Multi-Disease Prediction Based on Deep Learning: A Survey

In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in themedical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Amongthem, the hot deep learning field has shown greater potential in applications such as disease prediction and drugresponse prediction. From the initial logistic regression model to the machine learning model, and then to thedeep learning model today, the accuracy of medical disease prediction has been continuously improved, and theperformance in all aspects has also been significantly improved. This article introduces some basic deep learningframeworks and some common diseases, and summarizes the deep learning prediction methods correspondingto different diseases. Point out a series of problems in the current disease prediction, and make a prospect for thefuture development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates thehigh correlation between deep learning and the medical field in future development. The unique feature extractionmethods of deep learning methods can still play an important role in future medical research.

Prototypical Network Based on Manhattan Distance

Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data,such as medical images,terrorist surveillance,and so on.The Metric Learning in the Few-shot Learning algorithmis classified by measuring the similarity between the classified samples and the unclassified samples.This paper improves the Prototypical Network in the Metric Learning,and changes its core metric function to Manhattan distance.The Convolutional Neural Network of the embedded module is changed,and mechanisms such as average pooling and Dropout are added.Through comparative experiments,it is found that thismodel can converge in a small number of iterations(below 15,000 episodes),and its performance exceeds algorithms such asMAML.Research shows that replacingManhattan distance with Euclidean distance can effectively improve the classification effect of the Prototypical Network,and mechanisms such as average pooling and Dropout can also effectively improve the model.

Building the metaverse using digital twins at all scales,states,and relations

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the Metaverse.Metaverse objects can be at the micro-,meso-,or macroscale.The Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain states.Additionally,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social relations.Therefore,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

Artificial intelligence in the digital twins:State of the art,challenges,and future research topics[version 1;peer review:1 approved,1 approved with reservations]

Advanced computer technologies such as big data,Artificial Intelligence(AI),cloud computing,digital twins,and edge computing have been applied in various fields as digitalization has progressed.To study the status of the application of digital twins in the combination with AI,this paper classifies the applications and prospects of AI in digital twins by studying the research results of the current published literature.We discuss the application status of digital twins in the four areas of aerospace,intelligent manufacturing in production workshops,unmanned vehicles,and smart city transportation,and we review the current challenges and topics that need to be looked forward to in the future.It was found that the integration of digital twins and AI has significant effects in aerospace flight detection simulation,failure warning,aircraft assembly,and even unmanned flight.In the virtual simulation test of automobile autonomous driving,it can save 80%of the time and cost,and the same road conditions reduce the parameter scale of the actual vehicle dynamics model and greatly improve the test accuracy.In the intelligent manufacturing of production workshops,the establishment of a virtual workplace environment can provide timely fault warning,extend the service life of the equipment,and ensure the overall workshop operational safety.In smart city traffic,the real road environment is simulated,and traffic accidents are restored,so that the traffic situation is clear and efficient,and urban traffic management can be carried out quickly and accurately.Finally,we looked forward to the future of digital twins and AI,hoping to provide a reference for future research in related fields.