The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model … heterogeneous data, achieve optimal performance or use minimal resources. In this paper, we introduce a class membership-based (CMB …) classifier which is a transparent approach well adapted to heterogeneous data that exploits nominal variables in the decision …

learning and other innovative technologies, it is also unclear how these changes interact with human roles in organisations and … examine the impact of deep machine learning modules in predicting the human actor roles in accounting information systems in … organisations. We targeted 120 human actors and examined the influence of deep machine learning modules in predicting 11 personnel …

evidence (e.g., from climate–weather integrated models). We demonstrate how machine learning methods can contribute to not …-for-profit pressing societal issues, such as global warming, where data complexity and availability can be overcome. …

of the data and the developments of predictive models of churn were performed using the Orange data mining software. The …

In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from … financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages …. In our first method, we propose a novel structure that uses the word information and the machine learning method …

with Kohonen networks, which represent the main method for processing a large volume of not only accounting data on …

In this paper, we present a study on Reinforcement Learning optimization models for automatic trading, in which we … focus on the effects of varying the observation time. Our Reinforcement Learning agents feature a Convolutional Neural …

Kong. A novel machine-learning algorithm with the latest technique in natural language processing where applicable to multi …

multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential …-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures. …

tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework …We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in … according to market trend movements predicted by machine learning models. We employ Gini's Mean Difference (GMD) to specify the …