Due to the complexity of the financial market, security returns are sometimes expressed by expert estimates rather than historical data. In this paper, we deal with a multiobjective multiperiod portfolio selection problem based on uncertainty theory. We propose a new uncertain multiobjective multiperiod mean-semisentropy-skewness portfolio optimization model, in which uncertain semi-entropy is used to quantify the downside risk. To be more realistic, several constraints are also considered, such as the transaction costs, cardinality, liquidity, budget, and bound constraint. Moreover, a novel hybrid technique, called the MFA-SOS algorithm, which combines the features of the firefly algorithm (FA) and symbiotic organism search algorithm (SOS) is designed to solve the proposed model. Finally, a numerical example is given to illustrate the effectiveness of the proposed approach.

At present, integrating investor sentiment into the prediction of stock market crisis has attracted more and more attention. However, the existing researches only considered the impact of the market indicators and the micro investor sentiment on stock market, while ignored the impact of the macro one. Therefore, in this paper, we develop an early warning system for predicting stock market crisis via market indicators and mixed frequency investor sentiments. The proposed early warning system consists of five components, which includes the construction of mixed frequency investor sentiments that consider both macro and micro investor sentiments, the identification of stock market crisis, the determination of the forecast horizon using Ensemble Empirical Mode Decomposition (EEMD), the definition of the early warning signal, and the building of prediction model using artificial neural network (ANN). Lastly, we apply the developed warning system to China’s stock markets. Experimental results show that mixed frequency investor sentiments can improve the early warning ability in predicting the stock market crisis, and the ANN model has better performance than other methods including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Logistic Regression (LR).

The adaptability plays a significant role in moving detection. The diverse scenarios in real world still challenge this problem. Therefore, in this paper, we proposed an adaptive moving detection method, namely Adaptive Random-based Self-Organizing back- ground subtraction (ABSOBS) method. This method can adaptively extract the moving objects in various conditions and eliminate the “ghost” pixels simultaneously. Therefore, a robust initialization strategy is proposed to remove the noise pixels caused by the initialized frames. The proposed method uses a random- based scheme which allows the foreground pixels to up- date the neural network with a small probability. This strategy allows our algorithm to efficiently handle scene changes. Moreover, a foreground filter based on random rule is designed to eliminate the “ghost” pixel. More importantly, ABSOBS adopts a regulator to control the updating rate in different conditions. It makes our method easy-to-used and need not to set the parameters manually. The experiment results on various scenarios show that our method improves the detection accuracy for the SOBS and outperforms other state-of- the-art methods.

Stock market timing is regarded as a challenging task of financial prediction. An accurate prediction of stock trend can yield great profits for investors. At present, recurrent neural networks (RNNs) have a good performance in stock market forecasting. However, there has been a relative lack of research in the stock market timing using RNNs. In this paper, a novel model named hybrid RNN model is proposed for stock market timing by incorporating multi-layer long short-term memory, multi-layer gated recurrent unit and one-layer ReLU layer. Moreover, based on five popular benchmark datasets from UCI Machine Learning Repository and six daily securities from Shanghai Stock Exchange, comparisons with 12 state-of-the-art models are conducted to verify the superiority of the proposed hybrid RNN model in terms of nine technical indicators. The findings from the experiment demonstrate that: (1) as opposed to 12 models, the average accuracy, MSE and AUC of hybrid RNN model (0.7406, 0.2592, 0.7368) are significantly better than other comparison models, and (2) the proposed hybrid RNN classification procedure can be considered as a feasible and effective tool for stock market timing.

The projection and representation learning is an attractive tool for image classification problem due to its effectiveness and efficiency of extracting interior structure for data. However, the complexity and diversity of real data lead to the decline of classification performance. A novel image classification method is proposed by learning a minimum similarity projection and lowest correlation representation. This method attempts to produce a discriminative representation on a low-dimensional space for the data, which takes two steps: feature projection and feature representation. By learning a projection matrix, the feature projection aims to map the samples into a low-dimensional space which jointly minimises the similar within-class difference and maximises the dissimilar cross-class difference. A discriminative representation for the data on the new space is generated by using the de-correlated effect to the representation results of all classes. Therefore, the learned projection and representation simultaneously demonstrate discriminative properties in the learning of both steps. The extensive experiments conducted on different visual classification tasks consist of face recognition, object categorisation, and scene classification that the proposed method performs superior performance for image classification.

In the complex financial market, there are situations where the security returns have to be evaluated by experienced experts due to the lack of historical data. In this paper, within the framework of uncertainty theory, we propose a multi-period bi-objective regret minimization model for portfolio selection, in which bankruptcy risk and liquidity risk are both considered. Furthermore, in order to solve the proposed multi-objective optimization problem, a novel hybrid algorithm named MFA-SCA is proposed by combining the advantages of the firefly algorithm (FA) and sine cosine algorithm (SCA). Finally, a numerical example is given to illustrate the effectiveness of the proposed approaches.

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus.Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance.Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance.However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization.Also, some of measured data may be unavailable due to the interference of a complicated environment.In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion.We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix.Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database.Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation.Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

In this paper, within the framework of uncertainty theory, two kinds of concepts about uncertain numerical series and uncertain positive numerical series are introduced. Besides, several convergence theorems in terms of above two concepts are presented respectively, and some corresponding examples are also given.

In this paper, we discuss a multi-period portfolio selection problem when security returns are given by experts’ estimations. By considering the security returns as uncertain variables, we propose a multi-period mean–semivariance portfolio optimization model with real-world constraints, in which transaction costs, cardinality and bounding constraints are considered. Furthermore, we provide an equivalent deterministic form of mean–semivariance model under the assumption that the security returns are zigzag uncertain variables. After that, a modified imperialist competitive algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

Big data, Web of Things and cloud computing have emerged and are widely used. Only by making full use of the development of emerging technologies, wisdom city can realize its own wisdom city essence. This article will tell you how to combine big data with wisdom cities. Big data is not only the practical application of big data, but also the further improvement for intelligence of the city's level.

In the mean-variance-skewness-kurtosis framework, this paper discusses an uncertain higher-order moment portfolio selection problem when security returns are given by experts' evaluations. Based on uncertainty theory and the assumption that the security returns are zigzag uncertain variables, an uncertain multi-objective portfolio optimization model is proposed by considering the maximizationof both the expected return and skewness of portfolio return while simultaneously minimizing the risk and kurtosis of portfolio return. Subsequently, the proposed model is transformed into a single-objective programming model by using fuzzy programming approach, in which investor preferences for high moments are incorporated. Furthermore, a modified flower pollination algorithm (MFPA) is developed for solution, in which PSO in local update strategy (PSOLUS) and dynamic switching probability strategy (DSPS) are employed to enhance the local searching and global searching abilities. Finally, a numerical example is presented to illustrate the application of the proposed model and solution comparisons are also given to demonstrate the effectiveness of the designed algorithm.

The raising and operation of social security funds have become a serious problem faced by China. Levying social security tax is an effective way to raise fund for social security system. This paper analyzes the favorable and unfavorable conditions of levying social security tax in China. We found that the social security tax should be deferred in China.

Welcome to the WIT Press eLibrary - the home of the Transactions of the Wessex Institute collection, providing on-line access to papers presented at the Institute's prestigious international conferences and from its State-of-the-Art in Science & Engineering publications.

Some traditional methods, such as NPV, now cannot afford appropriate decision assistance for information project. Information system project costs long time and need more complicated technologies, and it can be divided into phases. The real option of information project is playing an increasingly crucial role in the decision of investment. This paper considers the steps of information project, and the value project real option is descirte. Evaluate the real option of information project with improved binomial model which based on the characters of information project.

This paper solves the design problem of enterprise informationization evaluation indicator system. It analyzed the national informatization evaluation center of enterprise informatization evaluation system of investment benefit. This paper proves that the comprehensive evaluation index system is worth of popularization and easy to practice.

This paper describes the meaning of enterprise informatization index system building. It proposes the enterprise informationization index system construction principle and design ideas. The enterprise informatization index is calculated by the mathematical model of fuzzy comprehensive evaluation.

Based on resource allocation problems, the article explored the upgrade informatization investment theories and methods of bi-level programming method. It is used to construct resource allocation model of informatization promotion investment, in order to provide useful reference for the informatization investment research in the future.