Wemerson Delcio Parreira

A lightweight neural‐network classifier was developed to assign surface‐water samples to the five quality classes established by Brazil's environmental standard CONAMA 357/2005. The study used a 2018 dataset from SEMASA‐Itajaí comprising 8928 hourly observations of pH, turbidity, apparent color, and local rainfall. After removing outliers, applying weighted k‐nearest‐neighbor imputation and performing min–max normalization, the data were evaluated using a 5‐fold Stratified Cross‐Validation approach to ensure robust generalization. The network, implemented in TensorFlow, consists of two fully connected layers with 32 and 16 rectified linear units, followed by a softmax output layer. Training for 100 epochs with the Adam optimizer (learning rate 0.001) achieved an average validation accuracy of 97.63% (±0.86%), with misclassifications restricted to adjacent regulatory classes. Adding rainfall as an auxiliary feature and fine‐tuning the learning rate further increased accuracy by 0.7 percentage points and reduced training variance.

The ability to accurately predict future time series behavior in multiple steps, known as multi-horizon forecasting, is a vital aspect in various industries, including retail sales, energy consumption, server load, healthcare, weather, and others. We have proposed, in this paper, the use of statistical forecasters as covariates in a Deep Neural Network (DNN) model and evaluated its impact on forecast metrics. Our analysis covered four diverse datasets: M5, Stallion, Stock Market, and Synthetic. The results demonstrated that the inclusion of statistical predictors in the DNN model led to varying degrees of improvement in forecast performance, depending on the dataset and the chosen evaluation metric. In general, our findings suggest that incorporating statistical prediction as a covariate can be a valuable approach to improving multi-horizon prediction, especially in scenarios with data scarcity and intermittency. The hybrid model achieved consistent improvements, particularly on Symmetric Mean Absolute Percentage Error (SMAPE) across datasets, with statistically significant gains on synthetic and stock market series. Specifically, SMAPE was reduced by approximately 33% on synthetic and stock market datasets, by 15–20% on Stallion, and by around 6% on M5. These results confirm that integrating statistical forecasts as covariates can substantially enhance predictive accuracy, especially for volatile or synthetic series.

This paper presents the results of a structured scoping review (SSR) that explores the integration of the Internet of Things (IoT) and embedded systems in creating a sustainable and interconnected technological ecosystem. The study focuses on water quality monitoring, an area where these technologies have demonstrated significant potential. The SSR follows a meticulous methodology, covering planning, execution, and documentation stages to ensure a comprehensive and unbiased review of the existing literature. Key research questions guide the review, focusing on extracting and analyzing water sample characteristics, using machine learning algorithms for classification, and the technologies utilized in these systems. The search process involved multiple databases, yielding 343 articles, of which 8 met the stringent inclusion and exclusion criteria. The review highlights the widespread use of IoT for real-time data collection and artificial intelligence (AI) for analyzing complex patterns in water quality data. Our findings underscore the significance of temperature, pH, turbidity, and conductivity, commonly utilized in water classification. In addition, prevalent machine learning techniques for analyzing water quality data include K-Nearest Neighbors (KNN) and artificial neural networks (ANN). Despite the advances, challenges such as implementation costs, connectivity in remote areas, and the interpretability of AI models remain. This review underscores the transformative potential of IoT and AI in water quality monitoring, with implications for ensuring safe drinking water and sustainable water resource management.

The ability to accurately predict future time series behavior in multiple steps, known as multi-horizon forecasting, is a vital aspect in various industries, including retail sales, energy consumption, server load, healthcare, weather, and others. We have proposed, in this paper, the use of statistical forecasters as covariates in a Deep Neural Network (DNN) model and evaluated its impact on forecast metrics. Our analysis covered four diverse datasets: M5, Stallion, Stock Market, and Synthetic. The results demonstrated that the inclusion of statistical predictors in the DNN model led to varying degrees of improvement in forecast performance, depending on the dataset and the evaluation metric chosen. In general, our findings suggest that the incorporation of statistical prediction as a covariate can be a valuable approach to improving multi-horizon prediction, especially in scenarios with data scarcity and intermittence.

Objetivo: Este artigo apresenta uma revisão sistemática da literatura de trabalhos que apresentam algoritmos para aplicações de visão computacional (VC) para pessoas com deficiência visual. O objetivo é identificar esses estudos e entender o propósito de cada solução para o mapeamento de aplicações voltadas ao acesso a saúde digital. Método: Para o desenvolvimento deste trabalho foi conduzida uma revisão sistemática da literatura com uma busca nas principais bases de artigos científicos com acesso aberto. Resultados: Inicialmente encontrou-se 360 estudos, dos quais selecionou-se apenas seis artigos a partir dos critérios de inclusão e exclusão. Conclusão: Mostra-se a existência de pesquisas baseadas em VC para o desenvolvimento de dispositivos que atendem uma população com deficiência visual com diferentes funcionalidades. Porém, não há dentre os estudos encontrados trabalhos baseados em visão computacional para tecnologias que considere o acesso à saúde ou a redução das barreiras da acessibilidade para a saúde digital.

This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe.

This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe.

This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe.

This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe.