Image segmentation is a fundamental computer vision technique that divides a digital image into distinct regions or segments, each representing different objects or parts of objects. This process enables computers to ...
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This review study explores the role of license plate detection systems, aiming to develop an intelligent system capable of automatically detecting and reading license plates in images or video streams. Leveraging rece...
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Atthe forefront of the Artificial Intelligence Revolution is the Generative AI domain which is making splashes in generation of new content from existing Large Language Models. Large Language Models (LLMs) are flexibl...
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Sentiment analysis (SA) is an active and dynamic aspect of text mining that focuses on the automated analysis of subjectivity, opinions, and sentiments in textual material. The study explores new SA domains such as re...
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Cryptocurrency has taken the financial world by storm, with its value and relevance growing daily. For financial players, predicting cryptocurrency prices accurately has become crucial. Considering the growing importa...
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Non-Fungible Tokens (or, for short, NFT) establish the ownership and the authenticity of digital assets. Regardless the high security levels provided by blockchain, are not excluded the cases where the ownership of a ...
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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.
This research seeks to use parameter reduction optimization to improve the understanding of consumer behavior gained from Association Rule mining where the density of the data set is low. Low density occurs where smal...
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Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Human - Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop1. Th...
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Identification of gesture is very important for specially challenged people and also to improve the smartness of the working environment i.e. using the hand gesture the presentation slides can move on its own. Also he...
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