Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy

“…In this study, soil continuous (pH and OC) and categorical variables (order and suborder) were predicted using deep learning–multi layer perceptron (DL-MLP) and one-dimensional convolutional neural networks (1D-CNN) for the entire state of Tamil Nadu, India. …”
Get full text

AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation by Marta Narigina, Agris Vindecs, Dušanka Bošković, Yuri Merkuryev, Andrejs Romanovs

Get full text

Tumor segmentation in whole-slide histology images using deep learning by V. A. Kovalev, V. A. Liauchuk, A. A. Kalinovski, M. V. Fridman

“…The procedure capitalizes on convolutional neural networks and Deep Learning methods. …”
Get full text

Interpreting CNN models for musical instrument recognition using multi-spectrogram heatmap analysis: a preliminary study by Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

“…This task poses significant challenges due to the complexity and variability of musical signals.MethodsIn this study, we employed convolutional neural networks (CNNs) to analyze the contributions of various spectrogram representations—STFT, Log-Mel, MFCC, Chroma, Spectral Contrast, and Tonnetz—to the classification of ten different musical instruments. …”
Get full text

Mapping Peatlands Combing Deep Learning With Sparse Spectral Unmixing Based on Zhuhai-1 Hyperspectral Images by Yulin Xu, Xiaodong Na

“…The mixed pixel problem, arising from the complex vegetation types of peatlands, poses a significant challenge for remote sensing-based peatland mapping. A convolution and transformer-based reconstruction and sparse unmixing algorithm that integrates deep learning and sparse spectral unmixing is proposed to address the spectral variability and spatial heterogeneity of the endmembers in hyperspectral datasets. …”
Get full text

Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis by Aikaterini Chatzara, Eirini Maliagkani, Dimitra Mitsopoulou, Andreas Katsimpris, Ioannis D. Apostolopoulos, Elpiniki Papageorgiou, Ilias Georgalas

Get full text

Early prediction of proton therapy dose distributions and DVHs for hepatocellular carcinoma using contour-based CNN models from diagnostic CT and MRI by Toshiya Rachi, Taku Tochinai

“…This study aimed to predict proton dose distributions using diagnostic CT (dCT) and diagnostic MRI (dMRI) with a convolutional neural network (CNN), enabling early treatment feasibility assessments. …”
Get full text

Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. Existing methods often fail to consider how to effectively combine their complementary advantages and fail to fully capture the internal information in the load sequence, leading to a decrease in accuracy. …”
Get full text

Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers by Johan Björkman, Sigrid Lagerroth, Jan Siarov, Filmon Yacob, Noora Neittaanmäki

Get full text

Rolling Bearing Fault Diagnosis Method Based on SWT and Improved Vision Transformer by Saihao Ren, Xiaoping Lou

Get full text

Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…We derive and feed heart rate variability (HRV) and pulse rate variability (PRV) features as auxiliary inputs to the framework for robustness. …”
Get full text

Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…To enhance the prediction accuracy of asphalt pavement rutting, this study introduces an end-to-end multivariate time series prediction model that integrates graph neural networks(GNN) with causal inference methodologies.The proposed model aims to effectively capture long-term and short-term temporal patterns as well as interdependencies among multiple variables. The model comprises four modules: global feature extraction, local feature extraction,causal inference, and dual-channel graph convolution. …”
Get full text

Voltage Trajectory Prediction of Photovoltaic Power Station Based on CNN-GRU by Yuqi FENG, Hui LI, Lijuan LI, Yanbo ZHOU, Mao TAN, Hanmei PENG

“…Then, the autocorrelation coefficient of the voltage time series and its maximal information coefficient (MIC) relative to external variables are calculated, and the correlations of the voltage time series with external variables in timing are analyzed. …”
Get full text

Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. …”
Get full text

Addressing Distribution Discrepancies in Pulsar Candidate Identification via Bayesian-neural-network-based Multimodal Incremental Learning by Yi Liu, Jing Jin, Hongyang Zhao, Zhenyi Wang, Yi Shen

Get full text

Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning by Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Somaye Mohammadi, Len Gelman

“…This study assesses the effectiveness of a simple convolutional neural network (SCNN) and a transfer learning-based convolutional neural network (TL-CNN) for diagnosing REB faults using time-domain signals, frequency-domain spectra, and envelope frequency spectrum analysis. …”
Get full text

GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…The model outperformed baseline methods with an R2 of 0.9165 and mean squared error (MSE) of 0.6893, significantly outperforming conventional approaches for optimizing production in variable growing conditions.…”
Get full text

A Temporal Network Based on Characterizing and Extracting Time Series in Copper Smelting for Predicting Matte Grade by Junjia Zhang, Zhuorui Li, Enzhi Wang, Bin Yu, Jiangping Li, Jun Ma

“…Secondly, we used a Time2Vec module to extract periodic information from the copper smelting process variables, incorporates time series processing directly into the prediction model. …”
Get full text

A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data by Zhibo Cui, Bifeng Hu, Songchao Chen, Nan Wang, Defang Luo, Jie Peng

“…Within this period, optimal feature subsets were extracted using variable selection algorithms. The performance of the partial least squares regression, random forest, and convolutional neural network–long short-term memory (CNN-LSTM) models was evaluated using a 10-fold cross-validation approach. …”
Get full text

Enhancing the genomic prediction accuracy of swine agricultural economic traits using an expanded one-hot encoding in CNN models by Zishuai Wang, Wangchang Li, Zhonglin Tang

“…Deep learning (DL) methods like multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) have been applied to predict the complex traits in animal and plant breeding. …”
Get full text