Academic Articles & Research Papers

Neural networks and deep learning

Privacy-Preserving Deep Learning

Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them into t...

Deep Learning on Graphs: A Survey

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characte...

A review on deep learning in medical image analysis

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is ef...

Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the ef...

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures ...

Controlled Signal Technique in VL‐NOMA Communication Under Interference‐Controlled Environment With Intelligent Reflecting Surfaces

ABSTRACT This paper proposes a controlled signal technique for visible light non‐orthogonal multiple access (VL‐NOMA) communication in an interference‐controlled environment with intelligent reflecting surfaces (IRS) for beyond 5G (B5G) and...

Neural Networks and Deep Learning

A Selective Overview of Deep Learning

Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a l...

Deployment optimization of laser chargers in self-organizing power transfer internet of things

Recently, the Internet of Things (IoT) has played an important role in many fields. Nevertheless, the fast and uneven energy consumption of IoT Devices (IoTDs) significantly limits the lifetime of IoT networks. One of the effective solution...

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 ...

DWSTr: a hybrid framework for ship-radiated noise recognition

The critical nature of passive ship-radiated noise recognition for military and economic security is well-established, yet its advancement faces significant obstacles due to the complex marine environment. The challenges include natural sou...

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 ...

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 ...

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...

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...

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...

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...

Development of Nuclear-thermal-mechanical Coupling Calculation Acceleration Method in Advanced Reactor Simulation Code FSAR

To meet the computational efficiency requirements for multi-physics simulations in the design and development of advanced reactor cores, a neural network-based acceleration method was investigated for multi-physics coupling calculations in ...