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Artikel ; Online: ClsGAN: Selective Attribute Editing Model based on Classification Adversarial Network.

Liu, Ying / Fan, Heng / Ni, Fuchuan / Xiang, Jinhai

Neural networks : the official journal of the International Neural Network Society

2020 Band 133, Seite(n) 220–228

Abstract: Attribution editing has achieved remarkable progress in recent years owing to the encoder-decoder structure and generative adversarial network (GAN). However, it remains challenging to generate high-quality images with accurate attribute transformation. ... ...

Abstract	Attribution editing has achieved remarkable progress in recent years owing to the encoder-decoder structure and generative adversarial network (GAN). However, it remains challenging to generate high-quality images with accurate attribute transformation. Attacking these problems, the work proposes a novel selective attribute editing model based on classification adversarial network (referred to as ClsGAN) that shows good balance between attribute transfer accuracy and photo-realistic images. Considering that the editing images are prone to be affected by original attribute due to skip-connection in encoder-decoder structure, an upper convolution residual network (referred to as Tr-resnet) is presented to selectively extract information from the source image and target label. In addition, to further improve the transfer accuracy of generated images, an attribute adversarial classifier (referred to as Atta-cls) is introduced to guide the generator from the perspective of attribute through learning the defects of attribute transfer images. Experimental results on CelebA demonstrate that our ClsGAN performs favorably against state-of-the-art approaches in image quality and transfer accuracy. Moreover, ablation studies are also designed to verify the great performance of Tr-resnet and Atta-cls.
Mesh-Begriff(e)	Humans ; Neural Networks, Computer ; Pattern Recognition, Automated/classification ; Pattern Recognition, Automated/methods
Sprache	Englisch
Erscheinungsdatum	2020-11-10
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2020.10.019
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Supporting a circular economy: Insights from Taiwan's plastic waste sector and lessons for developing countries.

Wu, Ching-Yan / Hu, Mei-Chih / Ni, Fu-Chuan

Sustainable production and consumption

2020 Band 26, Seite(n) 228–238

Abstract: This study investigates how, in the process of industrialization, Taiwan successfully developed its plastic waste industry into an industrial-level circular economy by leveraging a network-based collective bricolage in conjunction with a framework of ... ...

Abstract	This study investigates how, in the process of industrialization, Taiwan successfully developed its plastic waste industry into an industrial-level circular economy by leveraging a network-based collective bricolage in conjunction with a framework of adaptive institutional governance. Three conclusions are made: industrialized manufacturing sectors are foundations upon which developing nations can accumulate endogenous social capabilities and can enable the emergence of network-based collective bricolages; for developing nations that are attempting to establish circular economies based on their endogenous small-to-medium enterprises, developing network-based collective bricolages in conjunction with adaptive institutional governance is an essential and effective strategy; and transitioning into green-related sectors can further drive economic development and lead to the creation of new ventures, businesses, and job opportunities while supporting the formation of a circular economy. The approach is especially relevant for developing countries starting their industrialization process and waste management initiatives with few resources.
Schlagwörter	covid19
Sprache	Englisch
Erscheinungsdatum	2020-10-10
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article
ISSN	2352-5509
ISSN (online)	2352-5509
DOI	10.1016/j.spc.2020.10.009
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Deep Learning-Based Segmentation of Peach Diseases Using Convolutional Neural Network.

Yao, Na / Ni, Fuchuan / Wu, Minghao / Wang, Haiyan / Li, Guoliang / Sung, Wing-Kin

Frontiers in plant science

2022 Band 13, Seite(n) 876357

Abstract: Peach diseases seriously affect peach yield and people's health. The precise identification of peach diseases and the segmentation of the diseased areas can provide the basis for disease control and treatment. However, the complex background and ... ...

Abstract	Peach diseases seriously affect peach yield and people's health. The precise identification of peach diseases and the segmentation of the diseased areas can provide the basis for disease control and treatment. However, the complex background and imbalanced samples bring certain challenges to the segmentation and recognition of lesion area, and the hard samples and imbalance samples can lead to a decline in classification of foreground class and background class. In this paper we applied deep network models (Mask R-CNN and Mask Scoring R-CNN) for segmentation and recognition of peach diseases. Mask R-CNN and Mask Scoring R-CNN are classic instance segmentation models. Using instance segmentation model can obtain the disease names, disease location and disease segmentation, and the foreground area is the basic feature for next segmentation. Focal Loss can solve the problems caused by difficult samples and imbalance samples, and was used for this dataset to improve segmentation accuracy. Experimental results show that Mask Scoring R-CNN with Focal Loss function can improve recognition rate and segmentation accuracy comparing to Mask Scoring R-CNN with CE loss or comparing to Mask R-CNN. When ResNet50 is used as the backbone network based on Mask R-CNN, the segmentation accuracy of segm_mAP_50 increased from 0.236 to 0.254. When ResNetx101 is used as the backbone network, the segmentation accuracy of segm_mAP_50 increased from 0.452 to 0.463. In summary, this paper used Focal Loss on Mask R-CNN and Mask Scoring R-CNN to generate better mAP of segmentation and output more detailed information about peach diseases.
Sprache	Englisch
Erscheinungsdatum	2022-05-25
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2022.876357
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Supporting a circular economy: Insights from Taiwan's plastic waste sector and lessons for developing countries

Wu, Ching-Yan / Hu, Mei-Chih / Ni, Fu-Chuan

Sustainable Production and Consumption. 2021 Apr., v. 26

2021

Abstract	This study investigates how, in the process of industrialization, Taiwan successfully developed its plastic waste industry into an industrial-level circular economy by leveraging a network-based collective bricolage in conjunction with a framework of adaptive institutional governance. Three conclusions are made: industrialized manufacturing sectors are foundations upon which developing nations can accumulate endogenous social capabilities and can enable the emergence of network-based collective bricolages; for developing nations that are attempting to establish circular economies based on their endogenous small-to-medium enterprises, developing network-based collective bricolages in conjunction with adaptive institutional governance is an essential and effective strategy; and transitioning into green-related sectors can further drive economic development and lead to the creation of new ventures, businesses, and job opportunities while supporting the formation of a circular economy. The approach is especially relevant for developing countries starting their industrialization process and waste management initiatives with few resources.
Schlagwörter	business enterprises ; circular economy ; developing countries ; employment opportunities ; governance ; industrialization ; manufacturing ; waste management ; wastes ; Taiwan
Sprache	Englisch
Erscheinungsverlauf	2021-04
Umfang	p. 228-238.
Erscheinungsort	Elsevier B.V.
Dokumenttyp	Artikel
ISSN	2352-5509
DOI	10.1016/j.spc.2020.10.009
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel: Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization.

Huang, Feng / Qiu, Yang / Li, Qiaojun / Liu, Shichao / Ni, Fuchuan

Frontiers in bioengineering and biotechnology

2020 Band 8, Seite(n) 218

Abstract: Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease ... ...

Abstract	Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease associations involve different association types, such as drug indications and drug side effects. However, the existing models for predicting drug-disease associations merely concentrate on independent tasks: recommending novel indications to benefit drug repositioning, predicting potential side effects to prevent drug-induced risk, or only determining the existence of drug-disease association. They ignore crucial prior knowledge of the correlations between different association types. Since the Comparative Toxicogenomics Database (CTD) annotates the drug-disease associations as therapeutic or marker/mechanism, we consider predicting the two types of association. To this end, we propose a collective matrix factorization-based multi-task learning method (CMFMTL) in this paper. CMFMTL handles the problem as multi-task learning where each task is to predict one type of association, and two tasks complement and improve each other by capturing the relatedness between them. First, drug-disease associations are represented as a bipartite network with two types of links representing therapeutic effects and non-therapeutic effects. Then, CMFMTL, respectively, approximates the association matrix regarding each link type by matrix tri-factorization, and shares the low-dimensional latent representations for drugs and diseases in the two related tasks for the goal of collective learning. Finally, CMFMTL puts the two tasks into a unified framework and an efficient algorithm is developed to solve our proposed optimization problem. In the computational experiments, CMFMTL outperforms several state-of-the-art methods both in the two tasks. Moreover, case studies show that CMFMTL helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their association types.
Sprache	Englisch
Erscheinungsdatum	2020-04-09
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2719493-0
ISSN	2296-4185
ISSN	2296-4185
DOI	10.3389/fbioe.2020.00218
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: L2MXception: an improved Xception network for classification of peach diseases

Yao, Na / Ni, Fuchuan / Wang, Ziyan / Luo, Jun / Sung, Wing-Kin / Luo, Chaoxi / Li, Guoliang

Plant methods. 2021 Dec., v. 17, no. 1

2021

Abstract: BACKGROUND: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases ... ...

Abstract	BACKGROUND: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue. RESULTS: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. CONCLUSIONS: The proposed L2MXception network may have great potential in early identification of peach diseases.
Schlagwörter	data collection ; models ; peaches ; prediction
Sprache	Englisch
Erscheinungsverlauf	2021-12
Umfang	p. 36.
Erscheinungsort	BioMed Central
Dokumenttyp	Artikel
ZDB-ID	2203723-8
ISSN	1746-4811
ISSN	1746-4811
DOI	10.1186/s13007-021-00736-3
Datenquelle	NAL Katalog (AGRICOLA)

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Buch ; Online: ClsGAN

Ying, Liu / Fan, Heng / Ni, Fuchuan / Xiang, Jinhai

Selective Attribute Editing Model Based On Classification Adversarial Network

2019

Abstract: Attribution editing has achieved remarkable progress in recent years owing to the encoder-decoder structure and generative adversarial network (GAN). However, it remains challenging in generating high-quality images with accurate attribute transformation. ...

Abstract	Attribution editing has achieved remarkable progress in recent years owing to the encoder-decoder structure and generative adversarial network (GAN). However, it remains challenging in generating high-quality images with accurate attribute transformation. Attacking these problems, the work proposes a novel selective attribute editing model based on classification adversarial network (referred to as ClsGAN) that shows good balance between attribute transfer accuracy and photo-realistic images. Considering that the editing images are prone to be affected by original attribute due to skip-connection in encoder-decoder structure, an upper convolution residual network (referred to as Tr-resnet) is presented to selectively extract information from the source image and target label. In addition, to further improve the transfer accuracy of generated images, an attribute adversarial classifier (referred to as Atta-cls) is introduced to guide the generator from the perspective of attribute through learning the defects of attribute transfer images. Experimental results on CelebA demonstrate that our ClsGAN performs favorably against state-of-the-art approaches in image quality and transfer accuracy. Moreover, ablation studies are also designed to verify the great performance of Tr-resnet and Atta-cls.
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
Thema/Rubrik (Code)	006
Erscheinungsdatum	2019-10-25
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Data-driven Approach for Quality Evaluation on Knowledge Sharing Platform

Xu, Lu / Xiang, Jinhai / Wang, Yating / Ni, Fuchuan

2019

Abstract: In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice ... ...

Abstract	In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live). Extensive experiments demonstrate the effectiveness of the proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open resource for researchers in related areas. This dataset will facilitate the development of new methods on knowledge sharing services quality evaluation.
Schlagwörter	Computer Science - Social and Information Networks ; Computer Science - Computation and Language
Erscheinungsdatum	2019-03-01
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks.

Zheng, Fang / Wei, Le / Zhao, Liang / Ni, FuChuan

BioMed research international

2018 Band 2018, Seite(n) 5670210

Abstract: Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important ... ...

Abstract	Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective.
Mesh-Begriff(e)	Breast Neoplasms/etiology ; Breast Neoplasms/genetics ; Computational Biology ; Disease ; Female ; Gene Regulatory Networks ; Genomics ; Humans ; Models, Biological ; Phenotype
Sprache	Englisch
Erscheinungsdatum	2018-07-30
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2698540-8
ISSN	2314-6141 ; 2314-6133
ISSN (online)	2314-6141
ISSN	2314-6133
DOI	10.1155/2018/5670210
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: L2MXception: an improved Xception network for classification of peach diseases.

Yao, Na / Ni, Fuchuan / Wang, Ziyan / Luo, Jun / Sung, Wing-Kin / Luo, Chaoxi / Li, Guoliang

Plant methods

2021 Band 17, Heft 1, Seite(n) 36

Abstract: Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases ... ...

Abstract	Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue. Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.
Sprache	Englisch
Erscheinungsdatum	2021-04-01
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2203723-8
ISSN	1746-4811
ISSN	1746-4811
DOI	10.1186/s13007-021-00736-3
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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