Novel logic mining incorporating log linear approach

Electronic word-of-mouth data on social media influences stock trading and the confidence of stock markets. Thus, sentiment analysis of comments related to stock markets becomes crucial in forecasting stock markets. However, current sentiment analysis is mainly in English. Therefore, this study performs multilingual sentiment analysis by translating non-native English-speaking countries’ texts into English. This study used unstructured data from social media and structured data, including trading data and technical indicators, to forecast stock markets. Deep learning techniques and machine learning models have emerged as powerful ways of coping with forecasting problems, and parameter determination greatly influences forecasting models’ performance. This study used Long Short-Term Memory (LSTM) models employing the genetic algorithm (GA) to select parameters for predicting stock market indices and prices of company stocks by hybrid data in non-native English-speaking regions. Numerical results revealed that the developed LSTMGA model with hybrid multilingual sentiment data generates more accurate forecasting than the other machine learning models with various data types. Thus, the proposed LSTMGA model with hybrid multilingual sentiment analysis is a feasible and promising way of forecasting the stock market.

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Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis

Survey on KNN Methods in Data Science

Analysis of Decision Tree Algorithms for Diabetes Prediction

A stochastic approximation approach to fixed instance selection

K-Nearest Neighbors (KNN) rule is a simple yet powerful classification technique in machine learning. Nevertheless, it suffers from some drawbacks such as high memory consumption, low time efficiency, class overlapping and difficulty of setting an appropriate K value. In this study, we propose an Improved K-Nearest Neighbor rule combining Prototype Selection and Local Feature Weighting (IKNN_PSLFW) to address the above issues in one framework. Differing from conventional prototype selection, IKNN_PSLFW not only selects the representative instances as prototypes, but also preserves the information of instances that are not selected. To deal with the class overlapping problem, IKNN_PSLFW explores the feature relevance in local regions by assigning different weights to different features. For an instance with unknown class label, IKNN_PSLFW uses three classification rules corresponding to three scenarios, according to the distance between the instance and each prototype, for classification. To evaluate the performance of IKNN_PSLFW, we conduct experimental study on 20 benchmark datasets. The experimental results show that compared with the conventional KNN rule, some state-of-the-art prototype selection methods and other machine learning algorithms, the proposed IKNN_PSLFW achieves promising classification performance with high time efficiency. 

K-nearest neighbors rule combining prototype selection and local feature weighting for classification

Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule

In order to improve the accuracy of hierarchical network security situational awareness data fusion and shorten the fusion time, this paper proposes a hierarchical network security situational awareness data fusion method in cloud computing environment. The hierarchical model is established to obtain the hierarchical structure of data fusion. Hierarchical network security situational awareness data are collected and processed in parallel by cloud computing technology. According to the data collection results, the similarity between security events is calculated by using the clustering idea, and the similar security events are merged to achieve the purpose of removing redundant events. The hierarchical network security situational awareness data is fused by grey relational analysis. Finally, the simulation results show that the accuracy of data fusion of this method is high, up to 98%, and the fusion time is short, the longest is 13 s. Compared with the comparison method, this method has a better performance, indicating that this method is suitable for data fusion of hierarchical network security situation awareness.

Hierarchical network security situation awareness data fusion method in cloud computing environment

In recent years, due to the explosive growth of social media information, mining hot information in social media has become a research direction of great concern. In this paper, Python crawler technology is used to crawl the semi-structured text data of food safety news from static web pages and dynamic web pages. After preprocessing, the structured text data required to establish a document clustering algorithm (CASC) based on a convolutional neural network is obtained. Using the feature extraction ability of convolutional neural network and self-encoder, while preserving the internal structure of the original data to the greatest extent, it is embedded into the low-dimensional potential space for clustering. Finally, it is compared with the performance of the K-means algorithm and spectral clustering algorithm. The experimental results show that the CASC algorithm reduces the running time and time complexity of the algorithm on the premise of ensuring clustering accuracy. The CASC algorithm is superior to the k-means algorithm and spectral clustering algorithm in precision, recall, and composite index. At the same time, the running time is 91 seconds faster than the K-Means algorithm and 5 seconds faster than the spectral clustering algorithm.

Hongwu Zhang

Papers

K-nearest neighbors rule combining prototype selection and local feature weighting for classification

Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule

Hierarchical network security situation awareness data fusion method in cloud computing environment

Media Content Mining Based on Artificial Intelligence and Network Interaction