Narrow Results

The BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)–Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets.

Volatility of gold price is of great significance for avoiding the risk of gold investment. It is necessary to understand the effect of external events and intrinsic regularities to make accurate price predictions. This paper first compared EMD with CEEMD algorithm, and the results find that CEEMD algorithm performance is better than that of EMD in analysis gold price volatility. Then this paper uses the complementary ensemble empirical mode decomposition (CEEMD) to decompose the historical price of international gold into price components at different frequencies, and extracts a short-term fluctuation, a shock from significant events and a long-term price. In addition, this paper combines the Iterative cumulative sum of squares (ICSS) with Chow test to test the three event prices for structural breaks, and analyzes the effect of external events on volatility of gold price by comparing the external events with the test results for structural breaks. Finally, this paper constructs support vector machine (SVM) models and artificial neural network (ANN) on three series for prediction, and finds that the SVM performed better in gold price prediction in one-step-ahead and five-step-ahead, and when we combine the SVMs and ANNs with price components to make predictions, the error of the combined prediction is smaller than SVMs and ANNs with separate terms of series extracted.

The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.

Text summarization is one of the most discussed topic in the field in information exchange and retrieval. Recently, the need for local language based text summarization methods are increasing. In this paper, a method for text summarization in Hindi language is plotted with help of extraction methods. The proposed approach is uses three major algorithms, fuzzy classifier, neural network and global search optimization (GSO). The fuzzy classifier and neural network are used for generating sentence score. The GSO algorithm is used with the neural network, in order to optimize the weights in the neural network. A hybrid score is generated from fuzzy method and neural network for each input sentences. Finally, based on the hybrid score from fuzzy classifier and neural network, the summary of the given input records are generated. An experimental analysis of the proposed approach will subjected based on the evaluation parameters precision, recall. Later on experimental analysis are conducted on the proposed approach in order to evaluate the performance. According to the experimental analysis, the proposed approach achieved an average precision rate 0.90 and average recall rate of 0.88 for compression rate 20%. The comparative analysis also provided reasonable results to prove the efficiency of the proposed approach.

In the plastic industry for mold making, pocket milling is applied. The surface finish of the mold affects the quality of the plastic product, especially for toys. This can be achieved by minimising the surface roughness of the mold. To get a good quality product with a better production rate, the selection of the best combination of parameters in pocket milling is necessary. Multi-response optimisation can be applied for selecting such parameters which are suited for fulfilling the objective. In this study, one of the toy mold designs is selected as a pocket profile on which, two tool trajectories, viz Follow Periphery (FP) and Zigzag (ZZ), are applied for generation of pocket by varying Speed (S), Feed (F) and Step Over (SO). Box–Behnken Response Surface Methodology is applied to find the experimental runs. Two conflicting objectives minimising Surface Roughness (SR) and maximising Material Removal Rate (MRR) are obtained by applying Artificial Neural Networks (ANN) and Multi-Objective Genetic Algorithm (MOGA). Conformational experiments were conducted for the random set of Pareto results obtained from MOGA for both the tool trajectories to validate the model. From the analysis, it is observed that the FP tool path strategy is well suited to generate the pocket to get minimum SR and maximum MRR as the error percentage between the predicted and test results observed is 0.8085% for SR and 0.9236% for MRR.