Explorative Visualization of Food Data to Raise Awareness of Nutritional Value

Lunterova, Anna; Špetko, Ondrej; Palamas, George

doi:10.1007/978-3-030-30033-3_14

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…This is the first visualization study which was undertaken using t-SNE with the Standard Tables of Food Composition in Japan, 2020 (Eighth Revised Edition). Furthermore, this study showed that visualization by two-dimensional mapping was possible based on nutrient information in food-composition tables, as shown in a previous study [15]. Previous research using PCA has shown that nuts, seeds, and pulses were well separated; however, fruits and vegetables were not separated adequately [13].…”

Section: Discussionsupporting

confidence: 70%

“…However, the results of this study showed that these foods were well-separated. In addition, as shown in a previous study that indicated that t-SNE was better than PCA for the visualization of food tables [15], this study using t-SNE showed that it was possible to classify many foods into distinct food groups by focusing on the nutrients in the dietary reference intake for the Japanese population.…”

Section: Discussionmentioning

confidence: 63%

“…Recently, a new technique called the t-distributed stochastic neighbor embedding (t-SNE) was developed for visualizing high-dimensional data [14]. A previous study showed that t-SNE performs better than PCA in visualizing a food-composition table [15]. However, food classification based on nutrient values has not yet been performed using the t-SNE method.…”

Section: Introductionmentioning

confidence: 99%

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Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping

Wakayama,

Takasugi,

Honda

et al. 2023

Nutrients

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Worldwide, several food-based dietary guidelines, with diverse food-grouping methods in various countries, have been developed to maintain and promote public health. However, standardized international food-grouping methods are scarce. In this study, we used two-dimensional mapping to classify foods based on their nutrient composition. The Standard Tables of Food Composition in Japan were used for mapping with a novel technique—t-distributed stochastic neighbor embedding—to visualize high-dimensional data. The mapping results showed that most foods formed food group-based clusters in the Standard Tables of Food Composition in Japan. However, the beverages did not form large clusters and demonstrated scattered distribution on the map. Green tea, black tea, and coffee are located within or near the vegetable cluster whereas cocoa is near the pulse cluster. These results were ensured by the k-nearest neighbors. Thus, beverages made from natural materials can be categorized based on their origin. Visualization of food composition could enable an enhanced comprehensive understanding of the nutrients in foods, which could lead to novel aspects of nutrient-value-based food classifications.

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Section: Discussionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

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Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping

Wakayama,

Takasugi,

Honda

et al. 2023

Nutrients

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“…Algeciras-Rodriguez (2018) 43 utilized self-organizing maps (Miljkovi, 2017) 44 to produce hybrid forms that acquired characteristics from several input references. Dimensionality reduction tools were used for design data visualizations by Meng et al (2020) 45 and Lunterova (2019) 46 , while Harding (2016) 47 and Lunterova (2022) 48 used these for generative design exploration.…”

Section: Architectural Design and Machine Learningmentioning

confidence: 99%

Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks

Pouliou

Horvath

Palamas

2023

International Journal of Architectural Computing

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The process of architectural design aims at solving complex problems that have loosely defined formulations, no explicit basis for terminating the problem-solving activity, and where no ideal solution can be achieved. This means that design problems, as wicked problems, sit in a space between incompleteness and precision. Applying digital tools in general and artificial intelligence in particular to design problems will then mediate solution spaces between incompleteness and precision. In this paper, we present a study where we employed machine learning algorithms to generate conceptual architectural forms for site-specific regulations. We created an annotated dataset of single-family homes and used it to train a 3D Generative Adversarial Network that generated annotated point clouds complying with site constraints. Then, we presented the framework to 23 practitioners of architecture in an attempt to understand whether this framework could be a useful tool for early-stage design. We make a three-fold contribution: First, we share an annotated dataset of architecturally relevant 3D point clouds of single-family homes. Next, we present and share the code for a framework and the results from training the 3D generative neural network. Finally, we discuss machine learning and creative work, including how practitioners feel about the emergence of these tools as mediators between incompleteness and precision in architectural design.

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GrainSynth: A Generative Synthesis Tool Based on Spatial Interpretations of Sound Samples

Vasileiou

Tenera

Papageorgiou

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Explorative Visualization of Food Data to Raise Awareness of Nutritional Value

Cited by 5 publications

References 17 publications

Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping

Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping

Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks

GrainSynth: A Generative Synthesis Tool Based on Spatial Interpretations of Sound Samples

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