Cybersecurity has become very important due the rapid growth of digital technologies. It plays an important role in protecting computer systems and networks from potential attacks. Cybersecurity involves a set of tech...
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In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coher...
This study offers an approach that uses SARS-COV-2 mNGS (meta-genomic next-generation sequencing) samples to apply XAI (explainable artificial intelligence) methodologies derived from the use of machinelearning metho...
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Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN),...
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Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT), and graph convolutional networks (GCN), have shown remarkable results on a variety of deep learning tasks in recent years. In this study, we offer a generic design pipeline for GNN models, go over the variations of each part, classify the applications in an organized manner, and suggest four outstanding research issues. Dealing with graph data, which provides extensive connection information among pieces, is necessary for many learning tasks. A model that learns from graph inputs is required for modelling physics systems, learning molecular fingerprints, predicting protein interfaces, and identifying illnesses. Reasoning on extracted structures (such as the dependency trees of sentences and the scene graphs of photos) is an important research issue that also requires graph reasoning models in other domains, such as learning from non-structural data like texts and images. Graph Neural Networks (GNNs) are primarily designed for dealing with graph-structured data, where relationships between entities are modeled as edges in a graph. While GNNs are not traditionally applied to image classification problems, researchers have explored ways to leverage graph-based structures to enhance the performance of Convolutional Neural Networks (CNNs) in certain scenario. GNN have been increasingly applied to Natural Language Processing (NLP) tasks, leveraging their ability to model structured data and capture relationships between elements in a graph. GNN are also applied for traffic related problems particularly in modeling and optimizing traffic flow, analyzing transportation networks, and addressing congestion issues. GNN can be used for traffic flow prediction, dynamic routing & navigation, Anomaly detection, public transport network
In India, over 63 million individuals are affected by hearing loss, yet less than 350 certified sign language interpreters are available, thus leading to communication barriers and significant social and economic excl...
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Wireless Sensor Network (WSN) is a self-configured and infrastructure-less network that is used to monitor the environmental conditions and transfer sensor data to the desired destination in a particular region. Energ...
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Smart grid systems are seen as the next big thing to energy distribution and management with more efficiency, stability, and sustainability in power distribution and energy management systems. It is challenging to pre...
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Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, t...
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Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits ...
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Multi-modal learning has become increasingly popular due to its ability to leverage information from different data sources (e.g., text and images) to improve the model performance. Recently, CLIP has emerged as an ef...
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