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Transferable preference learning in multi-objective decision analysis and its application to hydrocracking
Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low...
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A hyperparameter study for quantum kernel methods
Quantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them. Their accessibility for...
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TRAA: a two-risk archive algorithm for expensive many-objective optimization
Many engineering problems are essentially expensive multi-/many-objective optimization problems, and surrogate-assisted evolutionary algorithms have...
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Hybrid genetic optimization for quantum feature map design
Kernel methods are an import class of techniques in machine learning. To be effective, good feature maps are crucial for map** non-linearly...
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A novel bayesian network-based ensemble classifier chains for multi-label classification
In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by...
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Reading Between the Lines: Machine Learning Ensemble and Deep Learning for Implied Threat Detection in Textual Data
With the increase in the generation and spread of textual content on social media, natural language processing (NLP) has become an important area of...
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CommentClass: A Robust Ensemble Machine Learning Model for Comment Classification
Enormous amounts of data are generated in the form of feedback or comments from online platforms such as social media, e-commerce, education, and...
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ASR-Fed: agnostic straggler-resilient semi-asynchronous federated learning technique for secured drone network
Federated Learning (FL) has emerged as a transformative artificial intelligence paradigm, facilitating knowledge sharing among distributed edge...
