A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Genetic diversity by AFLP analysis within Jatropha curcas L. populations in the State of São Paulo,Brazil

The genetic diversity of 5 populations of Jatropha curcas L. (Euphorbiaceae), a species cultivated in tropical countries and used in biodiesel production, was analyzed by amplified fragment length polymorphism (AFLP). Plants from distinct populations found in the State of São Paulo, Brazil, had their genetic diversity characterized by using three primer combinations. The number of polymorphic bands obtained reached 184 and the base pair length of bands ranged from 75 to 350, with average PIC values of 0.418. Accessions from the Aguas de Santa Barbara population presented the highest percentage of polymorphic loci (89.76%), followed by the populations of Catanduva (84.24%), Jales (80.98%), Jurucê (78.80%) and Taquaritinga (70.65%). Plants collected from the populations of Taquaritinga and Jales presented the smallest and highest genetic diversities, respectively, measured by using both Nei's genetic variability index (h = 0.2242 and 0.2973) and Shannon's index (I = 0.3359 and 0.4319). The results obtained indicated that 73.1% of genetic variability corresponds to intrapopulational variation and 26.8% to variation among populations. The clustering dendrogram using Jacquard showed four clusters. Three clusters with low genetic diversity grouped most of individuals collected in distinct regions (63.3% JU, 47.0% JA and 82.5% TA) and the fourth with the higher genetic diversity was composed with basically individuals collected in CA and AS, but it also had samples collected in JU, JA and TA, where it is possible to select individuals to be included in breeding programs.     

Enhancing cation exchange capacity of chars through ozonation

The use of ozone to increase the cation exchange capacity (CEC) of two chars produced from pyrolysis of Douglas fir (Pseudotsuga menziessii) and a control bituminous coal activated carbon (AC) is reported. Chars were produced from the wood fraction of Douglas fir (DFWC) and the bark (DFBC) at 500 °C using an auger driven reactor with a nitrogen sweep gas under mild vacuum. Five ozone treatment times, ranging from 5 min to 60 min, were investigated. The initial properties of each char were found to differ significantly from the other samples in terms of surface area, proximate composition, and elemental composition. DFWC did not show significant mass loss or temperature variation during ozone treatment; however, after 1 h of oxidation both DFBC and AC samples resulted in 20% and 30% mass loss, respectively, and reactor temperatures in excess of 60 °C. Analysis of the pore size distribution of each treatment shows that ozone treatment did not significantly affect small micropores after 30 min of treatment for any material, but did reduce the apparent surface area of mesopores. Increases in carboxylic groups were identified with ozone treatment and found to correlate strongly with changes in measured CEC. The formation of lactone was found to correlate positively with reactor temperature during oxidation. These results indicate that the properties of chars, including surface area, pore structure, and chemical composition, as well as reactor conditions strongly affect the ozone oxidation of chars.     

Hierarchical models for analysis and recognition of handwritten characters

Different hierarchical models in pattern analysis and recognition are proposed, based on occurrence probability of patterns. As an important application of recognizing handprinted characters, three typical kinds of hierarchical models such asM _89-89,M _89-36 andM _36-36 have been presented, accompanied by the computer algorithms for computing recognition rates of pattern parts. Moreover, a comparative study of their recognition rates has been conducted theoretically; and numerical experiments have been carried out to verify the analytical conclusions made. Various hierarchical models deliberated in this paper can provide users more or better choices of pattern models in practical application, and lead to a uniform computational scheme (or code). The recognition rates of parts can be improved remarkably by a suitable hierarchical model. For the modelM _89-36 in which case some of the Canadian standard handprinted characters have multiple occurrence probabilities, the total mean recognition rates of the given sample may reach 120% of that by the model proposed by Li et al., and 156% of that obtained from the subjective experiments reported by Suen.     

Applying data mining to learn system dynamics in a biological model

Data mining consists of a set of powerful methods that have been successfully applied to many different application domains, including business, engineering, and bioinformatics. In this paper, we propose an innovative approach that uses genetic algorithms to mine a set of temporal behavior data output by a biological system in order to determine the kinetic parameters of the system. Analyzing the behavior of a biological network is a complicated task. In our approach, the machine learning method is integrated with the framework of system dynamics so that its findings are expressed in a form of system dynamics model. An application of the method to the cell division cycle model has shown that the method can discover approximate parametric values of the system and reproduce the input behavior.     

Evaluation of accuracy of measuring intraocular pressure by handheld non-contact applanation tonometer

PURPOSE: To evaluate the accuracy of measuring intraocular pressure by handheld non-contact applanation tonometer. METHOD: 58 patients' (113 eyes) intraocular pressure were measured by Keeler, non-contact tonometer and R 900 Goldmann applanation tonometer and the results of measurement of intraocular pressure by the two kinds of tonometers were compared. RESULT: The mean intraocular pressure measured by non-contact is 16.31 +/- 5.59 mmHg and 17.49 +/- 6.13 mmHg (1 mmHg = 0.1333 kPa) by Goldmann applanation tonometer, respectively. There was no statistical significance to be found (P > 0.05) between the two methods. By linear correlation and regression analysis, a positive correlation was found between the two methods (r = 0.8942, b = 0.8154). CONCLUSION: The handheld non-contact tonometer has the same accuracy and reliability of measurement of intraocular pressure comparing with Goldmann applanation tonometer, and it can be used in glaucoma clinic and screening.     

Ownership-attached unblinding of blind signatures for untraceable electronic cash

In an untraceable electronic cash protocol based on blind signatures, an identified customer can withdraw a blinded electronic cash from the bank and the unblinding operation is adopted by the customer to transform the blinded electronic cash into a valid one. Before performing the operation, the blinded electronic cash is protected well since attackers cannot convert it into a valid electronic cash without the blinding factor corresponding to the operation. However, after unblinding, the electronic cash will suffer from the theft attacks since it is not protected by any security mechanism. This paper introduces a new unblinding operation called ownership-attached unblinding which attaches the identities of a designated payee and a specified transaction to the blinded electronic cash and then produces an ownership-attached electronic cash other than a bare one such that the cash can withstand the theft attacks during the entire transaction because it is valid for the designated payee and the specified transaction only. Furthermore, the proposed scheme does not largely increase the computation cost required for each customer so that it also is a customer efficient protection solution for untraceable electronic cash and especially suitable for mobile clients and smart-card users.     

抗几何失真的视频Hashing算法研究

文中提出了一种基于视频灰度直方图形状的Hashing算法,能有效抵抗各种常见的几何失真和视频处理操作.算法的鲁棒性原理如下:1)由于直方图的形状与像素位置无关,故基于直方图的视频Hashing算法能有效抵抗各种常见的几何攻击;2)由于在计算Hashing前对视频帧进行了平滑预处理,故算法对加噪攻击、模糊滤波、有损压缩等处理操作有很好的鲁棒性;3)由于计算Hashing前在时间轴上进行了低通滤波预处理,故算法能抵抗帧率变化、帧丢失等时域同步攻击.实验结果表明,所提出的Hashing算法有良好的唯一性和鲁棒性能.