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Found 7 papers, 1 papers with code
Current and future roles of artificial intelligence in retinopathy of prematurity
Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness.
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
This paper provides an overview of the recent developments and difficulties in using artificial intelligence and retinal imaging to diagnose cardiovascular diseases.
Solving the multiplication problem of a large language model system using a graph-based method
The generative pre-trained transformer (GPT)-based chatbot software ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems, especially multiplication.
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Machine learning tasks can be divided into regression and classification.
The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms
In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication(M2M), Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network.
Application of artificial intelligence techniques for automated detection of myocardial infarction: A review
Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals.
Arrhythmia Detection Using Deep Convolutional Neural Network With Long Duration ECG Signals
The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias.
