Results 21 to 30 of about 71,775 (147)
Sensors, 2023 Pervasive computing, human–computer interaction, human behavior analysis, and human activity recognition (HAR) fields have grown significantly. Deep learning (DL)-based techniques have recently been effectively used to predict various human actions using
Shaik Jameer, Hussain Syed
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IEEE Access, 2023 Non-traumatic grief can be defined as, a complex process that includes emotional, physical, spiritual, social, and intellectual behaviors and responses through which individuals, families, and communities incorporate actual, anticipated, or perceived ...
Jacinto Mata-Vazquez +4 more
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Comparative Analysis of Transformers to Support Fine-Grained Emotion Detection in Short-Text Data
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Understanding a person’s mood and circumstances by way of sentiment or finer-grained emotion detection can play a significant role in AI systems and applications, such as in chat dialogue or reviews.
Robert H. Frye, David C. Wilson
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BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework
Frontiers in Computational Neuroscience, 2020 Human intelligence is constituted by a multitude of cognitive functions activated either directly or indirectly by external stimuli of various kinds. Computational approaches to the cognitive sciences and to neuroscience are partly premised on the idea ...
Newton Howard +6 more
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Enhancing Load Prediction Accuracy using Optimized Support Vector Regression Models
Journal of Digital Food, Energy & Water Systems, 2023 This paper investigates the effect of Support Vector Regression hyperparameters optimization on electrical load prediction. Accurate and robust load prediction helps policy makers in the energy sector to make inform decision and reduce losses.
Abdulsemiu Olawuyi +3 more
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Handwritten Digit Recognition: Hyperparameters-Based Analysis
Applied Sciences, 2020 Neural networks have several useful applications in machine learning. However, benefiting from the neural-network architecture can be tricky in some instances due to the large number of parameters that can influence performance.
Saleh Albahli +3 more
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Hyperparameter Optimization for AST Differencing
IEEE Transactions on Software Engineering, 2023 Computing the differences between two versions of the same program is an essential task for software development and software evolution research. AST differencing is the most advanced way of doing so, and an active research area. Yet, AST differencing algorithms rely on configuration parameters that may have a strong impact on their effectiveness.
Matias Martinez +2 more
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On Architecture Selection for Linear Inverse Problems with Untrained Neural Networks
Entropy, 2021 In recent years, neural network based image priors have been shown to be highly effective for linear inverse problems, often significantly outperforming conventional methods that are based on sparsity and related notions.
Yang Sun +2 more
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Interpolation Models with Multiple Hyperparameters [PDF]
Statistics and Computing, 1996 A traditional interpolation model is characterized by the choice of regularizer applied to the interpolant, and the choice of noise model. Typically, the regularizer has a single regularization constant α, and the noise model has a single parameter β.
DAVID J. C. MACKAY, RYO TAKEUCHI
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MathematicsLoad forecasting is an integral part of the power industries. Load-forecasting techniques should minimize the percentage error while prediction future demand. This will inherently help utilities have an uninterrupted power supply.
Umme Mumtahina +2 more
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