Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved...
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In this paper, the possibilities of deep learning approaches for sandwich structure delamination detection and prediction have been investigated. The research was divided into three parts: a validation study, a classi...
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Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, ...
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The widespread availability of code-mixed data in digital spaces can provide valuable insights into low-resource languages like Bengali, which have limited annotated corpora. Sentiment analysis, a pivotal text classif...
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Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity o...
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Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.
Conventional healthcare systems have long struggled to address the varied needs of large patient populations, often leading to inefficiencies and less-than-optimal outcomes. Yet, the advent of machine learning (ML) an...
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The rapid proliferation of Internet of Things (IoT) devices, coupled with the rollout of advanced 5G networks, has generated significant concerns regarding security breaches. These concerns stem from the expanded atta...
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Can Visual Question Answering (VQA) systems maintain their performance when deployed in the real world? Or are they susceptible to realistic corruption effects, e.g., image blur, which can be detrimental in sensitive ...
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In the area of data hiding and information security, the greater need is to ensure high embedding capacity of the stego media without hampering the visual quality while ensuring the tightest possible security. Now, it...
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Plant diseases significantly impact agricultural productivity, making it essential to accurately assess not only the presence of disease but also its severity. This paper presents a deep learning-based framework using...
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