Rachel K. Fischer, Aubrey Iglesias, Alice L. Daugherty and Zhehan Jiang
The article presents a methodology that can be used to analyze data from the transaction log of EBSCO Discovery Service searches recorded in Google Analytics. It explains the…
Abstract
Purpose
The article presents a methodology that can be used to analyze data from the transaction log of EBSCO Discovery Service searches recorded in Google Analytics. It explains the steps to follow for exporting the data, analyzing the data, and recreating searches. The article provides suggestions to improve the quality of research on the topic. It also includes advice to vendors on improving the quality of transaction log software.
Design/methodology/approach
Case study
Findings
Although Google Analytics can be used to study transaction logs accurately, vendors still need to improve the functionality so librarians can gain the most benefit from it.
Research limitations/implications
The research is applicable to the usage of Google Analytics with EBSCO Discovery Service.
Practical implications
The steps presented in the article can be followed as a step-by-step guide to repeating the study at other institutions.
Social implications
The methodology in this article can be used to assess how library instruction can be improved.
Originality/value
This article provides a detailed description of a transaction log analysis process that other articles have not previously described. This includes a description of a methodology for accurately calculating statistics from Google Analytics data and provides steps for recreating accurate searches from data recorded in Google Analytics.
Details
Keywords
Zhehan Jiang and Richard Carter
This paper aims to provide two real examples to inspire librarians to use modern techniques for data visualization.
Abstract
Purpose
This paper aims to provide two real examples to inspire librarians to use modern techniques for data visualization.
Design/methodology/approach
Two interactive applications were created for visualizing longitudinal and geographical data collected by libraries.
Findings
R language has high versatility and flexibility in visualizing various types and hierarchy of data under a prevalent Web framework. The two demonstrations provide workflows from data extractions to visualization products.
Originality/value
Proper visualization assists librarians and administrators to understand data better and gain more insightful prospects. As a result, more scientific decisions can be made for improving libraries operation.
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Keywords
Yongqing Ma, Yifeng Zheng, Wenjie Zhang, Baoya Wei, Ziqiong Lin, Weiqiang Liu and Zhehan Li
With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its…
Abstract
Purpose
With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.
Design/methodology/approach
To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.
Findings
Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.
Originality/value
This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.
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Prashant Anerao, Atul Kulkarni and Yashwant Munde
This paper aims to investigate the current state of biocomposites used in fused deposition modelling (FDM) with a focus on their mechanical characteristics.
Abstract
Purpose
This paper aims to investigate the current state of biocomposites used in fused deposition modelling (FDM) with a focus on their mechanical characteristics.
Design/methodology/approach
The study presents a variety of biocomposite materials that have been used in filaments for 3D printing by different researchers. The process of making filaments is then described, followed by a discussion of the process parameters associated with the FDM.
Findings
To achieve better mechanical properties of 3D-printed parts, it is essential to optimize the process parameters of FDM while considering the characteristics of the biocomposite material. Polylactic acid is considered the most promising matrix material due to its biodegradability and lower cost. Moreover, the use of natural fibres like hemp, flax and sugarcane bagasse as reinforcement to the polymer in FDM filaments improves the mechanical performance of printed parts.
Originality/value
The paper discusses the influence of critical process parameters of FDM like raster angle, layer thickness, infill density, infill pattern and extruder temperature on the mechanical properties of 3D-printed biocomposite.