Over the past decade, there has been a steady increase in health security breaches. Therefore, healthcare organizations must protect their sensitive information such as test results, diagnoses, prescriptions, research...
Over the past decade, there has been a steady increase in health security breaches. Therefore, healthcare organizations must protect their sensitive information such as test results, diagnoses, prescriptions, research, and customer personal information. A leak of sensitive data can result in significant economic loss and damage to the organization’s image. In this sense, Data Leakage Prevention (DLP) systems can help to identify, monitor, protect, and reduce the risks of leaking sensitive data. However, state-of-the-art DLP solutions only use signature comparisons and static comparisons. Therefore, we propose to develop the ARTERIAL model based on Natural Language Processing (NLP), Entity Recognition (NER), and Artificial Neural Networks (ANN) to be more assertive in extracting information and recognizing entities from Electronic Health Records (EHR). Different from the current literature, ARTERIAL considers semantic features present in the EHR. Three approaches were implemented and tested, two based on ANN and the following based on machine learning algorithms. As a result, the approach taken in its implementation using a machine learning algorithm reached 98.0% of Precision, 86.0% of Recall, and 91.0% of F1-Score.
Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact ma...
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ISBN:
(数字)9798350356632
ISBN:
(纸本)9798350356649
Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact maps can significantly enhance the exploration of the search space. In this study, an evolutionary algorithm is introduced, which incorporates such problem information for protein structure prediction. The proposed method employs a dynamic speciation technique alongside fragment insertion to foster population diversity. To ensure a rich variety of fragments, a fragment library is constructed using the Rosetta Quota protocol. Additionally, information from contact maps and secondary structure is integrated into two selection strategies to facilitate a more thorough exploration of the conformational search space. The results of an experimental evaluation involving 9 proteins are presented, demonstrating competitive performance compared to existing literature. Evaluation metrics include RMSD, GDT, and processing time.
Computer games continue to present challenging experimental fields for developing Artificial Intelligence (AI) models. With Case-Based Reasoning and Clustering, this work proposes novel cases and clusters-based reuse ...
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Language differences are one of the obstacles to communication. Especially if you use certain regional languages such as Banjar Language. Banjar Language is the mother tongue used by the Banjar tribe in South Kalimant...
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Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due...
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Serverless computing, also known as Function as a Service, is a new paradigm that aims to separate the user of the platform from details about any infrastructure deployment. The problem lies in the fact that all the c...
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Today, streaming, Artificial Intelligence, and the Internet of Things (IoT) are being some of the main drivers to accelerate process automation in various companies. These technologies are often connected to critical ...
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In software development, code autocomplete can be an essential tool in order to accelerate coding. However, many of these tools built into the IDEs are limited to suggesting only methods or arguments, often presenting...
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Global network alignment is the computational problem of determining the similarity between nodes of different networks to establish a one-to-one correspondence between them. It has important applications in the biolo...
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The extensive exploration of the Low Earth Orbit (LEO) has created a dangerous spacial environment, where space debris has threatened the feasibility of future operations. In this sense, Active Debris Removal (ADR) mi...
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