Frictional Active Brownian Particles

Frictional forces affect the rheology of hard-sphere colloids, at high shear rate. Here we demonstrate, via numerical simulations, that they also affect the dynamics of active Brownian particles, and their motility induced phase separation. Frictional forces increase the angular diffusivity of the particles, in the dilute phase, and prevent colliding particles from resolving their collision by sliding one past to the other. This leads to qualitatively changes of motility-induced phase diagram in the volume-fraction motility plane. While frictionless systems become unstable towards phase separation as the motility increases only if their volume fraction overcomes a threshold, frictional system become unstable regardless of their volume fraction. These results suggest the possibility of controlling the motility induced phase diagram by tuning the roughness of the particles

Application of deep reinforcement learning in stock trading strategies and stock forecasting

The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of deep reinforcement learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of deep reinforcement learning in financial markets and the credibility and advantages of strategic decision-making

Recommendation and Sentiment Analysis Based on Consumer Review and Rating

Accurate analysis and recommendation on products based on online reviews and rating data play an important role in precisely targeting suitable consumer segmentations and therefore can promote merchandise sales. This study uses a recommendation and sentiment classification model for analyzing the data of beer product based on online beer reviews and rating dataset of beer products and uses them to improve the recommendation performance of the recommendation model for different customer needs. Among them, the beer recommendation is based on rating data; 10 classification models are compared in text sentiment analysis, including the conventional machine learning models and deep learning models. Combining the two analyses can increase the credibility of the recommended beer and help increase beer sales. The experiment proves that this method can filter the products with more negative reviews in the recommendation algorithm and improve user acceptance

Recommendation and Sentiment Analysis Based on Consumer Review and Rating

Accurate analysis and recommendation on products based on online reviews and rating data play an important role in precisely targeting suitable consumer segmentations and therefore can promote merchandise sales. This study uses a recommendation and sentiment classification model for analyzing the data of beer product based on online beer reviews and rating dataset of beer products and uses them to improve the recommendation performance of the recommendation model for different customer needs. Among them, the beer recommendation is based on rating data; 10 classification models are compared in text sentiment analysis, including the conventional machine learning models and deep learning models. Combining the two analyses can increase the credibility of the recommended beer and help increase beer sales. The experiment proves that this method can filter the products with more negative reviews in the recommendation algorithm and improve user acceptance

Correction to: Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

Correction to: Information Systems Frontiers (https://doi.org/10.1007/s10796-022-10295-0)

Effective Piecewise CNN with attention mechanism for distant supervision on relation extraction task

Relation Extraction is an important sub-task in the field of information extraction. Its goal is to identify entities from text and extract semantic relationships between entities. However, the current Relationship Extraction task based on deep learning methods generally have practical problems such as insufficient amount of manually labeled data, so training under weak supervision has become a big challenge. Distant Supervision is a novel idea that can automatically annotate a large number of unlabeled data based on a small amount of labeled data. Based on this idea, this paper proposes a method combining the Piecewise Convolutional Neural Networks and Attention mechanism for automatically annotating the data of Relation Extraction task. The experiments proved that the proposed method achieved the highest precision is 76.24% on NYT-FB (New York Times-Freebase) dataset (top 100 relation categories). The results show that the proposed method performed better than CNN-based models in most cases

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

Cyber-Physical Systems (CPS), as a multi-dimensional complex system that connects the physical world and the cyber world, has a strong demand for processing large amounts of heterogeneous data. These tasks also include Natural Language Inference (NLI) tasks based on text from different sources. However, the current research on natural language processing in CPS does not involve exploration in this field. Therefore, this study proposes a Siamese Network structure that combines Stacked Residual Long Short-Term Memory (bidirectional) with the Attention mechanism and Capsule Network for the NLI module in CPS, which is used to infer the relationship between text/language data from different sources. This model is mainly used to implement NLI tasks and conduct a detailed evaluation in three main NLI benchmarks as the basic semantic understanding module in CPS. Comparative experiments prove that the proposed method achieves competitive performance, has a certain generalization ability, and can balance the performance and the number of trained parameters

Credit Risk Scoring Analysis Based on Machine Learning Models

In the big data era, institutions can easily access a massive number of data describing different aspects of a user. Therefore, credit scoring models are now building from both the past credit records of the applicant, and other personal information including working years and characteristics of owned properties. A wide variety of usable information has required models to extract more expressive features from data and apply the effective models to fit the features. This paper reports our efforts in using feature engineering techniques and machine learning models for credit scoring modeling. Based on the Kaggle Home Credit Default Risk dataset, several current feature engineering techniques and machine learning models have been tested and compared in terms of the AUC score. The results have shown that the LightGBM model training on expert knowledge generated datasets can achieve the best result (About 78% AUC score)

Inter-Personal Relation Extraction Model Based on Bidirectional GRU and Attention Mechanism

Inter-Personal Relationship Extraction is an important part of knowledge extraction and is also the fundamental work of constructing the knowledge graph of people's relationships. Compared with the traditional pattern recognition methods, the deep learning methods are more prominent in the relation extraction (RE) tasks. At present, the research of Chinese relation extraction technology is mainly based on the method of kernel function and Distant Supervision. In this paper, we propose a Chinese relation extraction model based on Bidirectional GRU network and Attention mechanism. Combining with the structural characteristics of the Chinese language, the input vector is input in the form of word vectors. Aiming at the problem of context memory, a Bidirectional GRU neural network is used to fuse the input vectors. The feature information of the word level is extracted from a sentence, and the sentence feature is extracted through the Attention mechanism of the word level. To verify the feasibility of this method, we use the distant supervision method to extract data from websites and compare it with existing relationship extraction methods. The experimental results show that Bi-directional GRU with Attention mechanism model can make full use of all the feature information of sentences, and the accuracy of Bi-directional GRU model is significantly higher than that of other neural network models without Attention mechanism