Evaluation of mobile services and substantial adoption factors with Analytic Hierarchy Process (AHP)

Emergence of new technological innovations in networks, platforms, and applications has enabled service providers to gain back their massive investment in their infrastructures. However, due to lagging adoption, many service innovations have failed to generate profit. The adoption of different mobile service categories depends on several factors. The current explorative study aims to use Analytic Hierarchy Process (AHP) to identify the most relevant mobile services for consumers and the factors driving the adoption. The results of the AHP analyses indicate that functionality of services is of utmost important for the majority of respondents. The results reveal that basic mobile communication services are the most preferred ones, although several services within different categories are available. The results have important implications for mobile network operators, service and application providers on how to develop and implement specific mobile services. The current study also offers new insights for researchers by showing that AHP is applicable to analyze consumers' preferences.     

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.