Detection of Covid-19 using AI application
INTRODUCTION: In December of 2019, the infection which caused the pandemic started in
the Hubei territory of Wuhan, China. They were identified as SARS-CoV-2, a highly …
the Hubei territory of Wuhan, China. They were identified as SARS-CoV-2, a highly …
Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization …
Today, diagnostic systems based on artificial intelligence play a significant role in confirming
doctors' recommendations. These systems are becoming effective tools in clinical treatment …
doctors' recommendations. These systems are becoming effective tools in clinical treatment …
Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy
This study presents a novel dynamic localisation-based decision (DLBD) with fuzzy
weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic …
weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic …
Smart home management system with face recognition based on ArcFace model in deep convolutional neural network
TV Dang - Journal of Robotics and Control (JRC), 2022 - journal.umy.ac.id
In recent years, artificial intelligence has proved its potential in many fields, especially in
computer vision. Facial recognition is one of the most essential tasks in the field of computer …
computer vision. Facial recognition is one of the most essential tasks in the field of computer …
MD-DCNN: Multi-Scale Dilation-Based Deep Convolution Neural Network for epilepsy detection using electroencephalogram signals
Approximately 65 million individuals experience epilepsy globally. Surgery or medication
cannot cure more than 30% of epilepsy patients. However, through therapeutic intervention …
cannot cure more than 30% of epilepsy patients. However, through therapeutic intervention …
Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation
Z Wang, D Zhao, AA Heidari, Y Chen, H Chen… - Scientific Reports, 2024 - nature.com
Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-
threshold image segmentation methods are favored for their computational simplicity and …
threshold image segmentation methods are favored for their computational simplicity and …
Multivariate time series short term forecasting using cumulative data of coronavirus
Coronavirus emerged as a highly contagious, pathogenic virus that severely affects the
respiratory system of humans. The epidemic-related data is collected regularly, which …
respiratory system of humans. The epidemic-related data is collected regularly, which …
Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification
Accurate classification of logos is a challenging task in image recognition due to variations
in logo size, orientation, and background complexity. Deep learning models, such as …
in logo size, orientation, and background complexity. Deep learning models, such as …
Enhancing anomaly detection Efficiency: Introducing grid searchbased multi-population particle Swarm optimization algorithm based optimized Regional based …
Anomaly detection is critically important for enhancing data security across networks,
industrial applications, and fraud detection systems. Traditional methods in anomaly …
industrial applications, and fraud detection systems. Traditional methods in anomaly …
A turning point few-shot learning for COVID-19 diagnosis
L Qain, Y Bouteraa, T Vaiyapuri, Y Haung - Engineering Applications of …, 2024 - Elsevier
The current landscape of medical diagnostics grapples with a critical challenge posed by the
limitations of existing meta-learning techniques in interpreting complex representations from …
limitations of existing meta-learning techniques in interpreting complex representations from …