Artificial intelligence and neuroscience: An update on fascinating relationships
N Gopinath - Process Biochemistry, 2023 - Elsevier
Innovative technologies such as Artificial Intelligence (AI), deep learning, Machine learning
and optogenetics have been considered key components in the contribution to the …
and optogenetics have been considered key components in the contribution to the …
Monkeypox diagnosis with interpretable deep learning
As the world gradually recovers from the impacts of COVID-19, the recent global spread of
Monkeypox disease has raised concerns about another potential pandemic, highlighting the …
Monkeypox disease has raised concerns about another potential pandemic, highlighting the …
[HTML][HTML] Enhancing lung abnormalities detection and classification using a Deep Convolutional Neural Network and GRU with explainable AI: A promising approach …
Accurate and timely detection and classification of lung abnormalities are crucial for effective
diagnosis and treatment planning. In recent years, Deep Learning (DL) techniques have …
diagnosis and treatment planning. In recent years, Deep Learning (DL) techniques have …
A novel approach for best parameters selection and feature engineering to analyze and detect diabetes: Machine learning insights
Humans are familiar with “diabetes,” a chronic metabolic disease that causes resistance to
insulin in the human body, and about 425 million cases worldwide. Diabetes is a hazard to …
insulin in the human body, and about 425 million cases worldwide. Diabetes is a hazard to …
Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI
The research describes an effective deep learning-based, data-centric approach for
diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD …
diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD …
[HTML][HTML] Enhancing Atrial Fibrillation detection accuracy: A wavelet transform filtered single lead ECG signal analysis with artificial neural networks and novel feature …
Atrial Fibrillation (AF) is a common cardiac arrhythmia that can lead to fatal outcomes.
Detecting AF early is crucial for improving patient outcomes and reducing complications …
Detecting AF early is crucial for improving patient outcomes and reducing complications …
An Enhanced Technique of COVID‐19 Detection and Classification Using Deep Convolutional Neural Network from Chest X‐Ray and CT Images
Background. Coronavirus disease (COVID‐19) is an infectious illness that spreads widely
over a short period of time and finally causes a pandemic. Unfortunately, the lack of …
over a short period of time and finally causes a pandemic. Unfortunately, the lack of …
Enhancing lung abnormalities diagnosis using hybrid DCNN-ViT-GRU model with explainable AI: A deep learning approach
In this study, we propose a novel approach called DCNN-ViT-GRU, which combines deep
Convolutional Neural Networks (CNNs) with Gated Recurrent Units (GRUs) and the Vision …
Convolutional Neural Networks (CNNs) with Gated Recurrent Units (GRUs) and the Vision …
[HTML][HTML] Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI
Cardiovascular disease (CVD) is a leading cause of death worldwide, with millions dying
each year. The identification and early diagnosis of CVD are critical in preventing adverse …
each year. The identification and early diagnosis of CVD are critical in preventing adverse …
Explainable AI-based Alzheimer's prediction and management using multimodal data
Background According to the World Health Organization (WHO), dementia is the seventh
leading reason of death among all illnesses and one of the leading causes of disability …
leading reason of death among all illnesses and one of the leading causes of disability …