Background: The IoT (Internet of Things) assigns to the capacity of Device-to-Machine (D2M) connections, which is a vital component in the development of the digital economy. IoT integration with a human being enables...
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Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a...
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With tremendous evolution in the internet world, the internet has become a household thing. Internet users use search engines or personal assistants to request information from the internet. Search results are greatly...
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Post Quantum Cryptography has received an increasing amount of active research. This has been made prominent by the ever-growing field of quantum computing which poses a formidable threat to the modern cybersecurity l...
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Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare faciliti...
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With the development of the Internet, the use of social media has increased dramatically over time and has emerged as the most powerful networking tool of the twenty-first century. From youngsters of ten years to seni...
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To address the privacy concerns that arise from centralizing model training on a large number of IoT devices, a revolutionary new distributed learning framework called federated learning has been developed. This setup...
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With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications...
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With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications, resulted in rigorous demands for quality of experience (QoE) and intricate task caching. The diverse requirements of on-vehicle applications, as well as the freshness of dynamic cached information, provide significant challenges for edge servers in efficiently fulfilling energy and latency demands. This work studies a freshness-aware caching-aided offloading-based task allocation problem (FCAOP) in DT-enabled IoV (DTIoV) with Intelligent Reflective Surfaces (IRS) and edge computing. DT is used to accumulate real-time data and digitally depict the physical objects of the IoV to enhance decision-making. A quantum-inspired differential evolution (QDE) algorithm is proposed to reduce the overall delay and energy consumption in DTIoV (QDE-DTIoV). The quantum vector (QV) is encoded to represent a complete solution to the FCAOP. The decoding of the QVs is done using a one-time hashing algorithm. The fitness function is derived by considering delay, energy consumption, and freshness of the tasks. Extensive simulations demonstrate the superiority of QDE-DTIoV over other benchmark algorithms, showing an average latency improvement of 23%-26% and a reduction in energy consumption ranging from 22% to 33%. IEEE
Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text cont...
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Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu
Lung disorders are medical conditions that disrupt the lungs and their capacity to function normally. One fatal lung disease is a collapsed lung where the lung collapses partially or fully due to diseases like pneumot...
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