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DQN-PACG: load regulation method based on DQN and multivariate prediction model
Demand response plays a pivotal role in modern smart grid systems, aiding in balancing energy consumption. However, the increasing energy demands of...
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Iterative missing value imputation based on feature importance
Many datasets suffer from missing values due to various reasons, which not only increases the processing difficulty of related tasks but also reduces...
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VAE-GNA: a variational autoencoder with Gaussian neurons in the latent space and attention mechanisms
Variational autoencoders (VAEs) are generative models known for learning compact and continuous latent representations of data. While they have...
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LightCapsGNN: light capsule graph neural network for graph classification
Graph neural networks (GNNs) have achieved excellent performances in many graph-related tasks. However, they need appropriate pooling operations to...
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Efficient base station deployment in specialized regions with splitting particle swarm optimization algorithm
Signal coverage quality and intensity distribution in complex environments pose a critical challenge, particularly evident in high-density personnel...
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A novel discrete slash family of distributions with application to epidemiology informatics data
This study puts forward a new class of discrete distribution that can be used by the epidemiologists and medical scientists to model data relating to...
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Temporal analysis of topic modeling output by machine learning techniques
Topic modeling is widely recognized as one of the most effective and significant methods of unsupervised text analysis. This method facilitates...
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Bridging distribution gaps: invariant pattern discovery for dynamic graph learning
Temporal graph networks (TGNs) have been proposed to facilitate learning on dynamic graphs which are composed of interaction events among nodes....
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New custom rating for improving recommendation system performance
Recommendation system is currently attracting the interest of many explorers. Various new businesses have surfaced with the rise of online marketing...
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Unmasking GAN-Generated Faces with Optimal Deep Learning and Cognitive Computing-Based Cutting-Edge Detection System
The emergence of deep learning (DL) has improved the excellence of generated media. However, with the enlarged level of photorealism, synthetic media...
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A survey of multi-population optimization algorithms for tracking the moving optimum in dynamic environments
The solution spaces of many real-world optimization problems change over time. Such problems are called dynamic optimization problems (DOPs), which...
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A review of sentiment analysis: tasks, applications, and deep learning techniques
Sentiment analysis, a transformative force in natural language processing, revolutionizes diverse fields such as business, social media, healthcare,...
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When large language models meet personalization: perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model...
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Cognitive Intelligent Decisions for Big Data and Cloud Computing in Industrial Applications using Trifold Algorithms
In contemporary real-time applications, diminutive devices are increasingly employing a greater portion of the spectrum to transmit data despite the...
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Search and Harvesting across NFDI Consortia – Gaps and Challenges
Search and harvesting use cases on harmonised metadata play an important role in several activities on National Research Data Infrastructures (NFDI)....
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GPU-based butterfly counting
When dealing with large bipartite graphs, butterfly counting is a crucial and time-consuming operation. Graphics processing units (GPUs) are widely...
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Learning from Failure: Towards Develo** a Disease Diagnosis Assistant That Also Learns from Unsuccessful Diagnoses
In recent years, automatic disease diagnosis has gained immense popularity in research and industry communities. Humans learn a task through both...
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DSPformer: discovering semantic parts with token growth and clustering for zero-shot learning
Transformers have achieved success in many computer vision tasks, but their potential in Zero-Shot Learning (ZSL) has yet to be fully explored. In...
