Pattern Recognition in Latin America in the “Big Data” Era

Fernández, Alicia; Gómez, Álvaro; Lecumberry, Federico; Pardo, Alberto; Ramírez, Ignacio Méndez

doi:10.1016/j.patcog.2014.04.012

Cited by 36 publications

(11 citation statements)

References 132 publications

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“…It is interesting to note that the T2FIS with five input variables achieved the worst result. As already stated in the introduction of this paper, this can be explained by the fact that introducing more variables in the model does not necessarily lead to better results [8][9][10]. However, to confirm the conclusions obtained, we performed the paired t-test to examine whether the difference in results between T2FIS structures is statistically significant (Table 5).…”

Section: Resultsmentioning

confidence: 63%

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Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

et al. 2020

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A constantly increasing number of deaths on roads forces analysts to search for models that predict the driver’s propensity for road traffic accidents (RTAs). This paper aims to examine a relationship between the speed and space assessment capabilities of drivers in terms of their association with the occurrence of RTAs. The method used for this purpose is based on the implementation of the interval Type-2 Fuzzy Inference System (T2FIS). The inputs to the first T2FIS relate to the speed assessment capabilities of drivers. These capabilities were measured in the experiment with 178 young drivers, with test speeds of 30, 50, and 70 km/h. The participants assessed the aforementioned speed values from four different observation positions in the driving simulator. On the other hand, the inputs of the second T2FIS are space assessment capabilities. The same group of drivers took two types of space assessment tests—2D and 3D. The third considered T2FIS sublimates of all previously mentioned inputs in one model. The output in all three T2FIS structures is the number of RTAs experienced by a driver. By testing three proposed T2FISs on the empirical data, the result of the research indicates that the space assessment characteristics better explain participation in RTAs compared to the speed assessment capabilities. The results obtained are further confirmed by implementing a multiple regression analysis.

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Section: Resultsmentioning

confidence: 63%

“…However, it is known that "more is not always better", especially for the prediction of RTAs, considering that a large number of variables may cause model overfitting [8,9]. In addition, this can impact accompanying activities such as long execution time and unreliable prediction results [8,10].…”

Section: Introductionmentioning

confidence: 99%

Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

et al. 2020

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“…However, there is still a long way to go, 29 especially in Latin America, where this type of technology is underdeveloped in the areas of natural sciences and health. [30][31][32] Even with the benefits they offer, these techniques have limitations, including the lack of quality standards and validation methods for some of their records, as they may be incomplete, inconsistent, and subject to a great deal of potential bias and confusion. On the other hand, the use of massive amounts of data may cause an existing relationship to go undetected due to the masking or dilution of a phenomenon.…”

Section: Big Data In the Health Areamentioning

confidence: 99%

Big data, farmacoepidemiología y farmacovigilancia

et al. 2020

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Big data is a term that comprises a group of technological tools capable of processing extremely large heterogeneous data sets, which are continuously collected and are available to be used at any time, and, therefore, constitutes a source of scientific evidence production.In the pharmacoepidemiology field, analyses made using these data sets may result in the development of pharmacological therapies that are more efficient, less expensive, and have a lower occurrence rate of adverse reactions. Likewise, the use of tools such as Text Mining or Machine Learning has led to major advances in pharmacoepidemiology and pharmacovigilance areas, so it is likely that these tools will be increasingly used over time.

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“…At the same time, there are challenges linked with deployment of big data. For instance, data storage is a complex process and there are concomitant computational challenges (Fernandez et al , 2015). Thus, there are numerous challenges of analyzing big data (Chen and Zhang, 2014).…”

Section: Big Data: Literature Reviewmentioning

confidence: 99%

Privacy concerns in integrating big data in “e-Oman”

Saxena

2017

JICES

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Purpose Whereas integration of big data in “e-Oman” – the e-government face of Oman – is a significant prospect, this paper aims to underscore the challenges of privacy concerns in effecting such integration. Design/methodology/approach Providing a brief description about the concepts of e-government and big data, the paper follows a discussion on “e-Oman”. While drawing a framework for integration of big data in “e-Oman”, the paper throws light on the privacy concerns in effecting such an integration following a qualitative approach. Findings Integration of big data in “e-Oman” is fraught with ethical challenges in terms of privacy issues which have social implications. Practical implications While the pros of integration of big data in “e-Oman” are noteworthy, technical and administrative constraints vis-à-vis the cons of such integration need to be taken care of. It may be underlined that there is a trade-off between the norms of transparency and privacy of individuals versus the deployment of big data in “e-Oman” for effective real-time problem-solving. Social implications Technical and administrative infrastructure needs to be robust for countering challenges of cybercrimes. Furthermore, confidentiality and privacy of individuals may be at stake; the government needs to tackle these issues. Originality/value Integration of big data in “e-Oman” needs to be probed, and this paper seeks to fill this gap. Second, the paper underscores that it is important that ethical norms are being accounted for while effecting this integration.

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Pattern Recognition in Latin America in the “Big Data” Era

Cited by 36 publications

References 132 publications

Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

Big data, farmacoepidemiología y farmacovigilancia

Privacy concerns in integrating big data in “e-Oman”

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