As modern retail processes are rapidly developing;inventory management becomes one of the key factors affecting the client's needs and business competitiveness. The merchandising concept of product accessibility o...
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Network intrusion remains a critical security concern in the modern cyber world, necessitating innovative approaches to enhance network confidentiality. In this paper, we introduce a novel Hybrid Genetic Coati-Pelican...
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This paper solves the boundary value problem of the second-order differential equation under the neutrosophic fuzzy boundary condition. The proposed solution is approximated using the finite difference method but dene...
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As a remote game originating from the roots of Nepal, Baghchal has witnessed limited exploration and consequently, the strategic aspect of the game remains underdeveloped. The game can be characterized as a heterogene...
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In the contemporary financial landscape, managing and hedging risk remains an indispensable aspect of portfolio management. Value at Risk (VaR) is a widely accepted measure for quantifying and understanding the downsi...
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One of the prime technological advancements in modern healthcare solutions is smart and interconnected wearable devices. The progress of the Internet of Things (IoT) has facilitated the ability of wearable devices to ...
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Improving the generative and representational capabilities of auto-encoders is a hot research topic. However, it is a challenge to jointly and simultaneously optimize the bidirectional mapping between the encoder and ...
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Improving the generative and representational capabilities of auto-encoders is a hot research topic. However, it is a challenge to jointly and simultaneously optimize the bidirectional mapping between the encoder and the decoder/generator while ensuing convergence. Most existing auto-encoders cannot automatically trade off bidirectional mapping. In this work, we propose Bi-GAE, an unsupervised bidirectional generative auto-encoder based on bidirectional generative adversarial network (BiGAN). First, we introduce two terms that enhance information expansion in decoding to follow human visual models and to improve semantic-relevant feature representation capability in encoding. Furthermore, we embed a generative adversarial network (GAN) to improve representation while ensuring convergence. The experimental results show that Bi-GAE achieves competitive results in both generation and representation with stable convergence. Compared with its counterparts, the representational power of Bi-GAE improves the classification accuracy of high-resolution images by about 8.09%. In addition, Bi-GAE increases structural similarity index measure (SSIM) by 0.045, and decreases Fréchet inception distance (FID) by in the reconstruction of 512*512 images.
Decentralized Autonomous Organizations (DAOs) have become a transformative force in the ever-evolving landscape of decentralized finance (DeFi), reshaping traditional investment frameworks. This research thoroughly ex...
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Cloud computing enhances defense, public sectors, innovation, and infrastructure. The Korean Military plans to adopt cloud computing for its defense system, but current security measures are inadequate. A secure archi...
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The early detection of oral malignancy by physicians is a strenuous task. The analysis of histopathological oral malignancy images using image processing and deep learning techniques can be an add-on facility for doct...
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