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A Meta-learner approach to multistep-ahead time series prediction
The utilization of machine learning has become ubiquitous in addressing contemporary challenges in data science. Moreover, there has been significant...
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Extract Implicit Semantic Friends and Their Influences from Bipartite Network for Social Recommendation
Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that...
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Open benchmark for filtering techniques in entity resolution
Entity Resolution identifies entity profiles that represent the same real-world object. A brute-force approach that considers all pairs of entities...
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Minimum motif-cut: a workload-aware RDF graph partitioning strategy
In designing a distributed RDF system, it is quite common to divide an RDF graph into subgraphs, called partitions , which are then distributed. Graph...
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Video anomaly localization using modified faster RCNN with soft NMS algorithm
Localization of anomalies in surveillance videos is a critical component of smart and intelligent surveillance systems. The goal of anomaly detection...
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An efficient facial emotion recognition using convolutional neural network with local sorting binary pattern and whale optimization algorithm
Facial emotion recognition is one of the fields of machine learning and pattern recognition. Facial expression recognition is used in a variety of...
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Similarity-based face image retrieval using sparsely embedded deep features and binary code learning
Human face retrieval has long been established as one of the most interesting research topics in computer vision. With the recent development of deep...
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Hyper-relational knowledge graph neural network for next POI recommendation
With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought...
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Parallel continuous skyline query over high-dimensional data stream windows
Real-time multi-criteria decision-making applications in fields like high-speed algorithmic trading, emergency response, and disaster management have...
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Storage of weights and retrieval method (SWARM) approach for neural networks hybridized with conformal prediction to construct the prediction intervals for energy system applications
The prediction intervals represent the uncertainty associated with the model-predicted responses that impacts the sequential decision-making...
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Exploring AI-driven approaches for unstructured document analysis and future horizons
In the current industrial landscape, a significant number of sectors are grappling with the challenges posed by unstructured data, which incurs...
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Taxonomy of deep learning-based intrusion detection system approaches in fog computing: a systematic review
The Internet of Things (IoT) has been used in various aspects. Fundamental security issues must be addressed to accelerate and develop the Internet...
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Deep learning and embeddings-based approaches for keyphrase extraction: a literature review
Keyphrase extraction is a subtask of natural language processing referring to the automatic extraction of salient terms that semantically capture the...
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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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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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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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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...
