An improved framework for detecting thyroid disease using filter-based feature selection and stacking ensemble
In recent years, machine learning (ML) has become a pivotal tool for predicting and
diagnosing thyroid disease. While many studies have explored the use of individual ML …
diagnosing thyroid disease. While many studies have explored the use of individual ML …
Aedes Larva Detection Using Ensemble Learning to Prevent Dengue Endemic
Dengue endemicity has become regular in recent times across the world. The numbers of
cases and deaths have been alarmingly increasing over the years. In addition to this, there …
cases and deaths have been alarmingly increasing over the years. In addition to this, there …
[HTML][HTML] Evaluation of machine learning algorithms for classification of EEG signals
In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …
Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass
Rice biomass is a biofuel's source and yield indicator. Conventional sampling methods
predict rice biomass accurately. However, these methods are destructive, time-consuming …
predict rice biomass accurately. However, these methods are destructive, time-consuming …
Forecasting and meta-features estimation of wastewater and climate change impacts in coastal region using manifold learning
South Asia's coastlines are the most densely inhabited and economically active ecosystems
have already begun to shift due to climate change. Over the past century, climate change …
have already begun to shift due to climate change. Over the past century, climate change …
Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting
Ensemble learning aims to improve prediction performance by combining several models or
forecasts. However, how much and which ensemble learning techniques are useful in deep …
forecasts. However, how much and which ensemble learning techniques are useful in deep …
Machine learning algorithms for the retrieval of canopy chlorophyll content and leaf area index of crops using the PROSAIL-D model with the adjusted average leaf …
The canopy chlorophyll content (CCC) and leaf area index (LAI) are both essential
indicators for crop growth monitoring and yield estimation. The PROSAIL model, which …
indicators for crop growth monitoring and yield estimation. The PROSAIL model, which …
Development of a robust parallel and multi-composite machine learning model for improved diagnosis of Alzheimer's disease: correlation with dementia-associated …
Introduction Machine learning (ML) algorithms and statistical modeling offer a potential
solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging …
solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging …
[PDF][PDF] Improved vision-based diagnosis of multi-plant disease using an ensemble of deep learning methods
Farming and plants are crucial parts of the inward economy of a nation, which significantly
boosts the economic growth of a country. Preserving plants from several disease infections …
boosts the economic growth of a country. Preserving plants from several disease infections …
Stacked Ensemble Deep Learning for Outdoor Insulator Surface Condition Classification: A Profound Study on Water Droplets
Insulators are vital protection and isolation barriers used in power transmission systems. To
prevent unexpected failures caused by severe weather conditions, it is important to develop …
prevent unexpected failures caused by severe weather conditions, it is important to develop …