In this article, we aim to design an architecture for privacy-preserved credit data and model sharing guarantee the secure storage of information in a distributed environment. The proposed optimizes privacy by instead revealing actual data. This article also proposes efficient mechanism combined with deletable Bloom filter uniform consensus training computation process. addition, propose authority control contract verification certification results under federated learning. Extensive...

Carbon emissions trading has become an increasingly hot topic nowadays, due to the fact that how reduce carbon been a common effort of different countries. However, traditional methods are plagued by issues, such as inadequate privacy protection mechanisms and challenge representing data assets in comprehensive form using blockchain models. In this article, we propose scheme (CETS), secure system combined with digital transactions. The proposed CETS enhances performance models for...

Abstract Vaccination is the most effective method of preventing and controlling transmission infectious diseases within populations. However, phenomenon waning immunity can induce periodic fluctuations in epidemic spreading. This study proposes a coupled epidemic‐vaccination dynamic model to analyze influence on spreading context voluntary vaccination. First, we establish an SIRSV (susceptible–infected–recovered–susceptible–vaccinated) compartment describe mechanism based mean‐field theory....

Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency reduce the danger transport accidents. However, data measured during actual maritime navigation is limited, values have large variability. Big collected in real time by automatic identification systems (AIS) might provide way to estimate depths, although these include no direct channel information. We suggest deep neural network (DNN) based model, called DDTree, for using real-time AIS from Global...

In medical risk prediction, such as predicting heart disease, machine learning (ML) classifiers must achieve high accuracy, precision, and recall to minimize the chances of incorrect diagnoses or treatment recommendations. However, real-world datasets often have imbalanced data, which can affect classifier performance. Traditional data balancing methods lead overfitting underfitting, making it difficult identify potential health risks accurately. Early prediction attacks is paramount...

Information retrieval is applied widely to models and algorithms in wireless networks for cyber-physical systems. Query terms proximity has proved that it a very useful information improve the performance of cannot retrieve documents independently, must be incorporated into original models. This article proposes concept query term embedding, which new method incorporate Moreover, term-field-convolutions frequency framework, an implementation proposed this article, experimental results show...

Earthquake prediction, which is a key issue that has long existed among seismologists, of high scientific importance. An earthquake prediction model can output the time occurrence in advance using machine learning methods, receiving increasing attention. involves large variety data mining steps, requires amount for processing and development. Thus, an efficient accurate method needed. Aiming to solve this problem, we propose Auto-REP, automated learning-based regression model. Our...

This thesis provides an in-depth exploration of the Decentralized Co-governance Crowdfunding (DCC) Ecosystem, a novel solution addressing prevailing challenges in conventional crowdfunding methods faced by MSMEs and innovative projects. Among problems it seeks to mitigate are high transaction costs, lack transparency, fraud, inefficient resource allocation. Leveraging comprehensive review existing literature on economic activities blockchain's impact organizational governance, we propose...

This paper proposes a term field based co-occurrence model, which consults the concept of in physics, interpreting how to get functional relations terms' correlation and their distance. The result experiments illuminates that this model is not "descending model" simply there are two segments change trend terms evidence, interprets phenomenon from linguistics practice almost constant small

The paper brings forth a semantic search engine framework based on ontology, the technology overcomes traditional engine's shortcomings such as poor processing capability and understanding because of adoption text retrieval greatly lifts efficiency.

This paper proposes a term field based co-occurrence model, which consults the concept of in physics, interpreting how to get functional relations terms' correlation and their distance. The result experiments illuminates that this model is not "descending model" simply there are two segments change trend terms evidence, interprets phenomenon from linguistics practice almost constant small