Applied Behavior Analysis for the Treatment of Autism: A Systematic Review of Assistive Technologies

Alves, Fábio Junior; Carvalho, Emerson; Aguilar, Juliana; Lacerda, Lucelmo; Bastos, Guilherme Sousa

doi:10.1109/access.2020.3005296

Cited by 47 publications

(21 citation statements)

References 44 publications

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“…According to a recent review on ABA therapy for autism [ 39 ], there were a few existing cases where technologies were applied in ABA practices. These include the use of robots [ 40 , 41 ], avatars [ 42 ], computer-based didactic software [ 43 ], and augmented reality [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Internet of Things (IoT)-Enhanced Applied Behavior Analysis (ABA) for Special Education Needs

Wong

Chan

Yum

et al. 2021

Sensors

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Applied behavior analysis (ABA) has become a popular behavioral therapy in the special education needs (SEN) community. ABA is used to manage SEN students’ behaviors by solving problems in socially important settings, and puts emphasis on having precise measurements on physical and observable events. In this work, we present how Internet of Things (IoT) technologies can be applied to enhance ABA therapy in normal SEN classroom settings. We measured (1) learning performance data, (2) learners’ physiological data, and (3) learning environment sensors’ data. Upon preliminary analysis, we have found that learners’ physiological data is highly diverse, while learner performance seems to be related to learners’ electrodermal activity. Our preliminary findings suggest the possibility of enhancing ABA for SEN with IoT technologies.

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Section: Discussionmentioning

confidence: 99%

Internet of Things (IoT)-Enhanced Applied Behavior Analysis (ABA) for Special Education Needs

Wong

Chan

Yum

et al. 2021

Sensors

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“…Furthermore, only recent articles were accepted (2010-2021). An association of these criteria was employed to determine when a specific work would be included or excluded from the systematic review [16].…”

Section: Qc1mentioning

confidence: 99%

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Bernardes

Santos

Abreu

et al. 2022

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The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

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“…In this way, three publications were excluded and the remaining 34 will be analysed in the next step. Figure 1, based on [33], illustrates the number of articles separated during the described process.…”

Section: B Conducting the Reviewmentioning

confidence: 99%

Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Bastos

2020

IEEE Access

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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.

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Applied Behavior Analysis for the Treatment of Autism: A Systematic Review of Assistive Technologies

Cited by 47 publications

References 44 publications

Internet of Things (IoT)-Enhanced Applied Behavior Analysis (ABA) for Special Education Needs

Internet of Things (IoT)-Enhanced Applied Behavior Analysis (ABA) for Special Education Needs

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

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