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Article
BC-Net: Early Diagnostics of Breast Cancer Using Nested Ensemble Technique of Machine Learning
Breast cancer is a divergent and prominent cancer that is responsible for the morbidity and mortality of women throughout the world. This paper aims at early detection and accurate diagnosis of this fatal dise...
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Article
Black fungus immunosuppressive epidemic with Covid-19 associated mucormycosis (zygomycosis): a clinical and diagnostic perspective from India
The catastrophic phase of Covid-19 turns the table over with the spread of its disastrous transmission network throughout the world. Covid-19 associated with mucormycosis fungal infection accompanied by opport...
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Article
NestEn_SmVn: boosted nested ensemble multiplexing to diagnose coronary artery disease
Coronary artery disease (CAD) is the most prominent disease that is responsible for increasing mortality and morbidity rate from past few decades. Early and accurate detection of CAD (a type of cardiovascular ...
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Article
CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19
The pandemic of novel coronavirus disease 2019 (Covid-19) has left the world to a standstill by creating a calamitous situation. To mitigate this devastating effect the inception of artificial intelligence int...
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Article
CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks
Many countries around the world have been influenced by Covid-19 which is a serious virus as it gets transmitted by human communication. Although, its syndrome is quite similar to the ordinary flu. The critica...
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Article
Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic
The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new em...
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Article
GBoost: A novel Grading-AdaBoost ensemble approach for automatic identification of erythemato-squamous disease
Ensemble learning is one of the powerful machine learning approaches that is generally used to strengthen models by combining the performances of several weak learners. It holds a great potential for solving u...
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Article
A nested stacking ensemble model for predicting districts with high and low maternal mortality ratio (MMR) in India
The ensemble is an efficacious machine learning framework that combines variety of algorithms for better performance and effective prediction. Over the past few years, numerous researchers proposed wide variet...
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Chapter and Conference Paper
KDD-Based Decision Making: A Conceptual Framework Model for Maternal Health and Child Immunization Databases
This paper focuses on the issues apposite to the use of maternal health and child immunization data and throws light on how the KDD (Knowledge Discovery in Databases) process makes use of maternal health and c...