Academic Articles & Research Papers

Deep-learning-based ghost imaging

In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep...

Deep Learning for the Earth Sciences

Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only re...

Big Data Deep Learning: Challenges and Perspectives

Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language...

A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography

Abstract Hypertrophic Cardiomyopathy (HCM) and Cardiac Amyloidosis (CA) are two cardiac conditions that can advance to heart failure if left untreated, which present considerable diagnostic challenges due to their overlapping echocardiograp...

Identifying private pumping wells in a land subsidence area in Taiwan using deep learning technology and street view images

Study region: The Choushui River Fan, Taiwan. Study focus: Groundwater overdraft has led to not only groundwater depletion but also environmental disasters, such as subsidence and seawater intrusion in the Choushui River Alluvial Fan, Taiwa...

Biometric Signals Estimation Using Single Photon Camera and Deep Learning

The problem of performing remote biomedical measurements using just a video stream of a subject face is called remote photoplethysmography (rPPG). The aim of this work is to propose a novel method able to perform rPPG using single-photon av...

A 2D and 3D segmentation-based microstructure study on the role of brittle phases in diffusion brazed AISI 304L/NiCrSiFeMoB joints

Nickel-based filler metals are frequently used in high temperature vacuum diffusion brazing for austenitic stainless-steel joints when components are subjected to high static or dynamic loads, corrosive environments and elevated temperature...

An Automated Image-Based Dietary Assessment System for Mediterranean Foods

Goal: The modern way of living has significantly influenced the daily diet. The ever-increasing number of people with obesity, diabetes and cardiovascular diseases stresses the need to find tools that could help in the daily intake of the n...

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient...

Semantic-Aware Guided Low-Light Image Super-Resolution

The single image super-resolution based on deep learning has achieved extraordinary performance. However, due to inevitable environmental or technological limitations, some images not only have low resolution but also low brightness. The ex...

Anemia Modelling Using the Multiple Regression Analysis

The aim of this article is to forecast anemia from a population through biomedical variables of individuals using the multiple linear regression model. The study is conducted in terms of dataset consisting of 539 subjects provided from bloo...

TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts

Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their ...

Towards deep learning with segregated dendrites

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep le...

Regularization for Deep Learning: A Taxonomy

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying t...

Deep Learning for Generic Object Detection: A Survey

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a ...

An introduction to deep learning

Deep learning allows automatically learning multiple levels of representations of the underlying distribution of the data to be modeled. In this work, a specific implementation called stacked denoising autoencoders is explored. We contribut...

Deep Learning

DeepTox: Toxicity Prediction using Deep Learning

The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...

Deep Learning of Representations: Looking Forward

Spectral imaging with deep learning

The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational sp...