A Mobile Image-Driven PM2.5 Estimation Framework Using Deep Learning Techniques by Anupam Kamble, Somrawee Aramkul, Paskorn Champrasert

“…The EfficientNet-B1 neural network is applied in the image feature vector extraction process. …”
Get full text

A Fusion Method Incorporating Dual-Attention Mechanism and Transfer Learning Into UNet++ for Remote Sensing Image Coastline Extraction by Yanru Song, Bai Xue, Yueyue Meng, Xiang Qin, Yixiao Li, Qi Liu

“…This paper applies a deep convolutional neural network to the problem of sea-land segmentation in high-spatial resolution remote sensing images and innovates upon the classic encoder-decoder architecture. …”
Get full text

Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…Results: The random forest regressor (MAE: 0.416 mSv; MAPE: 7%; and R²: 0.98) showed best performances over the neural network and the support vector machine. However, all three machine learning algorithms outperformed effective dose estimation using k-factors (MAE: 2.06; MAPE: 26%) or multiple linear regression (MAE: 0.98; MAPE: 44.4%). …”
Get full text

Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…<h4>Methods</h4>Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. …”
Get full text

Prediction of the cutting tool wear during dry hard turning of AISI D2 steel by using models based on Learning process and GA polyfit by Khaled Djellouli, Kamel Haddouche, Mostefa Belarbi, Zoubir Aich

“…For this purpose, the current research focuses on the development of predictive models of flank wear based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Polynomial Fit using Genetic Algorithm (GAPOLYFITN). …”
Get full text

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore, Osamah Ibrahim Khalaf, Sarita Simaiya, Carlos Andrés Tavera Romero, Ghaida Muttashar Abdulsahib, Poongodi M, Dinesh Kumar

“…The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. …”
Get full text

Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images by Soichiro Kuwayama, Yuji Ayatsuka, Daisuke Yanagisono, Takaki Uta, Hideaki Usui, Aki Kato, Noriaki Takase, Yuichiro Ogura, Tsutomu Yasukawa

“…The remaining 100 images were used to evaluate the trained convolutional neural network (CNN) model. Results. Automated disease detection showed that the first candidate disease corresponded to the doctor’s decision in 83 (83%) images and the second candidate disease in seven (7%) images. …”
Get full text

Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit by Zhongyuan Liu, Xian Zhang, Diquan Li, Shupeng Liu, Ke Cao

“…Experiments demonstrate that the optimization capacity of the IDBO algorithm is conspicuously superior to other intelligent optimization algorithms, and the IDBO-GRU algorithm surpasses the probabilistic neural network (PNN) and the GRU algorithm in the denoising accuracy of WFEM data. …”
Get full text

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series by Hui Hu, Jianfeng Zhang, Tao Li

“…The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. …”
Get full text

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent by Korfmann, Kevin, Sellinger, Thibaut Paul Patrick, Freund, Fabian, Fumagalli, Matteo, Tellier, Aurélien

“…Our findings stress the aptitude of neural networks to leverage information from the ARG for inference but also the urgent need for more accurate ARG inference approaches.…”
Get full text

Multiscale Feature-Enhanced Water Body Detector of Truncated Gaussian Clutter in SAR Imagery by Bo Zhu, Yuli Xia, Yongsheng Zhou, Xiaoning Lv, Minqin Liu

“…Based on metrics of accuracy, <italic>F</italic>1, and mean of intersection over union, TGCFeWD achieves the best performance (92.4&#x0025;, 82.4&#x0025;, and 80.1&#x0025; for all data with five water body types) compared to several traditional methods, and even outperforms some neural-network-based methods in certain scenarios. The results are validated on the HISEA flooding dataset.…”
Get full text

Machine learning and AVO class II workflow for hydrocarbon prospectivity in the Messinian offshore Nile Delta Egypt by Nadia Abd-Elfattah, Aia Dahroug, Manal El Kammar, Ramy Fahmy

“…Machine learning techniques, specifically neural network models, were trained to differentiate seismic features such as low-amplitude gas sand from background-amplitude water sand and shale. …”
Get full text

Lighting Spectrum Optimization With Deep Learning for Moss Species Classification by Kenichi Ito, Pauli Falt, Markku Hauta-Kasari, Shigeki Nakauchi

“…Hence, we propose a method for obtaining spectral information on moss in the forest using a deep learning model to train convolutional neural network models while optimizing a suitable light source for moss identification. …”
Get full text

Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data by Hao Wang, Rong Yang, Jianxin He, Qiangyu Zeng, Taisong Xiong, Zhihao Liu, Hongfei Jin

“…To address this limitation, we introduce the Dual-Attention Recurrent Neural Network (DA-RNN), a model that combines satellite infrared (IR) data with radar-derived vertically integrated liquid (VIL) content. …”
Get full text

Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients by Dominique J. Monlezun, Alfred T. Samura, Ritesh S. Patel, Tariq E. Thannoun, Prakash Balan

“…Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. …”
Get full text

AI-powered estimation of tree covered area and number of trees over the Mediterranean island of Cyprus by Anna Zenonos, Sizhuo Li, Martin Brandt, Jean Sciare, Philippe Ciais

“…This study presents a deep learning neural network model applied to high resolution (10 cm) airborne images collected during the year 2019, to generate segmented tree crowns and the number of individual trees over selected areas of Cyprus, including a large national forest park, a forest park in the capital city, and a small urban area, encompassing a total studied area of 107km2. …”
Get full text

Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data by Vahid Nourani, Ramin Ahmadi, Yongqiang Zhang, Dominika Dąbrowska

“…This study applies several machine learning (ML) models—including a backpropagation neural network (BPNN), an adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and long short-term memory (LSTM)—to simulate PML-V2 ET in the Ahar Chay basin, Northwestern Iran. …”
Get full text

Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios by Hong-Gang Wang, Shan-Shan Wang, Ruo-Yu Pan, Sheng-Li Pang, Xiao-Song Liu, Zhi-Yong Luo, Sheng-Pei Zhou

“…Compared with BP neural network (BPNN) and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. …”
Get full text

Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities by Katja Kustura, David Conti, Matthias Sammer, Michael Riffler

“…We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. …”
Get full text

A Lightweight CNN-Transformer Implemented via Structural Re-Parameterization and Hybrid Attention for Remote Sensing Image Super-Resolution by Jie Wang, Hongwei Li, Yifan Li, Zilong Qin

“…Remote sensing imagery contains rich information about geographical targets, and performing super-resolution (SR) reconstruction on such images requires greater feature representation capabilities. Convolutional neural network (CNN)-based methods excel at extracting intricate local features but fall short in terms of capturing global representations. …”
Get full text