Academic Articles & Research Papers

Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization

Abstract Background Frailty models are extensively utilized in survival analysis to address unobserved heterogeneity among individuals. However, selecting the most robust model for survival prediction, especially in the context of high-dime...

Wide Field of View Microwave Interferometric Radiometer Imaging

In microwave interferometric radiometers with a large field of view, as for example the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) satellite, one of the major causes of r...

COMPUTATIONAL INTELLIGENCE IN NUCLEAR ENGINEERING

Approaches to several recent issues in the operation of nuclear power plants using computational intelligence are discussed. These issues include 1) noise analysis techniques, 2) on-line monitoring and sensor validation, 3) regularization o...

A Truncated-Kernel Mollification Method for the Cauchy Problem of the Modified Helmholtz Equation

This paper addresses the Cauchy problem for the multi-dimensional modified Helmholtz equation, a classical and severely ill-posed problem. A truncated-kernel mollification method is proposed as an effective regularization approach. Both the...

Lightweight MS-DSCNN-AttMPLSTM for High-Precision Misalignment Fault Diagnosis of Wind Turbines

Wind turbine (WT) misalignment fault diagnosis is constrained by critical signal processing challenges: weak fault features, intense background noise, and poor generalization. This study proposes a lightweight method for high-precision faul...

Bayesian ANN-Based Prediction and Multi-Objective Optimization of Tribological Behavior in Magnesium Alloy AZ91D at Elevated Temperature Using Pareto GA

This study uses an artificial neural network (ANN) model to predict the wear rate and friction coefficient of the magnesium alloy AZ91D, based on experimental data from a pin-on-disc tribometer. The model includes three essential process pa...

Quantum Communication Complexity of Regularized Linear Regression Protocols

Linear regression is fundamental to statistical analysis and machine learning, but its application to large-scale datasets necessitates distributed computing. The problem also arises in quantum computing, where handling extensive data requi...

A renaissance of neural networks in drug discovery

INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...

Self-Normalizing Neural Networks

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...

Recurrent Neural Network Regularization

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In ...

Joint Optimization of Transform and Quantization for High Efficiency Video Coding

In high efficiency video coding (HEVC), transformation and quantization are separately performed to eliminate the perceptual redundancy of visual signals. However, a uniform quantizer can inevitably degrade the compression efficiency of fix...

Holistic Parameter Optimization for Software Defect Prediction

A software defect prediction (SDP) model identifies the defect-prone modules. Setting appropriate parameters in an SDP model is critical because it affects the model performance. In a recent study, parameters were automatically explored usi...

Hybrid spatio-temporal transformer and variational autoencoder framework for advanced sports injury prediction and analysis

Sports injuries significantly affect sports performance and overall health, making using accurate and reliable prognostic models imperative. Conventional algorithms for injury prediction, e.g., random forest and XGBoost, generally cannot un...

QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by adva...

Multi-Task Learning in Natural Language Processing: An Overview

Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. ...

Identifying multi-omics spatial domains via masked autoencoder and diffusion-based latent regularization with SpaMEDM.

An Overview of the Attention Mechanisms in Computer Vision

Abstract Deep convolutional neural network (CNN) plays an important role in the field of computer vision and image processing. In order to further improve the performance of CNN, scholars have conducted a series of new explorations, such as...

Sequential Normalization: Embracing Smaller Sample Sizes for Normalization

Normalization as a layer within neural networks has over the years demonstrated its effectiveness in neural network optimization across a wide range of different tasks, with one of the most successful approaches being that of batch normaliz...

Image decomposition using a second-order variational model and wavelet shrinkage

The paper is devoted to the new model for image decomposition, that splits an image $f$ into three components $u+v+\omega$, with $u$ a piecewise-smooth or the ``cartoon'' component, $v$ a texture component and $\omega$ the noise part in var...

Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...