Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.

Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization

In this paper, polynomial fuzzy neural network classifiers (PFNNCs) is proposed by means of density fuzzy c-means and L2-norm regularization. The overall design of PFNNCs was realized by means of fuzzy rules that come in form of three parts, namely premise part, consequence part and aggregation part. The premise part was developed by density fuzzy c-means that helps determine the apex parameters of membership functions, while the consequence part was realized by means of two types of polynomials including linear and quadratic. L2-norm regularization that can alleviate the overfitting problem was exploited to estimate the parameters of polynomials, which constructed the aggregation part. Experimental results of several data sets demonstrate that the proposed classifiers show higher classification accuracy in comparison with some other classifiers reported in the literature.

DM-L Based Feature Extraction and Classifier Ensemble for Object Recognition

Deep Learning is a powerful technique that is widely applied to Image Recognition and Natural Language Processing tasks amongst many other tasks. In this work, we propose an efficient technique to utilize pre-trained Convolutional Neural Network (CNN) architectures to extract powerful features from images for object recognition purposes. We have built on the existing concept of extending the learning from pre-trained CNNs to new databases through activations by proposing to consider multiple deep layers. We have exploited the progressive learning that happens at the various intermediate layers of the CNNs to construct Deep Multi-Layer (DM-L) based Feature Extraction vectors to achieve excellent object recognition performance. Two popular pre-trained CNN architecture models i.e. the VGG_16 and VGG_19 have been used in this work to extract the feature sets from 3 deep fully connected multiple layers namely “fc6”, “fc7” and “fc8” from inside the models for object recognition purposes. Using the Principal Component Analysis (PCA) technique, the Dimensionality of the DM-L feature vectors has been reduced to form powerful feature vectors that have been fed to an external Classifier Ensemble for classification instead of the Softmax based classification layers of the two original pre-trained CNN models. The proposed DM-L technique has been applied to the Benchmark Caltech-101 object recognition database. Conventional wisdom may suggest that feature extractions based on the deepest layer i.e. “fc8” compared to “fc6” will result in the best recognition performance but our results have proved it otherwise for the two considered models. Our experiments have revealed that for the two models under consideration, the “fc6” based feature vectors have achieved the best recognition performance. State-of-the-Art recognition performances of 91.17% and 91.35% have been achieved by utilizing the “fc6” based feature vectors for the VGG_16 and VGG_19 models respectively. The recognition performance has been achieved by considering 30 sample images per class whereas the proposed system is capable of achieving improved performance by considering all sample images per class. Our research shows that for feature extraction based on CNNs, multiple layers should be considered and then the best layer can be selected that maximizes the recognition performance.

关于Focal Loss的Tensor Train多项式分类器

在模式分类领域,多项式分类器因其复杂决策边界能力而得到广泛研究。利用TensorTrain分解形式来表示多项式分类器,可有效克服维数灾难。针对多项式分类器在训练过程中遇到的训练集分布不平衡问题,文章使用FocalLoss重塑了标准交叉损失,以降低分配给易分类样本的损失的权重,并在被广泛使用的图像分类数据集MNIST上验证了分类器的有效性。

Impact of Portable Executable Header Features on Malware Detection Accuracy

One aspect of cybersecurity,incorporates the study of Portable Executables(PE)files maleficence.Artificial Intelligence(AI)can be employed in such studies,since AI has the ability to discriminate benign from malicious files.In this study,an exclusive set of 29 features was collected from trusted implementations,this set was used as a baseline to analyze the presented work in this research.A Decision Tree(DT)and Neural Network Multi-Layer Perceptron(NN-MLPC)algorithms were utilized during this work.Both algorithms were chosen after testing a few diverse procedures.This work implements a method of subgrouping features to answer questions such as,which feature has a positive impact on accuracy when added?Is it possible to determine a reliable feature set to distinguish a malicious PE file from a benign one?when combining features,would it have any effect on malware detection accuracy in a PE file?Results obtained using the proposed method were improved and carried few observations.Generally,the obtained results had practical and numerical parts,for the practical part,the number of features and which features included are the main factors impacting the calculated accuracy,also,the combination of features is as crucial in these calculations.Numerical results included,finding accuracies with enhanced values,for example,NN_MLPC attained 0.979 and 0.98;for DT an accuracy of 0.9825 and 0.986 was attained.

高灰细泥对煤炭分级浮选的影响研究

文章采用两种煤样分别进行常规混合浮选和分级浮选,运用多项式拟合的方法建立了浮选精煤产率、精煤灰分、浮选完善度与药剂用量的函数关系。通过对这一数学模型的分析表明对于裕隆煤样,分级浮选可明显改善浮选效果,而对于汶上煤样,分级浮选效果不佳。通过高灰细泥与不同粒度精煤颗粒静电作用的差异对这一现象进行了解释。

基于多项式曲面拟合的TFT-LCD斑痕缺陷自动检测技术

在液晶显示器的各种视觉缺陷中,斑痕缺陷的检测是最为复杂的问题,尚未能有效解决。为了实现自动化作业,提出了一种新的斑痕缺陷检测方法。在图像分割中,提出了一种基于最小二乘法多项式曲面拟合技术的背景建模方法。利用该背景模型,可以将可能含有斑痕缺陷的图像区域从复杂背景中分割出来。在特征提取中,综合考虑了目标区域的对比度、面积、位置、轮廓、形状和亮度均匀性等特征量,建立了一种新的斑痕缺陷模型。在模式识别中,设计了基于规则的模糊分类器。该方法可以充分利用检测人员的经验和知识从而获得与人类观察者心理特点相一致的检测结果。

图象块编码—分类的方法

提出了一个基于ＤＣＴ和二维多项式近似的块分类编码算法。在该算法中，原始图象被分割成互不覆盖的８×８子块。通过依次地利用灰度局部方差、二维多项式近似误差和图象信号的空间频率分布，把图象块分为均匀、平滑、粗糙和细节４类。均匀块和平滑块分别采用零阶和一阶多项式近似。粗糙和细节块先进行ＤＣＴ变换，然后对其ＤＣＴ系数量化后采用改进的游程编码表示。实验结果表明该算法具有良好的性能。在未采用熵编码为编码码流作后处理的情况下，性能仍优于ＪＰＥＧ标准。

一种基于NAPS核函数支持矢量机的说话人识别算法

介绍了指数展开分类器,引出了NAPS 核函数及核映射的概念. 详细讨论了如何利用基于NAPS 核函数的支持矢量机进行说话人识别的算法. 理论和实验表明,算法具有模型参数小、识别速度快和识别率较高的优点.

基于多项式映射的分类器及其在变压器故障诊断中的应用研究

变压器故障诊断和维修是一项复杂的任务,尽快诊断出故障并确定故障类型为即时安排相应的专业维修技术人员争取时间抢修,对于电力系统可靠供电,尤其是不允许断电的用电场所,具有非常重要的意义。文中采用多项式映射,将样本数据映射至高维空间,对高维空间的生成样本设计分类器进行分层分类。对于在线诊断系统,针对传感器或光谱仪收集数据存在较大误差的问题,文中对方法进行了"测不准"鲁棒性分析。文末给出了基于溶解氧含量(DGA)实例,并与相关研究结果进行了比较,证明文中所提出的方法的有效性和较好的鲁棒稳定性。

Novel algorithms for accurate DNA base-calling

The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces generated by existing sequencing machines, a process referred to as DNA base-calling. In this paper, a pattern recognition technique was adopted to minimize the inaccuracy in DNA base-calling. Two new frameworks using Artificial Neural Networks and Polynomial Classifiers are proposed to model electropherogram traces belonging to Homo sapiens, Saccharomyces mikatae and Drosophila melanogaster. De-correlation, de-convolution and normalization were implemented as part of the pre-processing stage employed to minimize data imperfections attributed to the nature of the chemical reactions involved in DNA sequencing. Discriminative features that characterize each chromatogram trace were subsequently extracted and subjected to the chosen classifiers to categorize the events to their respective base classes. The models are trained such that they are not restricted to a specific species or to a specific chemical procedure of sequencing. The base- calling accuracy achieved is compared with the exist- ing standards, PHRED (Phil’s Read Editor) and ABI (Applied Biosystems, version2.1.1) KB base-callers in terms of deletion, insertion and substitution errors. Experimental evidence indicates that the proposed models achieve a higher base-calling accuracy when compared to PHRED and a comparable performance when compared to ABI. The results obtained demon- strate the potential of the proposed models for efficient and accurate DNA base-calling.

Group Method of Data Handling for Modeling Magnetorheological Dampers

This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons that offers an effective solution to modeling non-linear systems. As such, we propose the use of GMDH to approximate the forward and inverse dynamic behaviors of MR dampers. We also introduce two enhanced GMDH-based solutions. Firstly, a two-tier architecture is proposed whereby an enhanced GMD model is generated by the aid of a feedback scheme. Secondly, stepwise regression is used as a feature selection method prior to GMDH modeling. The proposed enhancements to GMDH are found to offer improved prediction results in terms of reducing the root-mean-squared error by around 40%.

Feature aggregation for nutrient deficiency identification in chili based on machine learning

Macronutrient deficiency inhibits the growth and development of chili plants.One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision.This study uses 5166 image data after augmentation process for six plant health conditions.But the analysis of one feature cannot represent plant health condition.Therefore,a careful combination of features is required.This study combines three types of features with HSV and RGB for color,GLCM and LBP for texture,and Hu moments and centroid distance for shapes.Each feature and its combination are trained and tested using the same MLP architecture.The combination of RGB,GLCM,Hu moments,and Distance of centroid features results the best performance.In addition,this study compares the MLP architecture used with previous studies such as SVM,Random Forest Technique,Naive Bayes,and CNN.CNN produced the best performance,followed by SVM and MLP,with accuracy reaching 97.76%,90.55%and 89.70%,respectively.Although MLP has lower accuracy than CNN,the model for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural environment.

ON CYCLE STRUCTURE OF STATE DIAGRAM FOR A CLASS OF NONLINEAR SHIFT REGISTERS

The function of a feedback shift register is determined by its feedback function. For a linear shift register, the feedback function is determined by its connection polynomial. By factoring its connection polynomial, the distribution of the cyclic length of the state dia-

Parallel compact integration in handwritten Chinese character recognition

In this paper, a new parallel compact integration scheme based on multi-layer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition (HCCR) problems. The idea of metasynthesis is applied to HCCR, and compact MLP network classifier is defined. Human intelligence and computer capabilities are combined together effectively through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme is characterized with parallel compact structure and better performance. It provides a promising way for applying MLP to large vocabulary classification.