Academic Articles & Research Papers

Machine and deep learning methods for radiomics

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has...

Deep learning for undersampled MRI reconstruction

This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is u...

Teaching for Deep Learning

The authors have been engaged in research focused on students' depth of learning as well as teachers' efforts to foster deep learning. Findings from a study examining the teaching practices and student learning outcomes of sixtyfour teacher...

Pulling out all the tops with computer vision and deep learning

Abstract We apply computer vision with deep learning — in the form of a convolutional neural network (CNN) — to build a highly effective boosted top tagger. Previous work (the “DeepTop” tagger of Kasieczka et al) has shown that a CNN-based ...

Exhaust Emissions from Gasoline Vehicles with Different Fuel Detergency and the Prediction Model Using Deep Learning

Enhancing gasoline detergency is pivotal for enhancing fuel efficiency and mitigating exhaust emissions in gasoline vehicles. This study investigated gasoline vehicle emission characteristics with different gasoline detergency, explored syn...

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

Abstract Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex ...

SPAF-SegNeXt: Prior-Guided Attention and Frequency-Spatial Hybrid Fusion for Multimodal Sugarcane Segmentation in Remote Sensing

Accurate monitoring of the spatial extent of sugarcane cultivation is critical for optimizing land-use efficiency and regulating the sugar industry. However, sugarcane cultivation areas are characterized by complex topography and fragmented...

Integrating multi-source geoscience data with RF-weighted conditional variational autoencoders (CVAE) for porphyry copper prospectivity mapping: an application to the Ardestan district, Central Iran

Abstract Porphyry Cu deposits in the Urmia–Dokhtar magmatic belt constitute important exploration targets, yet the heterogeneous and multi-scale nature of geological, geochemical, and remote sensing data introduces substantial uncertainty i...

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrat...

A Comparison of Two Neural Network Based Methods for Human Activity Recognition

In this paper, two different methods of human activity recognition based on video signals are introduced. The first method explores the effectiveness of combining feature descriptors obtained by local descriptors and artificial neural network ...

Recent advances in deep learning for object detection

Deep Learning with Microfluidics for Biotechnology

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural...

Deep Learning for Image Super-Resolution: A Survey

Image Super-Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning ...

Computational Linguistics and Deep Learning

Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pun...

A Survey on Deep Learning for Named Entity Recognition

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language ...

A Review of Deep Learning Research

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and on...

DEEP LEARNING

A guide to deep learning in healthcare

Deep Learning-Based Channel Estimation

In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time-frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the...

The class imbalance problem in deep learning