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Article
Publication date: 19 June 2009

Brandie M. Stewart, Jessica M. Cipolla and Lisa A. Best

The purpose of this paper is to examine if university students could accurately extract information from graphs presented in 2D or 3D formats with different colour hue…

Abstract

Purpose

The purpose of this paper is to examine if university students could accurately extract information from graphs presented in 2D or 3D formats with different colour hue variations or solid black and white.

Design/methodology/approach

Participants are presented with 2D and 3D bar and pie charts in a PowerPoint presentation and are asked to extract specific information from the displays. A three (question difficulty) × two (graph type) × two (dimension) × two (colour) repeated measures ANOVA is conducted for both accuracy and reaction time.

Findings

Overall, 2D graphs led to better comprehension, particularly when complex information was presented. Accuracy was similar for colour and black and white graphs.

Practical implications

These results suggest that 2D graphs are preferable to 3D graphs, particularly when the task requires that the reader extract complex information.

Originality/value

For the past several decades, diagrams have been valuable additions to textual explanations in textbooks and in the classroom to teach various concepts. With an increase in technological advancements, many authors add extraneous features to their graphs to make them more aesthetically pleasing. This paper has shown, however, that 3D rendering may negatively affect graph comprehension.

Details

Campus-Wide Information Systems, vol. 26 no. 3
Type: Research Article
ISSN: 1065-0741

Keywords

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Article
Publication date: 1 June 1990

O.P. Gandhi and V.P. Agrawal

A method for qualitative estimation of reliability of large and complex mechanical and hydraulic systems is presented. It is especially useful for comparison and optimum…

Abstract

A method for qualitative estimation of reliability of large and complex mechanical and hydraulic systems is presented. It is especially useful for comparison and optimum selection of the structure at the conceptual stage of design when no other information about the salient features or parameters of the system is known. The method permits the identification and analysis of critical paths, loops and subsystems causing failure under different causes and modes. The method is based on graph theory and the graph variants proposed as reliability measures are also modified to yield realistic and useful results. The concept of system graph introduced in the article for dealing with large systems appears to be the most appropriate for analysis, comparison, selection and reliability estimates at the beginning of the system′s design.

Details

International Journal of Quality & Reliability Management, vol. 7 no. 6
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 1 November 2021

Maren Parnas Gulnes, Ahmet Soylu and Dumitru Roman

Neuroscience data are spread across a variety of sources, typically provisioned through ad-hoc and non-standard approaches and formats and often have no connection to the…

Abstract

Purpose

Neuroscience data are spread across a variety of sources, typically provisioned through ad-hoc and non-standard approaches and formats and often have no connection to the related data sources. These make it difficult for researchers to understand, integrate and reuse brain-related data. The aim of this study is to show that a graph-based approach offers an effective mean for representing, analysing and accessing brain-related data, which is highly interconnected, evolving over time and often needed in combination.

Design/methodology/approach

The authors present an approach for organising brain-related data in a graph model. The approach is exemplified in the case of a unique data set of quantitative neuroanatomical data about the murine basal ganglia––a group of nuclei in the brain essential for processing information related to movement. Specifically, the murine basal ganglia data set is modelled as a graph, integrated with relevant data from third-party repositories, published through a Web-based user interface and API, analysed from exploratory and confirmatory perspectives using popular graph algorithms to extract new insights.

Findings

The evaluation of the graph model and the results of the graph data analysis and usability study of the user interface suggest that graph-based data management in the neuroscience domain is a promising approach, since it enables integration of various disparate data sources and improves understanding and usability of data.

Originality/value

The study provides a practical and generic approach for representing, integrating, analysing and provisioning brain-related data and a set of software tools to support the proposed approach.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 4 October 2021

Guang-Yih Sheu and Chang-Yu Li

In a classroom, a support vector machines model with a linear kernel, a neural network and the k-nearest neighbors algorithm failed to detect simulated money laundering…

Abstract

Purpose

In a classroom, a support vector machines model with a linear kernel, a neural network and the k-nearest neighbors algorithm failed to detect simulated money laundering accounts generated from the Panama papers data set of the offshore leak database. This study aims to resolve this failure.

Design/methodology/approach

Build a graph attention network having three modules as a new money laundering detection tool. A feature extraction module encodes these input data to create a weighted graph structure. In it, directed edges and their end vertices denote financial transactions. Each directed edge has weights for storing the frequency of money transactions and other significant features. Social network metrics are features of nodes for characterizing an account’s roles in a money laundering typology. A graph attention module implements a self-attention mechanism for highlighting target nodes. A classification module further filters out such targets using the biased rectified linear unit function.

Findings

Resulted from the highlighting of nodes using a self-attention mechanism, the proposed graph attention network outperforms a Naïve Bayes classifier, the random forest method and a support vector machines model with a radial kernel in detecting money laundering accounts. The Naïve Bayes classifier produces second accurate classifications.

Originality/value

This paper develops a new money laundering detection tool, which outperforms existing methods. This new tool produces more accurate detections of money laundering, perfects warns of money laundering accounts or links and provides sharp efficiency in processing financial transaction records without being afraid of their amount.

Details

Journal of Money Laundering Control, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1368-5201

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Article
Publication date: 18 August 2021

Xiaoshuang Ma, Xixiang Liu, Chen-Long Li and Shuangliang Che

This paper aims to present a multi-source information fusion algorithm based on factor graph for autonomous underwater vehicles (AUVs) navigation and positioning to…

Abstract

Purpose

This paper aims to present a multi-source information fusion algorithm based on factor graph for autonomous underwater vehicles (AUVs) navigation and positioning to address the asynchronous and heterogeneous problem of multiple sensors.

Design/methodology/approach

The factor graph is formulated by joint probability distribution function (pdf) random variables. All available measurements are processed into an optimal navigation solution by the message passing algorithm in the factor graph model. To further aid high-rate navigation solutions, the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU measurements in the factor graph model.

Findings

The proposed factor graph was demonstrated both in a simulated and vehicle environment using IMU, Doppler Velocity Log, terrain-aided navigation, magnetic compass pilot and depth meter sensors. Simulation results showed that the proposed factor graph processes all available measurements into the considerably improved navigation performance, computational efficiency and complexity compared with the un-simplified factor graph and the federal Kalman filtering methods. Semi-physical experiment results also verified the robustness and effectiveness.

Originality/value

The proposed factor graph scheme supported a plug and play capability to easily fuse asynchronous heterogeneous measurements information in AUV navigation systems.

Details

Assembly Automation, vol. 41 no. 5
Type: Research Article
ISSN: 0144-5154

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Article
Publication date: 8 June 2021

Hui Yuan and Weiwei Deng

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods…

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Abstract

Purpose

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.

Design/methodology/approach

This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.

Findings

The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.

Originality/value

This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

Details

Internet Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 4 August 2021

Manjula T., Rajeswari R. and Praveenkumar T.R.

The purpose of this paper is to assess the application of graph coloring and domination to solve the airline-scheduling problem. Graph coloring and domination in graphs

Abstract

Purpose

The purpose of this paper is to assess the application of graph coloring and domination to solve the airline-scheduling problem. Graph coloring and domination in graphs have plenty of applications in computer, communication, biological, social, air traffic flow network and airline scheduling.

Design/methodology/approach

The process of merging the concept of graph node coloring and domination is called the dominator coloring or the χ_d coloring of a graph, which is defined as a proper coloring of nodes in which each node of the graph dominates all nodes of at least one-color class.

Findings

The smallest number of colors used in dominator coloring of a graph is called the dominator coloring number of the graph. The dominator coloring of line graph, central graph, middle graph and total graph of some generalized Petersen graph P_(n ,1) is obtained and the relation between them is established.

Originality/value

The dominator coloring number of certain graph is obtained and the association between the dominator coloring number and domination number of it is established in this paper.

Details

Aircraft Engineering and Aerospace Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1748-8842

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Book part
Publication date: 29 March 2016

Lasse Mertins and Lourdes Ferreira White

This study examines the impact of different Balanced Scorecard (BSC) formats (table, graph without summary measure, graph with a summary measure) on various decision…

Abstract

Purpose

This study examines the impact of different Balanced Scorecard (BSC) formats (table, graph without summary measure, graph with a summary measure) on various decision outcomes: performance ratings, perceived informativeness, and decision efficiency.

Methodology/approach

Using an original case developed by the researchers, a total of 135 individuals participated in the experiment and rated the performance of carwash managers in two different scenarios: one manager excelled financially but failed to meet targets for all other three BSC perspectives and the other manager had the opposite results.

Findings

The evaluators rated managerial performance significantly lower in the graph format compared to a table presentation of the BSC. Performance ratings were significantly higher for the scenario where the manager failed to meet only financial perspective targets but exceeded targets for all other nonfinancial BSC perspectives, contrary to the usual predictions based on the financial measure bias. The evaluators reported that informativeness of the BSC was highest in the table or graph without summary measure formats, and, surprisingly, adding a summary measure to the graph format significantly reduced perceived informativeness compared to the table format. Decision efficiency was better for the graph formats (with or without summary measure) than for the table format.

Originality/value

Ours is the first study to compare tables, graphs with and without a summary measure in the context of managerial performance evaluations and to examine their impact on ratings, informativeness, and efficiency. We developed an original case to test the boundaries of the financial measure bias.

Details

Advances in Management Accounting
Type: Book
ISBN: 978-1-78441-652-2

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Book part
Publication date: 10 July 2019

Tianxing Wu, Guilin Qi and Cheng Li

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and…

Abstract

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. Besides, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management, and so on. In recent years, knowledge graph techniques in China are also developing rapidly and different Chinese knowledge graphs have been built to support various applications. Under the background of “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China on developing knowledge graph is also a good reference. Thus, in this chapter, the authors mainly introduce the development of Chinese knowledge graphs and their applications. The authors first describe the background of OBOR, and then introduce the concept of knowledge graph and three typical Chinese knowledge graphs, including Zhishi.me, CN-DBpedia, and XLORE. Finally, the authors demonstrate several applications of Chinese knowledge graphs.

Details

The New Silk Road Leads through the Arab Peninsula: Mastering Global Business and Innovation
Type: Book
ISBN: 978-1-78756-680-4

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Article
Publication date: 22 July 2021

Sławomir Samolej, Grzegorz Dec, Dariusz Rzonca, Andrzej Majka and Tomasz Rogalski

The purpose of this study is to provide an alternative graph-based airspace model for more effective free-route flight planning.

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Abstract

Purpose

The purpose of this study is to provide an alternative graph-based airspace model for more effective free-route flight planning.

Design/methodology/approach

Based on graph theory and available data sets describing airspace, as well as weather phenomena, a new FRA model is proposed. The model is applied for near to optimal flight route finding. The software tool developed during the study and complexity analysis proved the applicability and timed effectivity of the flight planning approach.

Findings

The sparse bidirectional graph with edges connecting only (geographically) closest neighbours can naturally model local airspace and weather phenomena. It can be naturally applied to effective near to optimal flight route planning.

Research limitations/implications

Practical results were acquired for one country airspace model.

Practical implications

More efficient and applicable flight planning methodology was introduced.

Social implications

Aircraft following the new routes will fly shorter trajectories, which positively influence on the natural environment, flight time and fuel consumption.

Originality/value

The airspace model proposed is based on standard mathematical backgrounds. However, it includes the original airspace and weather mapping idea, as well as it enables to shorten flight planning computations.

Details

Aircraft Engineering and Aerospace Technology, vol. 93 no. 9
Type: Research Article
ISSN: 1748-8842

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