Academic Articles & Research Papers

Unmixing-Guided Convolutional Transformer for Spectral Reconstruction

Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design. Additionally, models excl...

Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effect...

Interpretability-preserving knowledge distillation via multi-granular feature alignment for resource-efficient CNNs

Abstract The application of deep learning models in resource-constrained environments requires a trade-off among accuracy, efficiency, and interpretability a trilemma frequently neglected in conventional knowledge distillation (KD) approach...

Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eli...

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses signi...

Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks

The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs)...

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery

Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several ...

HIFU Tissue Degeneration Classification Based on Multifractal Detrending Fluctuation Analysis and Vision Transformer

For high-intensity focused ultrasound (HIFU) thermal ablation to be safe and effective, real-time, high-precision monitoring of tissue coagulative necrosis is essential. However, decoding the ultra-long, non-stationary radio frequency (RF) ...

Deep-learning seismology

Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processin...

Deep Learning

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vi...

An explainable machine learning framework for cross-dataset chronic kidney disease risk prediction using hybrid data balancing.

Availability-Aware Multitask Learning for Robust Clinical Prediction from Incomplete Electronic Health Records

This study presents NORIA, an availability-aware multitask learning framework for clinical prediction from incomplete electronic health records (EHRs). We analyzed a public dataset of 77,724 patients and 107 numerical features. To prevent t...

Machine Learning Methods in the Environmental Sciences

Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and th...

Data-driven performance prediction of polymer solar cells using multi-layer perceptron with Bayesian hyperparameter tuning

Polymer solar cells (PSCs) rely on labor-intensive trial-and-error experiments to optimize power conversion efficiency (PCE), due to complex coupling of molecular electronic properties, blend morphology and fabrication parameters. This work...

Machine learning insight in quality control and maintenance management of drilling mud pumps

Abstract This study proposes a machine learning-based framework for predictive maintenance and quality control of drilling mud pumps, targeting critical operational challenges such as equipment failure, downtime, and high maintenance costs....

Malware Detection via Extended Label Propagation Through Graph Inference

In this paper, we model the malware detection problem as a graph inference problem, and develop a novel belief propagation approach within a semi-supervised learning scheme that fully makes use of files' and hosts' connections to detect mal...

Machine Learning and Data Mining Methods in Diabetes Research

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, ap...

Machine learning in breast MRI

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmenta...

Using fuzzy and machine learning iterative optimized models to generate the flood susceptibility maps: case study of Prahova River basin, Romania

AbstractIn this work, the vulnerability to flooding in the Prahova River basin was calculated and analyzed using advanced methods and techniques. Thus, 2 hybrid models represented by Iterative Classifier Optimizer – Multiclass Alternating D...

Exploiting Content Characteristics for Explainable Detection of Fake News

The proliferation of fake news threatens the integrity of information ecosystems, creating a pressing need for effective and interpretable detection mechanisms. Recent advances in machine learning, particularly with transformer-based models...