Academic Articles & Research Papers

Quantum Computing in Transportation Engineering: A Survey

Quantum computing introduces new computational methods that address complex optimization and machine learning challenges in transportation engineering. This literature survey reviews fundamental quantum principles, including qubits, superpo...

Evaluating Uses of Deep Learning Methods for Causal Inference

Logistic regression (LR) is a popular method that is used for estimating causal effects in observational studies using propensity scores. We examine the use of deep learning models such as the deep neural network (DNN), PropensityNet (PN), ...

Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus

Abstract Partial discharge (PD) detection is used to evaluate the insulation status of high‐voltage equipment. The most challenging aspect of traditional PD recognition is extracting features from the discharge signal. Accordingly, this stu...

A rapid fingerprint positioning method based on deep convolutional neural network for MIMO-OFDM systems

Abstract The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In ...

Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs

Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method The transfer learning method w...

Deep learning prediction of pathological complete response in breast cancer using Mamba architecture

Abstract Deep learning is capable of efficiently predicting the therapeutic efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. However, current methods predominantly rely on convolutional neural networks or transformer architectur...

The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for ...

Adaptive frequency band attention-guided CNN–BiLSTM for Spatial–Spectral–Temporal EEG emotion recognition

Abstract Electroencephalography (EEG)-based emotion recognition has gained increasing attention in affective computing because EEG provides high temporal resolution and reflects intrinsic neural activity. However, reliable emotion recogniti...

RadarDiT: An advanced radar echo extrapolation model for three gorges reservoir area via diffusion transformer

Study region: The Three Gorges Reservoir Area (TGRA) Study focus: TGRA faces increasing vulnerability to extreme precipitation events driven by complex convective weather systems. Radar echo extrapolation—predicting future precipitation pat...

Bone fracture detection through the two-stage system of Crack-Sensitive Convolutional Neural Network

Automated fracture detection is an essential part in a computer-aided tele-medicine system. Fractures often occur in human's arbitrary bone due to accidental injuries such as slipping. In fact, many hospitals lack experienced surgeons to di...

The Role and Application of Matrices in Artificial Intelligence: Foundations, Methods, and Advancements

Matrices are foundational to artificial intelligence (AI), serving as critical tools for data representation, manipulation, and transformation across various applications. From machine learning algorithms to neural network architectures, ma...

Neural Network Methods for Natural Language Processing

Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...

Pointwise Convolutional Neural Networks

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored....

Recent advances in convolutional neural networks

Photonic Neural Networks: A Survey

Photonic solutions are today a mature industrial reality concerning high speed, high throughput data communication and switching infrastructures. It is still a matter of investigation to what extent photonics will play a role in next-genera...

Quantum convolutional neural networks

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...

Understanding of a convolutional neural network

The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...

An Introduction to Convolutional Neural Networks

Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous for...

Neural Network-Based Discriminators for Single-Shot Readout of Superconducting Qubits in Non-Gaussian Conditions

Single-shot dispersive readout of superconducting qubits is a very important part of real-time quantum feedback and quantum error correction (QEC). Conventional discriminators such as thresholding and matched filtering are optimal under Gau...