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 …
Develo** 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 …
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 …
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 …
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 …
Hybrid archimedes sine cosine optimization enabled deep learning for multilevel brain tumor classification using mri images
The most terrible form of cancer caused by uncontrolled and aberrant cell division is the
Brain Tumor (BT). The current methodologies are insufficient for precise categorization due …
Brain Tumor (BT). The current methodologies are insufficient for precise categorization due …