Abstract
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

Abstract

Abstract
The new 5G mobile communication system era brings a new set of communication devices that will appear on the market. These devices will generate data streams that require proper handling by machine algorithms. The processing of these data streams requires the design, development, and adaptation of appropriate machine learning algorithms. While stream processing algorithms include hyper-parameters for performance refinement, their tuning process is time-consuming and typically requires an expert to do the task. In this paper, we present an extension of the Self Parameter Tuning (SPT) optimization algorithm for data streams. We apply the Nelder-Mead algorithm to dynamically sized samples that converge to optimal settings in a double pass over data (during the exploration phase), using a relatively small number of data points. Additionally, the SPT automatically readjusts hyper-parameters when concept drift occurs. We did a set of experiments with well-known classification data sets and the results show that the proposed algorithm can outperform the results of previous hyper-parameter tuning efforts by human experts. The statistical results show that this extension is faster in terms of convergence and presents at least similar accuracy results when compared with the standard optimization techniques.

Abstract
The transformation of industrial manufacturing with computers and automation with smart systems leads us to monitor and log of industrial equipment events. It is possible to apply analytic approaches, and to find interpretive results for strategic decision making, providing advantages such as failure detection and predictive maintenance. Over the last years, many researchers have been studying the application of machine learning techniques to improve such tasks. In this context, we develop a system capable of detect anomalies on an Air Production Unit (APU), taking into consideration the peak frequency of each sensor. The study started with the analysis of the sensors installed on the APU, defining its normal behavior and its failure mode. Using that information, we define rules, to monitor the APU, to detect anomalies on its components, and to predict possible failures. The definition of rules was based on the peak frequency analysis, which allowed the setting of boundaries of normality for the APU working modes and, thus, the identification of anomalies.

Abstract
The goal of this study was grouping patients with parkinsonism that share similar gait characteristics based on principal component analysis (PCA). Spatiotemporal gait data during self-selected walking were obtained from 15 patients with Vascular Parkinsonism, 15 patients with Idiopathic Parkinson's Disease and 15 Controls. PCA was used to reduce the dimensionality of 12 gait characteristics for the 45 subjects. Fuzzy C-mean cluster analysis was performed plotting the first two principal components, which accounted for 84.1% of the total variability. Results indicates that it is possible to quantitatively differentiate different gait types in patients with parkinsonism using PCA. Objective graphical classification of gait patterns could assist in clinical evaluation as well as aid treatment planning.

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