Stock market forecasting is one of the biggest challenges in the financial market since its time series has a complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. However, due to computing development, an intelligent model can help investors and professional analysts reduce the risk of their investments. As Deep Learning models have been extensively studied in recent years, several studies have explored these techniques to predict stock prices using historical data and technical indicators. However, as the objective is to generate forecasts for the financial market, it is essential to validate the model through profitability metrics and model performance. Therefore, this systematic review focuses on Deep Learning models implemented for stock market forecasting using technical analysis. Discussions were made based on four main points of view: predictor techniques, trading strategies, profitability metrics, and risk management. This study showed that the LSTM technique is widely applied in this scenario (73.5%). This work significant contribution is to highlight some limitations found in the literature, such as only 35.3% of the studies analysed profitability, and only two articles implemented risk management. Therefore, despite the widely explored theme, there are still interesting open areas for research and development. INDEX TERMS Deep learning, profitability metrics, risk management, stock market forecasting, systematic review, technical analysis, technical indicators.

Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria through a lengthy and time-consuming process. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis. In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy and reliability. Therefore, we conducted a systematic review and meta-analysis to summarize the available evidence in the literature so far. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for ANN classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seems promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7% versus 72.8%). Finally, our analysis showed AUC values between acceptable and excellent. Still, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that may lead to significant impairment in social communication, repetitive patterns of behavior, and possible fixed and restricted interests. Applied Behavior Analysis (ABA) is a well-supported and evidence-based treatment for the delays attributed to ASD. Assistive technologies, such as gamification, software apps, computerbased training (Web), and robotics; provide a standardized method of implementing ABA techniques. This review provides a synthesis of the main characteristics of these technologies. The assessed proposals focused on technologies such as Distributed Systems, Image Processing, Gamification, and Robotics. The primary objectives of these tools sought to improve social behavior, attention, communication, and reading skills. Some common limitations found in the literature was a failure to accurately define their target audience, and a failure to comply with the dimensions of ABA as defined by Baer, Wolf, and Risley in 1968. INDEX TERMS Autism spectrum disorder, applied behavior analysis, assistive technologies.

No abstract

No abstract

No abstract