Academic Articles & Research Papers

Machine learning in construction: From shallow to deep learning

The development of artificial intelligence technology is currently bringing about new opportunities in construction. Machine learning is a major area of interest within the field of artificial intelligence, playing a pivotal role in the pro...

On the Origin of Deep Learning

This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep ...

Machine Learning and Deep Learning Approaches for CyberSecurity: A Review

The rapid evolution and growth of the internet through the last decades led to more concern about cyber-attacks that are continuously increasing and changing. As a result, an effective intrusion detection system was required to protect data...

Deep learning for universal linear embeddings of nonlinear dynamics

Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction, estimation, and control using linear theory. The Koopman operator is a leading data-driven e...

Deep Learning for Portfolio Optimization

In this article, the authors adopt deep learning models to directly optimize the portfolio Sharpe ratio. The framework they present circumvents the requirements for forecasting expected returns and allows them to directly optimize portfolio...

Deep learning improves antimicrobial peptide recognition

Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by w...

Scalable and accurate deep learning with electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variabl...

Neural Networks and Deep Learning

A benchmarking framework for perturbational signature recontextualization across biological contexts.

Detection of Atrial Fibrillation Using a Machine Learning Approach

The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocar...

Enhanced Multiscale Attention Network for Single Image Dehazing

Under severe weather conditions, the quality of the images taken outside is directly affected by floating atmospheric particles. To keep the quality of the images, haze removal methods play a critical role. The most difficult part of haze r...

Acoustic Impedance Inversion from Seismic Imaging Profiles Using Self Attention U-Net

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismi...

Automatic Identification of Brain Injury Mechanism Based on Deep Learning

ObjectiveTo apply the convolutional neural network （CNN） Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology...

Enhancing Deep Learning–Based Subabdominal MR Image Segmentation During Rectal Cancer Treatment: Exploiting Multiscale Feature Pyramid Network and Bidirectional Cross-Attention Mechanism

Conclusion: A multiscale feature pyramid network effectively reduces the semantic gap, and the bidirectional cross-attention mechanism facilitates feature alignment between the encoding and decoding stages....

A Dual-Segmentation Framework for the Automatic Detection and Size Estimation of Shrimp

In shrimp farming, determining the physical traits of shrimp is vital for assessing their health and growth. One of the critical traits is their size, as it serves as a key indicator of growth rates, biomass, and effective feed management. ...

Deep Learning-Based Prediction of the Axial Capacity of CFRP-Strengthened Concrete Columns

Fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) columns due to their high strength, durability, and ease of installation. Accurate prediction of the axial capacity of CFRP-strengthened concre...

Improved sports image classification using deep neural network and novel tuna swarm optimization

Abstract Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the de...

Theano: Deep Learning on GPUs with Python

In this paper, we present Theano1, a framework in the Python programming language for defining, optimizing and evaluating expressions involving high-level operations on ten-sors. Theano offers most of NumPy’s functionality, but adds automat...

A Simple Baseline for Bayesian Uncertainty in Deep Learning

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA), which computes the first moment of stochastic gradient desc...

Deep learning: A philosophical introduction

Abstract Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been larg...