Academic Articles & Research Papers

Treatment Effect Estimation in Survival Analysis Using Copula-Based Deep Learning Models for Causal Inference

This paper presents the use of Copula-based deep learning with Horvitz–Thompson (HT) weights and inverse probability of treatment weighting (IPTW) for estimating propensity scores in causal inference problems. This study compares the perfor...

Deep Learning for Spatio-Temporal Data Mining: A Survey

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temp...

Deep Learning and Its Application to LHC Physics

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex ...

Advances in Deep Learning

Deep Learning: The Good, the Bad, and the Ugly

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains...

Testing the Role of Temporal Attention in Speech: Pretarget Alpha Predicts Memory Encoding Rather Than Effects of Linguistic Focus.

Neural Measures of Human Decision-Making Track Evidence Accumulation in Learned Space.

Refinement of Nucleus Accumbens Neuronal Dynamics during Cocaine Self-Administration Training.

Distinct activity in prefrontal projections promotes temporal control of action.

Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative ...

Greedy Broad Learning System

In order to overcome the extremely time-consuming drawback of deep learning (DL), broad learning system (BLS) was proposed as an alternative method. This model is simple, fast, and easy to update. To ensure the fitting and generalization ab...

A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN

In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event fr...

Predicting Shield Machine Penetration Rate Using the CTCM-DELM Algorithm

The penetration rate (PR) is a critical indicator affecting the safety and cost of shield tunnel construction. However, due to the complexity of geological conditions and the nonlinear nature of tunneling parameters, traditional prediction ...

Transmission line fault detection and classification based on SA-MobileNetV3

Accurate fault detection and classification help to analyze fault causes and quickly restore faulty phases. Deep learning can automatically extract fault features and identify fault types from the original three-phase voltage and current si...

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves ...

Heart Disease Detection using ML and ES (Smart Wearable Health Monitoring System)

This paper proposed a smart wearable system for heart disease detection using machine learning and embedded systems. A smart wearable system that able to monitor the heart beat rate condition of patient. The heart beat rate is detected usin...

DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity

Abstract Efficient prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery. Recently, deep learning approaches leveraging 3D complexes have shown strong potential for drug-target affinity prediction. However, mos...

Deep learning with convolutional neural network in radiology

Deep learning in holography and coherent imaging

Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of...

The unreasonable effectiveness of deep learning in artificial intelligence

Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiqui...