Search

Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.

The use of information communication technologies (ICT) in education brings up new possibilities of promoting learning and health experiences. In this sense, education contexts of the 21st century must consider these two areas of knowledge, especially their integration. This article presents learning practices developed with mobile devices and games in order to improve learning and well-being in children and adolescents undergoing cancer treatment in a non-formal educational setting. The methodology is based on qualitative case studies with content-based data analyses, involving informal interviews and observation methods. The study considers data from five patients who participated in the research between 2015 and 2019. The results demonstrate a positive influence of the practices with mobile technologies and games in terms of learning and in the well-being experience of patients during the treatment.

This study addresses the critical challenge of proactive anomaly detection and efficient resource management in infrastructure observability. Introducing an innovative approach to infrastructure monitoring, this work integrates machine learning models into observability platforms to enhance real-time monitoring precision. Employing a microservices architecture, the proposed system facilitates swift and proactive anomaly detection, addressing the limitations of traditional monitoring methods that often fail to predict potential issues before they escalate. The core of this system lies in its predictive models that utilize Random Forest, Gradient Boosting, and Support Vector Machine algorithms to forecast crucial metric behaviors, such as CPU usage and memory allocation. The empirical results underscore the system's efficacy, with the GradientBoostingRegressor model achieving an R² score of 0.86 for predicting request rates, and the RandomForestRegressor model significantly reducing the Mean Squared Error by 2.06% for memory usage predictions compared to traditional monitoring methods. These findings not only demonstrate the potential of machine learning in enhancing observability but also pave the way for more resilient and adaptive infrastructure management. [ABSTRACT FROM AUTHOR]

The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient's profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient's no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law. [ABSTRACT FROM AUTHOR]

Blockchain technology provides a tremendous opportunity to transform current personal health record (PHR) systems into a decentralised network infrastructure. However, such technology possesses some drawbacks, such as issues in privacy and storage capacity. Given its transparency and decentralised features, medical data are visible to everyone on the network and are inappropriate for certain medical applications. By contrast, storing vast medical data, such as patient medical history, laboratory tests, X-rays, and MRIs, significantly affect the repository storage of blockchain. This study bridges the gap between PHRs and blockchain technology by offloading the vast medical data into the InterPlanetary File System (IPFS) storage and establishing an enforced cryptographic authorisation and access control scheme for outsourced encrypted medical data. The access control scheme is constructed on the basis of the new lightweight cryptographic concept named smart contract-based attribute-based searchable encryption (SC-ABSE). This newly cryptographic primitive is developed by extending ciphertext-policy attribute-based encryption (CP-ABE) and searchable symmetric encryption (SSE) and by leveraging the technology of smart contracts to achieve the following: (1) efficient and secure fine-grained access control of outsourced encrypted data, (2) confidentiality of data by eliminating trusted private key generators, and (3) multikeyword searchable mechanism. Based on decisional bilinear Diffie–Hellman hardness assumptions (DBDH) and discrete logarithm (DL) problems, the rigorous security indistinguishability analysis indicates that SC-ABSE is secure against the chosen-keyword attack (CKA) and keyword secrecy (KS) in the standard model. In addition, user collusion attacks are prevented, and the tamper-proof resistance of data is ensured. Furthermore, security validation is verified by simulating a formal verification scenario using Automated Validation of Internet Security Protocols and Applications (AVISPA), thereby unveiling that SC-ABSE is resistant to man-in-the-middle (MIM) and replay attacks. The experimental analysis utilised real-world datasets to demonstrate the efficiency and utility of SC-ABSE in terms of computation overhead, storage cost and communication overhead. The proposed scheme is also designed and developed to evaluate throughput and latency transactions using a standard benchmark tool known as Caliper. Lastly, simulation results show that SC-ABSE has high throughput and low latency, with an ultimate increase in network life compared with traditional healthcare systems. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare there are no competing interests., (©2024 Sabino Soares et al.)

The growing technological advance is causing constant business changes. The continual uncertainties in project management make requirements engineering essential to ensure the success of projects. The usual exponential increase of stakeholders throughout the project suggests the application of intelligent tools to assist requirements engineers. Therefore, this article proposes Nhatos, a computational model for ubiquitous requirements management that analyses context histories of projects to recommend reusable requirements. The scientific contribution of this study is the use of the similarity analysis of projects through their context histories to generate the requirement recommendations. The implementation of a prototype allowed to evaluate the proposal through a case study based on real scenarios from the industry. One hundred fifty-three software projects from a large bank institution generated context histories used in the recommendations. The experiment demonstrated that the model achieved more than 70% stakeholder acceptance of the recommendations., Competing Interests: The authors declare there are no competing interests., (©2022 Lima et al.)