, along degree industrialization demographic level.

Language: Английский

Citations

0
A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders DOI Creative Commons
Cosmina-Mihaela Roșca, Adrian Stancu

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(10), P. 5442 - 5442

Published: May 13, 2025

This research aims to explore the interdisciplinary connection between field of neurology and artificial intelligence (AI) through machine learning (ML) algorithms. The central objective is evaluate current state in Neuro-ML identify gaps literature that require additional approaches. To achieve this objective, 10 analyses were introduced analyze distribution articles based on keywords, countries, years, publishers, ML algorithms used context neurological diseases. Surveys also conducted diseases most frequently studied Thus, it was found Alzheimer’s disease (37 for Support Vector Regression—SVR; 31 Random Forest—RF), Parkinson’s (46 SVM 48 RF), multiple sclerosis (9 SVM) are Neuro-ML. study analyzes Alzheimer’s, Parkinson’s, detail by focusing diagnosis. overall results highlight an increase researchers’ interest applying neurology, with models such as (597 articles), Artificial Neural Network (525 RF (457 articles) being used. highlighted three major gaps: underrepresentation rare diseases, lack standardization evaluating performance models, exploration greater implementation difficulty, Extreme Gradient Boosting Multilayer Perceptron. value analysis metrics demonstrates ability correctly classify neuro-degenerative high accuracy some cases (for example, 97.46% diagnosis), but there may still be improvements. Future directions include exploring investigating underutilized algorithms, developing standardized protocols which will facilitate comparison across different studies.

Language: Английский

Citations

0