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BenchMetrics Prob: benchmarking of probabilistic error/loss performance evaluation instruments for binary classification problems
Probabilistic error/loss performance evaluation instruments that are originally used for regression and time series forecasting are also applied in...
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Optimal Spatial Prediction for Non-negative Spatial Processes Using a Phi-divergence Loss Function
A major component of inference in spatial statistics is that of spatial prediction of an unknown value from an underlying spatial process, based on...
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Error Estimation of Distributed Electric Metering Devices Based on the Least-Squared Error Fitting
Electric metering devices face problems of decreased accuracy of metering data due to their aging and malfunction. More importantly, it is difficult...
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Enhanced Human Action Recognition with Ensembled DTW Loss Function in CNN LSTM Architecture
Human action recognition is a concept that involves acquiring information based on the sequence of movements by the target. This recognition model is...
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Convergence analysis for sparse Pi-sigma neural network model with entropy error function
As a high-order neural network, the Pi-sigma neural network has demonstrated its capacities for fast learning and strong nonlinear processing. In...
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Robust Optimization of Discontinuous Loss Functions
Discontinuous loss functions are prevalent in engineering, sciences, data science, and machine learning applications. In engineering and the...
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Tversky-Kahneman: A New Loss Function for Skin Lesion Image Segmentation
This paper proposes a novel loss function inspired by the Tversky-Kahneman probability weighting function to effectively deal with medical image...
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Compressive Strength Estimation of Rice Husk Ash-Blended Concrete Using Deep Neural Network Regression with an Asymmetric Loss Function
This paper proposes a deep learning solution for estimating the compressive strength of rice husk ash-blended concrete. The deep learning models are...
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Solving Class Imbalance Problem in Target Detection with a Squared Cross Entropy Based Method
The foreground-background class imbalance in target detection is inevitable, which is caused by the training data set. Specifically, the number of...
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ILF-LSTM: enhanced loss function in LSTM to predict the sea surface temperature
Globe's primary issue is global warming, water temperatures have accompanied it as the sea surface temperature, and it is the primary attribute to...
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Sharp loss: a new loss function for radiotherapy dose prediction based on fully convolutional networks
BackgroundNeural-network methods have been widely used for the prediction of dose distributions in radiotherapy. However, the prediction accuracy of...
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modSwish: a new activation function for neural network
The activation functions are extremely important to neural networks since they are responsible for learning the abstract characteristics of the data...
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A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics
Despite its rapid development, Physics-Informed Neural Network (PINN)-based computational solid mechanics is still in its infancy. In PINN, the loss...
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Huber Loss and Neural Networks Application in Property Price Prediction
In this paper we aim to explore the Real Estate Market in Germany, and particularly we have taken a dataset of Berlin and applied various advanced...
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Error Detection and Error Concealment of Medical Images Using Frequency Selective Extrapolation (FSE) Algorithm
The fundamental problem of any transmission procedure is the introduction of unwanted discrepancies at the output obtained. These discrepancies cause...
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Improving deep learning-based image super-resolution with residual learning and perceptual loss using SRGAN model
This study introduces a new and inventive approach designed to address the complex challenges encountered in the domain of image super-resolution...
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Deep Image Retargeting Network with Multi-loss Functions
Image retargeting aims at displaying an image on a serious of device screen with different sizes, which has been widely applied in computer graphics...
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On the robustness and generalization of thermal error models for CNC machine tools
Thermally induced errors significantly affect the accuracy of the CNC machining process as they account for 40–70% of overall machining errors. The...
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Multi-fidelity error-estimate-based model management
This paper presents a novel multi-fidelity model-management framework based on the estimated error between the low-fidelity and high-fidelity models....
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A new fuzzy support vector machine with pinball loss
The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or...
