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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data by Le Ngoc Son, Nguyen The Duc, Sumihiko Murata, Phan Ngoc Trung

“…After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. …”
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Physical-aware model accuracy estimation for protein complex using deep learning method by Haodong Wang, Meng Sun, Lei Xie, Dong Liu, Guijun Zhang

“…Finally, these features are fed into a fused network architecture employing equivalent graph neural network and ResNet network to estimate residue-wise model accuracy. …”
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Single-shot super-resolved fringe projection profilometry (SSSR-FPP): 100,000 frames-per-second 3D imaging with deep learning by Bowen Wang, Wenwu Chen, Jiaming Qian, Shijie Feng, Qian Chen, Chao Zuo

“…SSSR-FPP uses only one pair of low signal-to-noise ratio (SNR), low-resolution, and pixelated fringe patterns as input, while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural network. Our approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras, while “regenerating” the lost spatial resolution through deep learning. …”
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Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor... by Mehdi Tlija, Muhammad Sana, Anamta Khan, Sana Hassan, Muhammad Umar Farooq

“…Multiple artificial neural network (ANN) architectures are generated to accurately model the non-linearity of the process for better prediction of key characteristics. …”
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Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data by Shejun Deng, Hongru Yu, Caoye Lu

“…To prevent and control public transport safety accidents in advance and guide the safety management and decision-making optimization of public transport vehicles, based on the forewarning and other multisource data of public transport vehicles in Zhenjiang, holographic portraits of public transport safety operation characteristics are constructed from the perspectives of time, space, and driver factors, and a prediction model of fatigue driving and driving risk of bus drivers based on BP neural network is constructed. Finally, model checking and virtual simulation experiments are carried out. …”
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Testing General Relativity Using Large-scale Structure Photometric Redshift Surveys and the Cosmic Microwave Background Lensing Effect by Shang Li, Jun-Qing Xia

“…In this formulation, we reconstruct the growth rate of structure, fσ _8 ( z ), using the artificial neural network method, while simultaneously utilizing model-independent constraints on the parameter bσ _8 ( z ), directly obtained from the DES collaboration. …”
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Analyzing the impact of non-Newtonian nanofluid flow on pollutant discharge concentration in wastewater management using an artificial computing approach by Sidra Jubair, Jie Yang, Bilal Ali, Bandar Bin-Mohsin, Hamiden Abd El-Wahed Khalifa

“…These equations (ODEs) are solved using the Levenberg Marquardt back-propagation optimization algorithm (LMBOA) of the artificial neural network (ANN). The Matlab package “bvp4c” is used for generating the dataset in order to validate the results of the ANN-LMBOA. …”
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Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only by Muhammad Ahmad Sultan, Wala Saadeh

“…Finally, RR and BP are estimated using breath counting and a neural network regression model, respectively. <italic>Results:</italic> The proposed approach outperforms the current state-of-the-art in both RR and BP. …”
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Optimizing Solid Oxide Fuel Cell Performance Using Advanced Meta-Heuristic Algorithms by Siva Ram Rajeyyagari, Srinivas Nowduri

“…Our approach utilizes a Radial Basis Function (RBF) neural network trained with experimental data encompassing five input parameters: oxygen concentration, operating temperature, instrumentation, electrolyte thickness, and electrical current, with the goal of optimizing the single output parameter of power. …”
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Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan by Kai Lin, Qingming Zhan, Wei Xue, Yulong Shu, Yixiao Lu

“…Then, the ANN (artificial neural network)–CA (Cellular Automata) model is employed to establish three distinct development scenarios (Ecological Priority, Tight Growth, and Natural Growth) to predict future urban expansion. …”
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QAR Data Imputation Using Generative Adversarial Network with Self-Attention Mechanism by Jingqi Zhao, Chuitian Rong, Xin Dang, Huabo Sun

“…In addition, we modify the basic structure of GAN by using an autoencoder as the generator and a recurrent neural network as the discriminator. The missing values in the QAR data are imputed by using the adversarial relationship between generator and discriminator. …”
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Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan by Hurriyatul Fitriyah, Rizal Maulana

“…Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. …”
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Empirical analysis of control models for different converter topologies from a statistical perspective by Hole Shreyas Rajendra

“…A wide variety of such models are proposed by researchers, that include, but are not limited to, bioinspired techniques for rating selection, Neural Network based models for load-based component selection, etc. …”
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Analysis of Gas Pipeline Failure Factors Based on the Novel Bayesian Network by Machine Learning Optimization by Shuangqing Chen, Shun Zhou, Zhe Xu, Yongbin Liu, Bing Guan, Xiaoyu Jiang, Wencheng Li

“…Secondly, by establishing a neural network model, the value of the CPT of the Bayesian network is determined. …”
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Impact of lens autofluorescence and opacification on retinal imaging by Frank G Holz, Maximilian Pfau, Monika Fleckenstein, Raffael Liegl, Geena C Rennen, Marc Vaisband, Jan Hasenauer

“…A regression model for predicting image quality was developed using a convolutional neural network (CNN). Correlation analysis was conducted to assess the association of lens scores, with retinal image quality derived from human or CNN annotations.Results Retinal image quality was generally high across all imaging modalities (IR (8.25±1.99) &gt;GAF &gt;BAF (6.6±3.13)). …”
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