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نتیجه جستجو - سازگاری مد

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 An exploratory study of the adaptation of green supply chain management in construction industry: The case of Indian Construction Companies
مطالعه اکتشافی در مورد سازگاری مدیریت زنجیره تأمین سبز در صنعت ساختمان: مورد شرکتهای ساختمانی هند-2021
Indian construction industry has not been addressed adequately in the context of green supply chain practices. However, various drivers, enablers and barriers for adoption of green supply chain practices have been reported in the literature. In view of these stylists facts, the main aim of the present research work is first to investigate the adaptability of green supply chain practices by various categories of Indian construction companies followed by identifying the correlation of drivers, enablers and barriers with the construction industry readiness of adaptation of green supply chain practices. Finally, these drivers, enablers and barriers are prioritized by using the AHP methodology. A questionnaire survey was disseminated to senior construction project managers of Indian construction companies having different size as well as area of operations. Preliminary analysis is performed by using the descriptive statistics. Four hypotheses are tested using ANOVA and spearman’s correlation coefficient. The priority structure of drivers, enablers and barriers is also developed by using the AHP methodology. Results demonstrate that institutional theory, complexity theory, ecological modernization theory, resource-based view and resource dependency theory are well supported. The study will be helpful for the construction project managers/clean production policy makers in taking more informed, systematic and efficient decisions, while adapting the sustainable supply chain practices in the context of construction industry.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Green supply chain | Construction supply chain | ANOVA | Correlation | Analytical hierarchy process
مقاله انگلیسی
2 Learning fashion compatibility across categories with deep multimodal neural networks
یادگیری سازگاری مد در سراسر دسته با شبکه های عصبی عمیق چند حالته-2019
Fashion compatibility is a subjective sense of human for relationships between fashion items, which is essential for fashion recommendation. Recently, it increasingly attracts more and more attentions and has become a very hot research topic. Learning fashion compatibility is a challenging task, since it needs to consider plenty of factors about fashion items, such as color, texture, style and functionality. Unlike low-level visual compatibility (e.g., color, texture), high-level semantic compatibility (e.g., style, function- ality) cannot be handled purely based on fashion images. In this paper, we propose a novel multimodal framework to learn fashion compatibility, which simultaneously integrates both semantic and visual em- beddings into a unified deep learning model. For semantic embeddings, a multilayered Long Short-Term Memory (LSTM) is employed for discriminative semantic representation learning, while a deep Convo- lutional Neural Network (CNN) is used for visual embeddings. A fusion module is then constructed to combine semantic and visual information of fashion items, which equivalently transforms semantic and visual spaces into a latent feature space. Furthermore, a new triplet ranking loss with compatible weights is introduced to measure fine-grained relationships between fashion items, which is more consistent with human feelings on fashion compatibility in reality. Extensive experiments conducted on Amazon fashion dataset demonstrate the effectiveness of the proposed method for learning fashion compatibility, which outperforms the state-of-the-art approaches.
Keywords: Fashion compatibility | Deep learning | Neural networks | Multimodal
مقاله انگلیسی
3 A balanced modularity maximization link prediction model in social networks
مدل پیش بینی پیوند حداکثر سازگاری مدولار در شبکه های اجتماعی-2017
Link prediction has been becoming an important research topic due to the rapid growth of social networks. Community-based link prediction methods are proposed to incorporate community information in order to achieve accurate prediction. However, the performance of such methods is sensitive to the selection of community detection algorithms, and they also fail to capture the correlation between link formulation and community evolution. In this paper we introduce a balanced Modularity-Maximization Link Prediction (MMLP) model to address this issue. The idea of MMLP is to integrate the formulation of two types of links into a partitioned network generative model. We proposed a probabilistic algo rithm to emphasize the role of innerLinks, which correspondingly maximizes the network modularity. Then, a trade-off technique is designed to maintain the network in a stable state of equilibrium. We also present an effective feature aggregation method by exploring two variations of network features. Our proposed method can overcome the limit of sev eral community-based methods and the extensive experimental results on both synthetic and real-world benchmark data demonstrate its effectiveness and robustness.
Keywords: Link prediction | Social network | Community detection | Modularity
مقاله انگلیسی
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