Context‐aware text classification system to improve the quality of text: A detailed investigation and techniques

Author(s):  
Zeeshan Saleem ◽  
Adi Alhudhaif ◽  
Kashif Naseer Qureshi ◽  
Gwanggil Jeon
Neurosurgery ◽  
2021 ◽  
Author(s):  
Kenny Yat Hong Kwan ◽  
J Naresh-Babu ◽  
Wilco Jacobs ◽  
Marinus de Kleuver ◽  
David W Polly ◽  
...  

Abstract BACKGROUND Existing adult spinal deformity (ASD) classification systems are based on radiological parameters but management of ASD patients requires a holistic approach. A comprehensive clinically oriented patient profile and classification of ASD that can guide decision-making and correlate with patient outcomes is lacking. OBJECTIVE To perform a systematic review to determine the purpose, characteristic, and methodological quality of classification systems currently used in ASD. METHODS A systematic literature search was conducted in MEDLINE, EMBASE, CINAHL, and Web of Science for literature published between January 2000 and October 2018. From the included studies, list of classification systems, their methodological measurement properties, and correlation with treatment outcomes were analyzed. RESULTS Out of 4470 screened references, 163 were included, and 54 different classification systems for ASD were identified. The most commonly used was the Scoliosis Research Society-Schwab classification system. A total of 35 classifications were based on radiological parameters, and no correlation was found between any classification system levels with patient-related outcomes. Limited evidence of limited quality was available on methodological quality of the classification systems. For studies that reported the data, intraobserver and interobserver reliability were good (kappa = 0.8). CONCLUSION This systematic literature search revealed that current classification systems in clinical use neither include a comprehensive set of dimensions relevant to decision-making nor did they correlate with outcomes. A classification system comprising a core set of patient-related, radiological, and etiological characteristics relevant to the management of ASD is needed.


2020 ◽  
Author(s):  
Diandre de Paula ◽  
Daniel Saraiva ◽  
Romeiro Natália ◽  
Nuno Garcia ◽  
Valderi Leithardt

With the growth of ubiquitous computing, context-aware computing-based applications are increasingly emerging, and these applications demonstrate the impact that context has on the adaptation process. From the context, it will be possible to adapt the application according to the requirements and needs of its users. Therefore, the quality of the context information must be guaranteed so that the application does not have an incorrect or unexpected adaptation process. But like any given data, there is the possibility of inaccuracy and/or uncertainty and so Quality of Context (QoC) plays a key role in ensuring the quality of context information and optimizing the adaptation process. To guarantee the Quality of Context it is necessary to study a quality model to be created, which will have the important function of evaluating the context information. Thus, it is necessary to ensure that the parameters and quality indicators to be used and evaluated are the most appropriate for a given type of application. This paper aims to study a context quality model for the UbiPri middleware, defining its quality indicators to ensure its proper functioning in the process of adaptation in granting access to ubiquitous environments. Keywords: QoC, Model, Context-Aware, Data, Privacy


Author(s):  
José Bringel Filho ◽  
Nazim Agoulmine

Ubiquitous Health (U-Health) smart homes are intelligent spaces capable of observing and correctly recognizing the activities and health statuses of their inhabitants (context) to provide the appropriate support to achieve an overall sense of health and well-being in their inhabitants’ daily lives. With the intrinsic heterogeneity and large number of sources of context information, aggregating and reasoning on low-quality raw sensed data may result in conflicting and erroneous evaluations of situations, affecting directly the reliability of the U-Health systems. In this environment, the evaluation and verification of Quality of Context (QoC) information plays a central role in improving the consistency and correctness of context-aware U-Health applications. Therefore, the objective of this chapter is to highlight the impact of QoC on the correct behavior of U-Health systems, and introduce and analyze the existing approaches of modeling, evaluating, and using QoC to improve its context-aware decision-making support.


2020 ◽  
Vol 54 (3) ◽  
pp. 113-123
Author(s):  
V. S. Egorov ◽  
E. S. Kozlova ◽  
K. E. Lomotin ◽  
O. V. Fedorets ◽  
A. V. Filimonov ◽  
...  

Author(s):  
Pascal Cuxac ◽  
Jean-Charles Lamirel ◽  
Maha Ghribi

Nous présentons une approche alternative pour l'évaluation de la qualité de classifications non supervisées de textes basée sur des critères de rappel, précision et F-mesure non supervisés, exploitant les descripteurs associées aux classes. La comparaison expérimentale du comportement des critères classiques avec notre approche est effectuée sur des données bibliographiques.This paper presents an alternative approach to measuring the quality of non-supervised text classification based on the recall, precision and non-supervised F-measure criteria, using class descriptors. The experimental comparison of classical criteria behaviour to our approach is based on bibliographic data.


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