[HTML][HTML] A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power

RA Rajagukguk, RAA Ramadhan, HJ Lee - Energies, 2020 - mdpi.com
Presently, deep learning models are an alternative solution for predicting solar energy
because of their accuracy. The present study reviews deep learning models for handling …

Weather forecasting for renewable energy system: a review

R Meenal, D Binu, KC Ramya, PA Michael… - … Methods in Engineering, 2022 - Springer
Energy crisis and climate change are the major concerns which has led to a significant
growth in the renewable energy resources which includes mainly the solar and wind power …

[HTML][HTML] Diabetes detection using deep learning algorithms

G Swapna, R Vinayakumar, KP Soman - ICT express, 2018 - Elsevier
Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence
rates are increasing alarmingly every year. If untreated, diabetes-related complications in …

[HTML][HTML] Deep LSTM model for diabetes prediction with class balancing by SMOTE

SA Alex, NZ Jhanjhi, M Humayun, AO Ibrahim… - Electronics, 2022 - mdpi.com
Diabetes is an acute disease that happens when the pancreas cannot produce enough
insulin. It can be fatal if undiagnosed and untreated. If diabetes is revealed early enough, it …

An optimization-based diabetes prediction model using CNN and Bi-directional LSTM in real-time environment

P Madan, V Singh, V Chaudhari, Y Albagory… - Applied Sciences, 2022 - mdpi.com
Featured Application Diabetes is a common chronic disorder defined by excessive glucose
levels in the blood. A good diagnosis of diabetes may make a person's life better; otherwise …

Deep convolutional neural networks with ensemble learning and transfer learning for automated detection of gastrointestinal diseases

Q Su, F Wang, D Chen, G Chen, C Li, L Wei - Computers in Biology and …, 2022 - Elsevier
Gastrointestinal (GI) diseases are serious health threats to human health, and the related
detection and treatment of gastrointestinal diseases place a huge burden on medical …

[HTML][HTML] Current techniques for diabetes prediction: review and case study

S Larabi-Marie-Sainte, L Aburahmah, R Almohaini… - Applied Sciences, 2019 - mdpi.com
Diabetes is one of the most common diseases worldwide. Many Machine Learning (ML)
techniques have been utilized in predicting diabetes in the last couple of years. The …

[HTML][HTML] Using recurrent neural networks for predicting type-2 diabetes from genomic and tabular data

PN Srinivasu, J Shafi, TB Krishna, CN Sujatha… - Diagnostics, 2022 - mdpi.com
The development of genomic technology for smart diagnosis and therapies for various
diseases has lately been the most demanding area for computer-aided diagnostic and …

A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare

TCT Chen, HC Wu, MC Chiu - Applied Soft Computing, 2024 - Elsevier
Artificial intelligence (AI) applications based on deep learning for diagnosing type-II diabetes
are sometimes difficult to understand and communicate even as patients are eager to …

A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

N Musa, AY Gital, N Aljojo, H Chiroma… - Journal of ambient …, 2023 - Springer
The success of deep learning over the traditional machine learning techniques in handling
artificial intelligence application tasks such as image processing, computer vision, object …