A novel intrusion detection system based on feature generation with visualization strategy

In this paper, a four-angle-star based visualized feature generation approach, FASVFG, is proposed to evaluate the distance between samples in a 5-class classification problem. Based on the four angle star image, numerical features are generated for network visit data from KDDcup99, and an efficient intrusion detection system with less features is proposed. The FASVFG-based classifier achieves a high generalization accuracy of 94.3555% in validation experiment, and the average Mathews correlation coefficient reaches 0.8858.     

A weighted full-reference image quality assessment based on visual saliency

In full reference image quality assessment (IQA), the images without distortion are usually employed as reference, while the structures in both reference images and distorted images are ignored and all pixels are equally treated. In addition, the role of human visual system (HVS) is not taken account into subjective IQA metric. In this paper, a weighted full-reference image quality metric is proposed, where a weight imposed on each pixel indicates its importance in IQA. Furthermore, the weights can be estimated via visual saliency computation, which can approximate the subjective IQA via exploiting the HVS. In the experiments, the proposed metric is compared with several objective IQA metrics on LIVE release 2 and TID 2008 database. The results demonstrate that SROCC and PLCC of the proposed metric are 0.9647 and 0.9721, respectively,which are higher than other methods and it only takes 427.5 s, which is lower than that of most other methods.     

Detecting outlier pairs in complex network based on link structure and semantic relationship

In this paper, we propose an outlier pair detection method, called LSOutPair, which discovers the vast differences between link structure and semantic relationship. LSOutPair addresses three important challenges: (1) how can we measure the target object's link similarity among multi-typed objects and multi-typed relations? (2) how can we measure the semantic similarity using the short texts? (3) how can we find the objects’ maximum differences between link structure and semantic relationship? To tackle these challenges, LSOutPair applies three main techniques: (1) two matrices are used to store link similarity and semantic similarity, (2) a k-step index algorithm, which calculates the term weighting for each object, (3) applying the linear transformation of Frobenius norm to matrices can obtain the top-K outlier pairs. LSOutPair considers link and semantics in complex network simultaneously, which is a new attempt in data mining. Substantial experiments show that LSOutPair is very effective for outlier pair detection.     

A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Stock market prediction is of great interest to stock traders and investors due to high profit in trading the stocks. A successful stock buying/selling generally occurs near price trend turning point. Thus the prediction of stock market indices and its analysis are important to ascertain whether the next day's closing price would increase or decrease. This paper, therefore, presents a simple IIR filter based dynamic neural network (DNN) and an innovative optimized adaptive unscented Kalman filter for forecasting stock price indices of four different Indian stocks, namely the Bombay stock exchange (BSE), the IBM stock market, RIL stock market, and Oracle stock market. The weights of the dynamic neural information system are adjusted by four different learning strategies that include gradient calculation, unscented Kalman filter (UKF), differential evolution (DE), and a hybrid technique (DEUKF) by alternately executing the DE and UKF for a few generations. To improve the performance of both the UKF and DE algorithms, adaptation of certain parameters in both these algorithms has been presented in this paper. After predicting the stock price indices one day to one week ahead time horizon, the stock market trend has been analyzed using several important technical indicators like the moving average (MA), stochastic oscillators like K and D parameters, WMS%R (William indicator), etc. Extensive computer simulations are carried out with the four learning strategies for prediction of stock indices and the up or down trends of the indices. From the results it is observed that significant accuracy is achieved using the hybrid DEUKF algorithm in comparison to others that include only DE, UKF, and gradient descent technique in chronological order. Comparisons with some of the widely used neural networks (NNs) are also presented in the paper.     

Text analytics in industry: Challenges,desiderata and trends

The recent decades have witnessed an unprecedented expansion in the volume of unstructured data in digital textual formats. Companies are now starting to recognize the potential economic value lying untapped in their text data repositories and sources, including external ones, such as social media platforms, and internal ones, such as safety reports and other company-specific document collections. Information extracted from these textual data sources is valuable for a range of enterprise application and for informed decision making. In this article we provide a systematic review of the current state of the art in the application of text analytics in industry. Our review is structured along three dimensions: the application context, the methods and techniques utilized, and the evaluation procedure. Based on the review, we identify the different challenges and constraints that an real-world, industrial environment imposes on text analytics techniques, as opposed to their deployment in more controlled, research environments. In addition, we formulate a set of desiderata that text analytics techniques should satisfy in order to alleviate these challenges and to ensure their successful deployment in industry. Furthermore, we discuss future trends in text analytics and their potential application in industry.     

Modeling multi-criteria objective functions using fuzzy measures

We introduce the idea of a measure. We describe several important finite integrals useful for obtaining an average value of a collection of argument values weighted by a measure. We particularly look at the case of binary measures and show that all integrals in this case evaluate to the same value. We describe the use of measures in multi-criteria decision making as a way of expressing a decision maker’s objective function in terms of collection of relevant criteria. We look at the role of an integral as a way to evaluate an alternative’s overall satisfaction to their objective function in terms of its satisfaction to the individual criteria. We look at a number of special types of measure based decision objective functions.     

Lessons learned on development and application of agent-based models of complex dynamical systems

The field of agent-based modelling (ABM) has gained a significant following in recent years, and it is often marketed as an excellent introduction to modelling for the novice modeller or non-programmer. The typical objective of developing an agent-based model is to either increase our mechanistic understanding of a real-world system, or to predict how the dynamics of the real-world system are likely to be affected by changes to internal or external factors. Although there are some excellent ABMs that have been used in a predictive capacity across a number of domains, we believe that the promotion of ABM as an ‘accessible to all’ approach, could potentially lead to models being published that are flawed and therefore generate inaccurate predictions of real-world systems. The purpose of this article is to use our experiences in modelling complex dynamical systems, to reinforce the view that agent-based models can be useful for answering questions of the real-world domain through predictive modelling, but also to emphasise that all modellers, expert and novice alike, must make a concerted effort to adopt robust methods and techniques for constructing, validating and analysing their models, if the result is to be meaningful and grounded in the system of interest.     

Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification

An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers.