The study focuses on Quality of Service (QoS) issues in learning automata-based AODV (LA-AODV) for V2V communication protocols. It analyzes and compares the LAAODV protocol with the original AODV protocol in dynamic t...
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Due to inconvenience and time wasting factor in traditional trolleys employed in shopping malls, markets and shopping complexes etc, we have come up with the novel solution named as RoboTrolley i.e. an intelligent cus...
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The Internet of Things (IoT) technology is a viable alternative for monitoring meteorological data in a specific area and making the data accessible from anywhere in the world. This is based on the idea that IoT techn...
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Medical image fusion plays a vital role in enhancing diagnostic accuracy by integrating complementary information from multimodal imaging modalities such as MRI, PET, and SPECT. However, many existing methods are limi...
Medical image fusion plays a vital role in enhancing diagnostic accuracy by integrating complementary information from multimodal imaging modalities such as MRI, PET, and SPECT. However, many existing methods are limited by information loss and inadequate structural preservation. To overcome these limitations, we propose a novel fusion framework that integrates spatial-frequency saliency analysis with graph-based optimization to improve fusion quality. The process begins with a preprocessing stage designed to enhance image quality through denoising, contrast adjustment, and edge sharpening. Following this, a multi-level decomposition strategy based on the Iterative Least Squares Smoothing Filter (ILSSF) is employed to extract base and detail components while preserving essential anatomical structures. For the fusion of base components, we introduce the Energy-Balanced Visual Saliency Map (EBVSM), which adaptively combines spatial gradients and frequency features through an energy-weighted scheme. To fuse detail components, we present a Patch-Based Graph Optimization (PBGO) method that models both local and global patch relationships using Laplacian regularization, with optimization performed via the Adam algorithm. This approach ensures a balance between visual saliency and structural coherence, resulting in superior detail preservation and enhanced contrast. Experimental results on two benchmark datasets (PET-MRI and SPECT-MRI) demonstrate that the proposed method achieves competitive performance and achieves superior results compared to nine existing techniques across eight quantitative metrics. Furthermore, the fused images exhibit strong clinical potential by effectively maintaining anatomical clarity and functional contrast.
This paper discusses the use of node2vec graph embeddings to improve the performance of wide and deep recommenders by substituting the embedding layer with graph embeddings to leverage its representational power witho...
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Healthcare stands out as a critical domain profoundly impacted by Internet of Things (IoT) technology, generating vast data from sensing devices as IoT applications expand. Addressing security challenges is paramount ...
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Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral ...
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In the realm of global sustainable development, effective management of water resources and the protection of ecological environments have increasingly emerged as focal points for both governmental and international o...
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As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requi...
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The emergence of blockchain technology is transforming various aspects, including electronic voting (e-voting) systems, which have become increasingly important due to the need for transparency, accessibility, and sec...
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