Filming Women: A Conversation with Alankrita Shrivastava

Arora, Anupama; Sanos, Sandrine; Siddiqui, Gohar

doi:10.23860/jfs.2022.21.2

Cited by 2 publications

(3 citation statements)

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“…However, this is not always the case with respect to RCA indices because these have non-normality and influential outliers. To overcome this problem, we apply quantile regression to the equation above (8). Koenker et al [43] developed quantile regression analysis based on median regression to overcome the difficulty that occurs when the dependent variable is not normally distributed.…”

Section: Methodsmentioning

confidence: 99%

“…High rates of productivity growth are often regarded as a way of strengthening competitiveness [7]. New processes and the adaptability of new technologies to meet changing demands are also helping companies to achieve competitiveness [8]. Information technologies also create added value in the supply chain to enhance industrial competitiveness as it enables fast communication with suppliers and clients [9].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Advantage of Yarn and Weaving Industries: Evidence for Türkiye and Top Exporters

KANTUR

Türkekul

2023

Fibres &Amp; Textiles in Eastern Europe

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With the repeal of the MFA agreement, the competitive conditions in yarn and weaving industries have changed. Especially, with the cheap labour of Asian and Far Eastern countries, they have become advantageous in this unrestricted competition environment. Therefore, this study was conducted to help to identify those products where Türkiye and her competitors have comparative advantages. This study mainly focuses on analysing the complementarity and substitutability of selected countries such as Türkiye, India, China and the USA. RTA, RC, RSCA and trade specialisation (TSI) indices were used to evaluate the competitive advantage. The results showed that Türkiye's competitiveness in cotton yarn and cotton weaving has deteriorated. However, India and China, which are the major countries in the world's cotton production, have a positive impact on their long-term competitiveness. Moreover, for these countries there is convergence in cotton yarn and divergence in cotton fabric products.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Advantage of Yarn and Weaving Industries: Evidence for Türkiye and Top Exporters

KANTUR

Türkekul

2023

Fibres &Amp; Textiles in Eastern Europe

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“…Mittal et al [1] observed that image segmentation is vital in various fields, primarily healthcare and autonomous driving. GANs significantly impact this, with 2084 studies identified.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network

LATHA,

VELMURUGAN

2024

Preprint

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Face sketch synthesis phenomenon, a kind of image-image translation, generates synthesized face/sketch with wide range of applications pertaining law enforcement and entertainment to mention few. Despite the compelling results produced by many existing methods of late, there are still challenges due to deformation and blurred effects on facial components resulting in unrealistic face/sketch. To overcome this problem, in this paper, we proposed a novel framework known as Deep Face-Sketch Synthesis Framework (DFSSF). The framework is realized with different building blocks including an algorithm known as Deep Face-Sketch Synthesis for High Perceptual Quality (DFSS-HPQ). The framework is based on the architecture of Generative Adversarial Network (GAN) which exploits facial structures and a novel labelling mechanism. It takes paired inputs compromising of face images and sketches. The framework also considers extraction of GANs with heterogeneity from inputs. Afterwards, they are stacked to obtain additional features that can be effectively used to rectify defects if any. Two algorithms known as Hybrid GAN for Face Sketch Synthesis (HGAN-FSS) and Stacked Hybrid GAN for Face Sketch Synthesis (SHGAN-FSS) are proposed. We used two frequently used datasets namely CUFS and CUFSF having samples collected from 606 and 1194 persons respectively. The proposed framework is built using Python data science platform. Empirical results of the framework are evaluated and compared with traditional face sketch methods, deep learning models and deep learning models based on GANs. The proposed framework showed better performance over the state of the art in presence of different styles, lighting conditions and head poses.

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Filming Women: A Conversation with Alankrita Shrivastava

Cited by 2 publications

References 0 publications

Comparative Advantage of Yarn and Weaving Industries: Evidence for Türkiye and Top Exporters

Comparative Advantage of Yarn and Weaving Industries: Evidence for Türkiye and Top Exporters

A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network

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