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Article

Hui Liu, Tinglong Tang, Jake Luo, Meng Zhao, Baole Zheng and Yirong Wu

This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns…

Abstract

Purpose

This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns are very rare under this condition.

Design/methodology/approach

The authors propose a new model with double encoder–decoder (DED) generative adversarial networks to detect anomalies when the model is trained without any abnormal patterns. The DED approach is used to map high-dimensional input images to a low-dimensional space, through which the latent variables are obtained. Minimizing the change in the latent variables during the training process helps the model learn the data distribution. Anomaly detection is achieved by calculating the distance between two low-dimensional vectors obtained from two encoders.

Findings

The proposed method has better accuracy and F1 score when compared with traditional anomaly detection models.

Originality/value

A new architecture with a DED pipeline is designed to capture the distribution of images in the training process so that anomalous samples are accurately identified. A new weight function is introduced to control the proportion of losses in the encoding reconstruction and adversarial phases to achieve better results. An anomaly detection model is proposed to achieve superior performance against prior state-of-the-art approaches.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

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Article

Kwonsang Sohn, Christine Eunyoung Sung, Gukwon Koo and Ohbyung Kwon

This study examines consumers' evaluations of product consumption values, purchase intentions and willingness to pay for fashion products designed using generative

Abstract

Purpose

This study examines consumers' evaluations of product consumption values, purchase intentions and willingness to pay for fashion products designed using generative adversarial network (GAN), an artificial intelligence technology. This research investigates differences between consumers' evaluations of a GAN-generated product and a non-GAN-generated product and tests whether disclosing the use of GAN technology affects consumers' evaluations.

Design/methodology/approach

Sample products were developed as experimental stimuli using cycleGAN. Data were collected from 163 members of Generation Y. Participants were assigned to one of the three experimental conditions (i.e. non-GAN-generated images, GAN-generated images with disclosure and GAN-generated images without disclosure). Regression analysis and ANOVA were used to test the hypotheses.

Findings

Functional, social and epistemic consumption values positively affect willingness to pay in the GAN-generated products. Relative to non-GAN-generated products, willingness to pay is significantly higher for GAN-generated products. Moreover, evaluations of functional value, emotional value and willingness to pay are highest when GAN technology is used, but not disclosed.

Originality/value

This study evaluates the utility of GANs from consumers' perspective based on the perceived value of GAN-generated product designs. Findings have practical implications for firms that are considering using GANs to develop products for the retail fashion market.

Details

International Journal of Retail & Distribution Management, vol. 49 no. 1
Type: Research Article
ISSN: 0959-0552

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Article

Shuangshuang Liu and Xiaoling Li

Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In…

Abstract

Purpose

Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In order to solve such problems, the purpose of this paper is to propose a novel image super-resolution algorithm based on improved generative adversarial networks (GANs) with Wasserstein distance and gradient penalty.

Design/methodology/approach

The proposed algorithm first introduces the conventional GANs architecture, the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction (SRWGANs-GP). In addition, a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction. The content loss is extracted from the deep model’s feature maps, and such features are introduced to calculate mean square error (MSE) for the loss calculation of generators.

Findings

To validate the effectiveness and feasibility of the proposed algorithm, a lot of compared experiments are applied on three common data sets, i.e. Set5, Set14 and BSD100. Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence. Compared with the baseline deep models, the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction. The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture.

Originality/value

Compared with the state-of-the-art algorithms, the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Article

Montek Singh, Utkarsh Bajpai, Vijayarajan V. and Surya Prasath

There are various style options available when one buys clothes on online shopping websites, however the availability the new fashion trends or choices require further…

Abstract

Purpose

There are various style options available when one buys clothes on online shopping websites, however the availability the new fashion trends or choices require further user interaction in generating fashionable clothes. The paper aims to discuss this issue.

Design/methodology/approach

Based on generative adversarial networks (GANs) from the deep learning paradigm, here the authors suggest model system that will take the latest fashion trends and the clothes bought by users as input and generate new clothes. The new set of clothes will be based on trending fashion but at the same time will have attributes of clothes where were bought by the consumer earlier.

Findings

In the proposed machine learning based approach, the clothes generated by the system will personalized for different types of consumers. This will help the manufacturing companies to come up with the designs, which will directly target the customer.

Research limitations/implications

The biggest limitation of the collected data set is that the clothes in the two domains do not belong to a specific category. For instance the vintage clothes data set has coats, dresses, skirts, etc. These different types of clothes are not segregated. Also there is no restriction on the number of images of each type of cloth. There can many images of dresses and only a few for the coats. This can affect the end results. The aim of the paper was to find whether new and desirable clothes can be created from two different domains or not. Analyzing the impact of “the number of images for each class of cloth” is something which is aim to work in future.

Practical implications

The authors believe such personalized experience can increase the sales of fashion stores and here provide the feasibility of such a clothes generation system.

Originality/value

Applying GANs from the deep learning models for generating fashionable clothes.

Details

International Journal of Clothing Science and Technology, vol. 32 no. 2
Type: Research Article
ISSN: 0955-6222

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Book part

Hongming Wang, Ryszard Czerminski and Andrew C. Jamieson

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business…

Abstract

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.

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Article

Abdelhak Belhi, Abdelaziz Bouras, Abdulaziz Khalid Al-Ali and Sebti Foufou

Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially…

Abstract

Purpose

Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning.

Design/methodology/approach

This framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning.

Findings

The authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks.

Originality/value

This research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors’ knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

Keywords

Content available
Book part

Abstract

Details

The Machine Age of Customer Insight
Type: Book
ISBN: 978-1-83909-697-6

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Article

Azra Nazir, Roohie Naaz Mir and Shaima Qureshi

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine…

Abstract

Purpose

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

Design/methodology/approach

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

Findings

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

Originality/value

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Abstract

Details

The Business of Choice: How Human Instinct Influences Everyone’s Decisions
Type: Book
ISBN: 978-1-83982-071-7

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Article

Dimitrios Sakkos, Edmond S. L. Ho, Hubert P. H. Shum and Garry Elvin

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA…

Abstract

Purpose

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.

Design/methodology/approach

In our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.

Findings

Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.

Originality/value

Such data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

Keywords

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