Quality standards for data and metadata addressed to data science applications

Lemos, Daniela Lucas da Silva; Martins, Dalton Lopes; Carmo, Danielle do

doi:10.47909/anis.978-9916-9760-3-6.116

Cited by 4 publications

(3 citation statements)

References 6 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several techniques have been presented in literature for object detection process have been brunched toward stream relied [10], LSTM-dependent [5], consideration relied [22], following relied [13] with various approaches [12]. The primary aim of the suggested study is to recognize the objects in a still image with along the visual utilizing OpenCV, Python, utilizing the better YOLOv3 technique foe such study.…”

Section: Methodology and Aprproaches (Maa)mentioning

confidence: 99%

“…At first, video data recognition techniques have depended on high quality elements 20. Along the quick improvement of profound learning with convolutional neural organizations [21], profound learning schemes have been demonstrated to be further efficient than regular methodologies for different undertakings in PC view, discourse handling, and multimethodology waveform handling [22]. Various profound learning-based video data recognition techniques were created later the ILSVRC2015 threats.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Big Data Detection utilizing Cloud Networks with Video Vision Techniques

Ahmed

Aljuboori

2023

EAI Endorsed Scal Inf Syst

View full text Add to dashboard Cite

Regardless of the number of grounded object identification procedures reliant upon still pictures, their application to edge video information through the system hypothesis faces two drawbacks: (1) the deficit of computational throughput in view of abundance across picture follows or through the shortfall of usage of a transient and spatial relationship for parts across the edges of the image, and (ii) a shortfall of energy for authentic conditions, e.g., muddled turn of events and impediment. Since the Visual Recognition challenge has been by and large introduced, different methods have emerged recorded as a printed version around video object distinguishing proof, countless which have used significant learning norms. The mark of this assessment is to present a twofold framework for a total investigation of the principle methodologies of video object acknowledgment regardless the methodology of murkiness associations. It presents a chart of existing datasets for video object location close by appraisal estimations ordinarily used connected with fleecy frameworks organization methodologies. The video data acknowledgment advancements are then arranged and each one imparted. Two test tables are given to know the differences between them to the extent that accuracy and math ability. Finally, a couple of future examples in video object recognition have been believed to address embedded difficulties.

show abstract

Section: Methodology and Aprproaches (Maa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Big Data Detection utilizing Cloud Networks with Video Vision Techniques

Ahmed

Aljuboori

2023

EAI Endorsed Scal Inf Syst

View full text Add to dashboard Cite

show abstract

“…A pesar de las investigaciones realizadas por especialistas de diversos campos de las ciencias biomédicas, los tratamientos se limitan principalmente a la atención de apoyo y al tratamiento de la encefalitis aguda, sumado a que las opciones farmacológicas son insuficientes y no deberían tratarse como alternativas a las medidas de control de infecciones. (25)(26)(27) Por ello, es necesario generar más evidencia que incluyan los resultados sobre la profilaxis después de la exposición en personas que estuvieron contacto con casos confirmados de NiV, debido a que este patógeno viral constituye aún una amenaza epidemiológica y las alternativas desarrolladas para enfrentar los potenciales brotes que pueda originar aún son insuficientes.…”

Section: Conclusionesunclassified

Nipah virus: Analysis of the scientific production in Open Access on the Web of Science, 2000 – 2020

Rodríguez

Rivero

Díaz-Chieng

2023

Salud, Ciencia y Tecnología

View full text Add to dashboard Cite

Introduction: the Nipah virus causes severe disease with high mortality rates in humans. Since its discovery, it has been studied for its pandemic potential and lethality. Objective: analyze the scientific production on the Nipah virus, to identify its trend at an international level. Method: a descriptive, longitudinal and retrospective study of the scientific production related to the Nipah virus published in open access on the Web of Science Core Collection between 2000 and 2020 was carried out. The information search used the equation “Nipah NOT Hendra”, and of each record Information regarding the year of publication, authors with their institutional affiliation, type of publication and title of the journal (for articles), country of origin of the authors was extracted. VOSviewer 1.6.17 was used to map the collaboration networks between authors and countries, as well as the co-citation network and the concurrency of keywords. Results: 443 documents were recovered, where 79,23 % are articles and 99,79 % were published in English. The most productive sources are: Journal of Virology and Emerging Infections Diseases. A total of 1.724 authors were identified, observing a trend towards an increase in scientific production on the Nipah virus in the period analyzed, which allows the construction of theoretical frameworks that support strategies on this virus. Conclusions: the analysis carried out responds to the WHO priority on monitoring this pathogen, classified as one of those with the greatest potential to generate pandemic outbreaks and high morbidity and mortality.

show abstract

Development of a Classification Model for Predicting Student Payment Behavior Using Artificial Intelligence and Data Science Techniques

Villarreal-Torres,

Ángeles-Morales,

Marín-Rodriguez

et al. 2023

EAI Endorsed Scal Inf Syst

View full text Add to dashboard Cite

Artificial intelligence today has become a valuable tool for decision-making, where universities have to adapt and optimize their processes, improving the quality of their services. In this context, the economic income from collections is vital for sustainability. There are several problems that can contribute to student delinquency, such as economic, financial, academic, family, and personal. For this reason, the study aimed to develop a classification model to predict the payment behavior of enrolled students. The methodology is a proactive, technological study of incremental innovation with a synchronous temporal scope. The study population consisted of 8,495 undergraduate students enrolled in the 2022 - II academic semester, containing information on academic performance, financial situation, and personal factors. The result is a classification model using the H2O.ai platform, discretization algorithms, data balancing, and the R language. Data science algorithms obtained the base from the institution's computer system. The data sets for training and testing correspond to 70% and 30%, obtaining the GBM Grid model whose performance metrics are AUC of 0.905, AUCPR of 0.926, and logLoss equivalent to 0.311; that is, the model efficiently complies with the classification of student debtors to provide them with early intervention service and help them complete their studies.

show abstract

Quality standards for data and metadata addressed to data science applications

Cited by 4 publications

References 6 publications

Big Data Detection utilizing Cloud Networks with Video Vision Techniques

Big Data Detection utilizing Cloud Networks with Video Vision Techniques

Nipah virus: Analysis of the scientific production in Open Access on the Web of Science, 2000 – 2020

Development of a Classification Model for Predicting Student Payment Behavior Using Artificial Intelligence and Data Science Techniques

Contact Info

Product

Resources

About