In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.
α-Fe 2 O 3 /reduced graphene oxide (RGO) nanocomposites were synthesized by a rapid and simple microwave method. Fe(OH) 3 sol was used as the precursor of α-Fe 2 O 3 . Under the microwave heating, graphene oxide (GO) was reduced to RGO using hydrazine hydrate as a reductant and Fe(OH) 3 sol transformed to α-Fe 2 O 3 particles attached uniformly onto RGO surfaces at the same time. The structure, morphology and composition of α-Fe 2 O 3 /RGO nanocomposites were characterized by X-ray diffraction, transmission electron microscopy, scanning electron microscopy, thermogravimetric analysis and Raman spectrum. The electrochemical characteristics were valuated by coin-type cells versus metallic lithium and cyclic voltammetry. The prepared α-Fe 2 O 3 /RGO nanocomposites exhibited high reversible specific capacity of 650 mA⋅h⋅g -1 after 50 cycles at a current density of 1.0 A⋅g -1 , showing more superior rate capability than both of α-Fe 2 O 3 nanoparticles and RGO sheets themselves. At the larger current density of 10.0 A⋅g -1 , the capacity of α-Fe 2 O 3 /RGO nanocomposites still remained 400 mA⋅h⋅g -1 . The significant improvements in the electrochemical properties of α-Fe 2 O 3 /RGO nanocomposite could be attributed to the uniform α-Fe 2 O 3 nanoparticles (30-50 nm) on the RGO substrate which provided high electrical conductivity, confined the position and buffered the volume changes of α-Fe 2 O 3 nan stability and electrochemical activity, γ-Fe 2 O 3 and Fe 3 O 4 are spinel structure exhibiting very good electrical conductivity and so on. As other transition oxides, iron oxides as lithium ion battery anode materials will generate great volume-stress with huge volume changes between iron oxides and iron atoms in the process of charging and discharging. The process will breakdown crystal structures, short cycle life and cause capacity loss. Wu et al. [29] highlighted that the lithium storage capability would be further increased by designing advanced nanocomposite materials including metal oxides and carbonaceous supports. Two-dimensional (2D) graphene is the basic composition unit of 0D buckyballs, 1D nanotubes and 3D graphite and it has excellent electrochemical property [30] .Furthermore, lithium ion can also be inserted or extracted on both sides of the RGO [31] . Given all this, many attempts have been done to prevent it with electrospinning method [22,23] , hydrothermal method [19,24,[32][33][34]37,38] , microwave method [16,27,31] and chemical vapor deposition [20,21,26] et al. by preparing composite materials with carbon nanotubes [15,19] and graphene [16,20,21,24,27,[31][32][33][34][36][37][38][39] , coating iron oxides by carbon [20,25,26] and synthesis nano-sized iron oxides [17] , etc. Iron oxides anchored on graphene preparing nanocomposites as anodes of lithium ion batteries is a research spots and the composites' perfect performance as anodes of lithium ion battery have been shown. Zhang et al. [24] manufactured Fe 2 O 3 /GO nanocomposites by hydrothermal reaction and then prepared Fe 3 O 4 /RGO nanocompo...
The stock market is a capitalistic haven where the issued shares are transferred, traded, and circulated. It bases stock prices on the issue market, however, the structure and trading activities of the stock market are much more complicated than the issue market itself. Therefore, making an accurate prediction becomes an intricate as well as highly difficult task. On the other hand, because of the potential benefits of stock prediction, it attracts generation after generation of scholars as well as investors to continuously develop various prediction methods from different perspectives, a myriad of theories, a multitude of investment strategies, and different practical experiences. In this article, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network that can be called a framework to improve the prediction accuracy of stock trading is proposed. The method that is proposed is called SSACNN, a short form of stock sequence array convolutional neural network. SSACNN collects data including historical data of prices and its leading indicators (options/futures) for a stock to take an array as the input graph of the convolutional neural network framework. In our experimental results, five Taiwanese and American stocks were used as a benchmark to compare with the previous algorithms and proposed algorithm, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market.
1D upconversion CeO2:Er, Yb nanofibers, which absorb NIR light and upconvert it to visible light to increase the photocurrent of DSSCs, have been fabricated by an electrospinning method. An enhancement of 14% in the light harvesting efficiency was observed.
Recently, revealing more valuable information except for quantity value for a database is an essential research field. High utility itemset mining (HAUIM) was suggested to reveal useful patterns by average-utility measure for pattern analytics and evaluations. HAUIM provides a more fair assessment than generic high utility itemset mining and ignores the influence of the length of itemsets. There are several high-performance HAUIM algorithms proposed to gain knowledge from a disorganized database. However, most existing works do not concern the uncertainty factor, which is one of the characteristics of data gathered from IoT equipment. In this work, an efficient algorithm for HAUIM to handle the uncertainty databases in IoTs is presented. Two upper-bound values are estimated to early diminish the search space for discovering meaningful patterns that greatly solve the limitations of pattern mining in IoTs. Experimental results showed several evaluations of the proposed approach compared to the existing algorithms, and the results are acceptable to state that the designed approach efficiently reveals high average utility itemsets from an uncertain situation.
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