SPECTRA: Secure Power Efficient Clustered Topology Routing Algorithm

Wireless Sensor Networks (WSNs) have emerged as one of the hottest fields today due to their low-cost, self-organizing behavior, sensing ability in harsh environments, and their large application scope. One of the most challenging topics in WSNs is security. In some applications it is critical to provide confidentiality and authentication in order to prevent information from being compromised. However, providing key management for confidentiality and authentication is difficult due to the ad hoc nature, intermittent connectivity, and resource limitations of the network. Though traditional public keybased security protocols do exist, they need large memory bandwidths and complex algorithms, and are thus unsuitable for WSNs. Current solutions to the security issue in WSNs were created with only authentication and confidentiality in mind. This is far from optimal, because routing and security are closely correlated. Routing and security are alike because similar steps are taken in order to achieve these functions within a given network. Therefore, security and routing can be combined together in a cross-layer design, reducing the consumption of resources. The focus of this work is on the integration of routing and key management to provide an energy efficient security and routing solution. Towards this goal, this work proposes a security protocol that encompasses the following features: integration of security and routing, dynamic security, robust re-keying, low-complexity, and dual levels of encryption. This work combines all the robust features of current security implementations while adding additional features like dual layer encryption, resulting in an extremely efficient security protocol

Machine Learning for Child and Adolescent Health: A Systematic Review

CONTEXT: In the last few decades, data acquisition and processing has seen tremendous amount of growth, thus sparking interest in machine learning (ML) within the health care system. OBJECTIVE: Our aim for this review is to provide an evidence map of the current available evidence on ML in pediatrics and adolescent medicine and provide insight for future research. DATA SOURCES: A literature search was conducted by using Medline, the Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature Plus, Web of Science Library, and EBSCO Dentistry & Oral Science Source. STUDY SELECTION: Articles in which an ML model was assessed for the diagnosis, prediction, or management of any condition in children and adolescents (0-18 years) were included. DATA EXTRACTION: Data were extracted for year of publication, geographical location, age range, number of participants, disease or condition under investigation, study methodology, reference standard, type, category, and performance of ML algorithms. RESULTS: The review included 363 studies, with subspecialties such as psychiatry, neonatology, and neurology having the most literature. A majority of the studies were from high-income (82%; n = 296) and upper middle-income countries (15%; n = 56), whereas only 3% (n = 11) were from low middle-income countries. Neural networks and ensemble methods were most commonly tested in the 1990s, whereas deep learning and clustering emerged rapidly in the current decade. LIMITATIONS: Only studies conducted in the English language could be used in this review. CONCLUSIONS: The interest in ML has been growing across various subspecialties and countries, suggesting a potential role in health service delivery for children and adolescents in the years to come

Machine learning for child and adolescent health: A systematic review

Context: In the last few decades, data acquisition and processing has seen tremendous amount of growth, thus sparking interest in machine learning (ML) within the health care system.Objective: Our aim for this review is to provide an evidence map of the current available evidence on ML in pediatrics and adolescent medicine and provide insight for future research.Data sources: A literature search was conducted by using Medline, the Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature Plus, Web of Science Library, and EBSCO Dentistry & Oral Science Source.Study selection: Articles in which an ML model was assessed for the diagnosis, prediction, or management of any condition in children and adolescents (0-18 years) were included.Data extraction: Data were extracted for year of publication, geographical location, age range, number of participants, disease or condition under investigation, study methodology, reference standard, type, category, and performance of ML algorithms.Results: The review included 363 studies, with subspecialties such as psychiatry, neonatology, and neurology having the most literature. A majority of the studies were from high-income (82%; n = 296) and upper middle-income countries (15%; n = 56), whereas only 3% (n = 11) were from low middle-income countries. Neural networks and ensemble methods were most commonly tested in the 1990s, whereas deep learning and clustering emerged rapidly in the current decade.Limitations: Only studies conducted in the English language could be used in this review.Conclusions: The interest in ML has been growing across various subspecialties and countries, suggesting a potential role in health service delivery for children and adolescents in the years to come