An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators

In the financial market, predicting stock trends based on stock market news is a challenging task, and researchers are devoted to developing forecasting models. From the existing literature, the performance of the forecasting model is better when news sentiment and technical analysis are considered than when only one of them is used. However, analyzing news sentiment for trend forecasting is a difficult task, especially for Chinese news, because it is unstructured data and extracting the most important features is difficult. Moreover, positive or negative news does not always affect stock prices in a certain way. Therefore, in this paper, we propose an approach to build an ensemble classifier using sentiment in Chinese news at sentence level and technical indicators to predict stock trends. In the training stages, we first divide each news item into a set of sentences. TextRank and word2vec are then used to generate a predefined number of key sentences. The sentiment scores of these key sentences are computed using the given financial lexicon. The sentiment values of the key phrases, the three values of the technical indicators and the stock trend label are merged as a training instance. Based on the sentiment values of the key sets, the corpora are divided into positive and negative news datasets. The two datasets formed are then used to build positive and negative stock trend prediction models using the support vector machine. To increase the reliability of the prediction model, a third classifier is created using the Bollinger Bands. These three classifiers are combined to form an ensemble classifier. In the testing phase, a voting mechanism is used with the trained ensemble classifier to make the final decision based on the trading signals generated by the three classifiers. Finally, experiments were conducted on five years of news and stock prices of one company to show the effectiveness of the proposed approach, and results show that the accuracy and P / L ratio of the proposed approach are 61% and 4.0821 are better than the existing approach

Employing Monitoring System to Analyze Incidents in Computer Network

Today, network technologies can handle throughputs or up to 100 Gbps, transporting 200 million packets per second on a single link. Such high bandwidths impact network flow analysis and as a result require significantly more powerful hardware. Methods used today concentrate mainly on analyses of data flows and patterns. It is nearly impossible to actively look for anomalies in network packets and flows. A small amount of change of monitoring patterns could result in big increase in potentially false positive incidents. This paper focuses on multi-criteria analyses of systems generated data in order to predict incidents. We prove that system generated monitoring data are an appropriate source to analyze and allow for much more focused and less computationally intensive monitoring operations. By using appropriate mathematical methods to analyze stored data, it is possible to obtain useful information. During our work, some interesting anomalies in networks were found by utilizing simple data correlations using monitoring system Zabbix. Afterwards, we prepared and pre-processed data to classify servers and hosts by their behavior. We concluded that it is possible to say that deeper analysis is possible thanks to Zabbix monitoring system and its features like Open-Source core, documented API and SQL backend for data. The result of this work is a new approach to analysis containing algorithms which allow to identify significant items in monitoring system

Recurrent neural network with density-based clustering for group pattern detection in energy systems

This research explores a new direction in power system technology and develops a new framework for pattern group discovery from large power system data. The efficient combination between the recurrent neural network and the density-based clustering enables to find the group patterns in the power system. The power system data is first collected in multiple time series data and trained by the recurrent neural network to find simple patterns. The simple patterns are then studied, and analyzed with the density-based clustering algorithm to identify the group of patterns. The solution was analyzed in two case studies (pattern discovery and outlier detection) specifically for power systems. The results show the advantages of the proposed framework and a clear superiority compared to state-of-the-art approaches, where the average correlation in group pattern detection is 90% and in group outlier detection more than 80% of both true-positive and true-negative rates.publishedVersio

Fast and accurate convolution neural network for detecting manufacturing data

This article introduces a technique known as clustering with particle for object detection (CPOD) for use in smart factories. CPOD builds on regional-based methods to identify smart object data using outlier detection, clustering, particle swarm optimization (PSO), and deep convolutional networks. The process starts by removing noise and errors from the images database by the local outlier factor (LOF) algorithm. Next, the algorithm studies different correlations from the set of images in the database. This creates homogeneous, and similar clusters using the well-known k-means algorithm, and the FastRCNN (fast region convolutional neural network) uses these clusters to design efficient and more focused models. PSO is used to optimize the different parameters including, the number of neighbors of LOF, the number of clusters of k-means, the number of epochs, and the error learning rate for FastRCNN. The inference process benefits from the knowledge provided by training. Instead of considering a complex single model of the whole images database, we consider a simple homogeneous model. To demonstrate the usefulness of our approach, intensive experiments have been carried out on standard images database, and real smart manufacturer data. Our results show that CPOD when compared to baseline object detection solutions is superior in terms of runtime and accuracy.acceptedVersio

Efficient chain structure for high-utility sequential pattern mining

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates

Recurrent neural network with density-based clustering for group pattern detection in energy systems

This research explores a new direction in power system technology and develops a new framework for pattern group discovery from large power system data. The efficient combination between the recurrent neural network and the density-based clustering enables to find the group patterns in the power system. The power system data is first collected in multiple time series data and trained by the recurrent neural network to find simple patterns. The simple patterns are then studied, and analyzed with the density-based clustering algorithm to identify the group of patterns. The solution was analyzed in two case studies (pattern discovery and outlier detection) specifically for power systems. The results show the advantages of the proposed framework and a clear superiority compared to state-of-the-art approaches, where the average correlation in group pattern detection is 90% and in group outlier detection more than 80% of both true-positive and true-negative rates.publishedVersio

Editor's Note

In today’s world, we have witnessed an onset of multimedia content being uploaded/downloaded and shared through a multitude of platforms both online and offline. In support of this trend, multimedia processing and analyzing has become very popular in all kinds of information extraction and attracts research interest from both academia and industry. This is to be expected as the multimedia digital world is worth trillions of dollars worldwide. However, multimedia information is hard to encode, interpret and recognize because it is combined with many complex components. Recently, there are many research areas related to the overall notion of intelligent multimedia processing. Therefore, the collected papers in this special issue provide a systematic overview and state-of-the-art research in the field of intelligent multimedia processing and analyzing system and outline new developments in fundamental, theorems, approaches, methodologies, software systems, recommendations, and real-world applications in this area

FRIOD: a deeply integrated feature-rich interactive system for effective and efficient outlier detection

In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the detection process. A user-friendly interactive mechanism is developed to allow easy and intuitive user interaction in all the major stages of the underlying outlier detection algorithm which includes dense cell selection, location-aware distance thresholding, and final top outlier validation. By doing so, we can mitigate the major difficulty of the competitive outlier detection methods in specifying the key parameter values, such as the density and distance thresholds. An innovative optimization approach is also proposed to optimize the grid-based space partitioning, which is a critical step of FRIOD. Such optimization fully considers the high-quality outliers it detects with the aid of human interaction. The experimental evaluation demonstrates that FRIOD can improve the quality of the detected outliers and make the detection process more intuitive, effective, and efficient