State of the art in image processing &amp; big data analytics: issues and challenges

Ezhilraman, S. Vahini; Srinivasan, Sujatha

doi:10.14419/ijet.v7i2.33.13885

Cited by 12 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, we were unable to develop suitable algorithms to follow Peirce's visual interpretation language due to difficulty in providing accurate interpretation of the meaning found in visuals, as understood through the lens of Peirce's theory. Indeed, according to Ezhilraman and Srinivasan (2018) the development of suitable algorithms and processes to evaluate and support the creation of visualizations presents an ongoing challenge.…”

Section: F Resultsmentioning

confidence: 99%

Big data visualization through the lens of Peirce’s visual sign theory

Friedman¹,

Thellefsen²

2022

Punctum

View full text Add to dashboard Cite

Data from social media platforms, such as Twitter and Facebook, are generated by people who produce, spread, share, or exchange multimedia content. Such content may include text, images, sounds, or videos. To derive insight into the behavior of social media users, researchers often use open-source technologies to visualize data and generate models for data analytics. One of the most popular open-source applications for managing and analyzing social media data is the open-source R programming language. Friedman and Feichtinger (2017) created an R package termed ‘Peirce’s sign theory R package’ to analyze data using Peirce’s principles of discovery. Though Peirce semiotics have been introduced in the context of computer programming languages, so far, no previous work has applied Peirce’s sign theory to data modelling of social media data. In this paper, we use Peirce’s sign theory R package as an overall framework to gain insight into data collected from Twitter. We assembled the data using Twitter’s Analytics algorithm, examined the relationships between variables, and visualized the results. Subsequently, we assessed the feasibility of analyzing those graphics using the triadic model set out by Jappy (2013) and Peirtarinen (2012) for the interpretation of visual signs. The study results showed that Peirce’s sign theory R package effectively analyzes and visualizes Big Data from social media feeds. However, due to complexities in both the social media data feeds and Peirce’s interpretation of meaning, as outlined by Jappy (2013) and Peirtarinen (2012), we were unable to develop algorithms that generate or suggest an interpretation of visual signs.

show abstract

Section: F Resultsmentioning

confidence: 99%

Big data visualization through the lens of Peirce’s visual sign theory

Friedman¹,

Thellefsen²

2022

Punctum

View full text Add to dashboard Cite

show abstract

“…This study's smoothing employed the mean and median smoothing methods because both methods have different calculation parameters. Mean, and median are essentially used in the filtering process for image processing in the field of image science [33][34] [35]. Figure 2 illustrates time series data smoothing preprocessing estimate of the number of distinct visitors to an online journal.…”

Section: Preprocessingmentioning

confidence: 99%

Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Wibawa

2023

J. Appl. Data Sci.

View full text Add to dashboard Cite

Unique visitors are first-time IP address visitors in a certain time window, a significant indicator of an electronic journal's performance and accreditation. This study uses a backpropagation neural network (BPNN) to improve visitor prediction. From January 1, 2018, to December 31, 2018, the KEDS.csv file on the page contained page views, sessions, visitors, and new visitors. The data is preprocessed using mean and median smoothing. MSE and RMSE are examined and compared to the BPNN model without smoothing and the original data. The BPNN model with mean smoothing, MSE 0.00129, and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture has the lowest error. Integrating mean and median smoothing techniques considerably enhances the BPNN forecasting model. Mean, and median smoothing lowers data volatility and noise, making predictions more accurate. The BPNN model with smoothing has lower MSE and RMSE than the model without smoothing and the original data. This work is unusual in combining mean and median smoothing with a BPNN model to predict unique visits to electronic journals. This research advances time series forecasting by predicting electronic journal visiting patterns. The literature benefits from evaluating smoothing strategies and their effects on predicting. The study helps electronic journal practitioners evaluate visitor patterns and journal performance by boosting prediction accuracy.

show abstract