Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection
Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability
to explain the complex decision-making process of machine learning (ML) and deep …
to explain the complex decision-making process of machine learning (ML) and deep …
HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals
Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES
detection with high accuracy from electroencephalogram (EEG) signals. The early detection …
detection with high accuracy from electroencephalogram (EEG) signals. The early detection …
Sustainability-Driven Hourly Energy Demand Forecasting in Bangladesh Using Bi-LSTMs
This research presents a comprehensive study on develo** and evaluating a deep
learning-based forecasting model for hourly energy demand prediction in Bangladesh …
learning-based forecasting model for hourly energy demand prediction in Bangladesh …
Optimizing Medical Imaging Quality: An In-Depth Examination of Preprocessing Methods for Brain MRIs
Neurodegenerative diseases arise from the gradual deterioration of neuronal structure or
function and spans different levels of neuronal circuitry in the brain, ranging from molecular …
function and spans different levels of neuronal circuitry in the brain, ranging from molecular …
Understanding Feature Importance of Prediction Models Based on Lung Cancer Primary Care Data
Machine learning (ML) models in healthcare are increasing but the lack of interpretability of
these models results in them not being suitable for use in clinical practice. In the medical …
these models results in them not being suitable for use in clinical practice. In the medical …
Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer's and Parkinson's Disease Classification
The use of transfer learning in medical imaging has shown promising results in various
applications, including disease classification and segmentation. Early detection of …
applications, including disease classification and segmentation. Early detection of …
Classifying Depressed and Healthy Individuals Using Wearable Sensor Data: A Comparative Analysis of Classical Machine Learning Approaches
This paper presents a comprehensive study on classifying depressed and healthy
individuals using the Depresjon dataset, which contains motor activity data collected from …
individuals using the Depresjon dataset, which contains motor activity data collected from …
Performance Analysis of a Single-Input Thermal Image Classifier with Patient Information for the Detection of Breast Cancer
AS Cherian, MJ Mammoottil, LJ Kulangara… - … Conference on Applied …, 2023 - Springer
Breast cancer is counted among one of the most invasive cancers with a high mortality rate
among women. Early detection is essential as this nature of cancer can be life threatening …
among women. Early detection is essential as this nature of cancer can be life threatening …
A Media-Pipe Integrated Deep Learning Model for ISL (Alphabet) Recognition and Converting Text to Sound with Video Input
TMV Mukundan, A Gadhiya, K Nadar… - … Conference on Applied …, 2023 - Springer
The present study showcases a novel deep learning-based vision application tasked with
reducing the communication gap between sign language and non-sign language users …
reducing the communication gap between sign language and non-sign language users …
[PDF][PDF] Optimizing Medical Imaging Quality: An In-Depth Examination of Preprocessing Methods for Brain MRIs
Neurodegenerative diseases arise from the gradual deterioration of neuronal structure or
function and spans different levels of neuronal circuitry in the brain, ranging from molecular …
function and spans different levels of neuronal circuitry in the brain, ranging from molecular …