Absorbing Aerosol Effects on Hyperspectral Surface and Underwater UV Irradiances from OMI Measurements and Radiative Transfer Computations by Alexander Vasilkov, Nickolay Krotkov, Matthew Bandel, Hiren Jethva, David Haffner, Zachary Fasnacht, Omar Torres, Changwoo Ahn, Joanna Joiner

“…The satellite UV surface irradiance algorithms combine satellite retrievals of extraterrestrial solar irradiance, cloud/surface reflectivity, aerosol optical depth, and total column ozone with radiative transfer computations. …”
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A clustering-based multi-setting overcurrent protection approach with optimisation and experimental validation for distribution networks by Feras Alasali, Mohamed Salem, Haytham Y. Mustafa, Hassen Loukil, Naser El-Naily, Abdelaziz Salah Saidi, William Holderbaum

“…This work involves integrating clustering-based topology grouping with bio-inspired optimisation techniques, specifically the Water Cycle Algorithm (WCA) and the Transit Search Algorithm (TSA), to assign optimised Time Multiply Settings (TMS) values for each relay group, ensuring fast and coordinated protection without reliance on continuous communication or centralised control. …”
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Information support for decision making in problem situation description by S. F. Lipnitsky

“…As an implementation of the proposed model, the following algorithms have been developed: an algorithm for creating a dictionary of communicative fragments; algorithms for creating fragment-slot templates for sentences, texts and subject areas; an algorithm of user information support. …”
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Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization by Adewale Amosu, Dung Bui, Oluwapelumi Oke, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Najmudeen Sibaweihi, William Ampomah

“…In this study, we develop a workflow that mitigates the variability in results produced by different clustering algorithms using a committee machine. Using several unsupervised machine learning methods, including k-means, k-median, hierarchical clustering, spectral clustering, and the Gaussian mixture model, we predict electrofacies from wireline well log data and generate their 3D vertical and lateral distributions and inferred geological properties. …”
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Forecasting readmission in COVID-19 patients utilizing blood biomarkers and machine learning in the Hospital-at-Home program by Maria Glòria Bonet-Papell, Maria Glòria Bonet-Papell, Georgina Company-Se, María Delgado-Capel, Beatriz Díez-Sánchez, Lourdes Mateu-Pruñosa, Roger Paredes-Deirós, Jordi Ara del Rey, Lexa Nescolarde

“…Various classification algorithms (bagged trees, KNN, LDA, logistic regression, Naïve Bayes, and the support vector machine [SVM]) were implemented to predict readmission, with performance evaluated using accuracy, sensitivity, specificity, F1 score, and the Matthews Correlation Coefficient (MCC).ResultsSignificant differences were observed in IL-6, Hs-TnT, CRP (p < 0.001), and ferritin (p < 0.01) between the first day of conventional hospitalization and the first day of HaH for patients who were not readmitted. …”
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Segmentation in object-oriented encoding and multi-view images transmission by V. Yu. Tsviatkou

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Evaluation of the angular coordinates in survey radar stations with subsystems for spatial noise compensation by S. V. Kozlov, Vu. Thanh Ha

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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…Integration with clinical characteristics further enhances predictive accuracy, offering a non-invasive tool for preoperative risk stratification and personalized treatment planning.…”
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Gauge-fixing quantum density operators at scale by Amit Jamadagni, Eugene Dumitrescu

“…We provide a theory, algorithms, and simulations of nonequilibrium quantum systems using a one-dimensional (1D) completely positive (CP), matrix-product (MP) density-operator (ρ) representation. …”
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Environmental performance driven optimization of urban modular housing layout in Singapore by Xiaoyu Shen, Xue Ye

“…It integrates three-dimensional cellular automata, parametric performance simulation, genetic optimization algorithms, and hierarchical clustering algorithms. …”
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Facial expression recognition based on adaptation of the classifier to videos of the user by E.N. Churaev, A.V. Savchenko

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A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. by Eric Nyarko, Edmund Fosu Agyemang, Ebenezer Kwesi Ameho, Louis Agyekum, José María Gutiérrez, Eduardo Alberto Fernandez

“…<h4>Method</h4>We used a MaxDiff statistical experiment design to collect data, and six supervised machine learning models were applied to predict responses whose performance showed an advantage over the other through 6921 data points partitioned using the hold-back validation method, with 70% training and 30% validation. …”
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Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection by Manping Wang, Yang Lu, Man Liu, Fuhui Cui, Rongke Gao, Feifei Wang, Xiaozhe Chen, Liandong Yu

“…This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. …”
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Multi-UAV DMPC Cooperative Guidance with Constraints of Terminal Angle and Obstacle Avoidance by Zijie Jiang, Xiuxia Yang, Cong Wang, Yi Zhang, Hao Yu

“…This paper studies the salvo attack problem for multiple unmanned aerial vehicles (UAVs) against a maneuvering target, and a guidance scheme based on distributed model predictive control (DMPC) is presented to achieve cooperative interception with constraints of terminal impact angle and no-fly zone (or obstacle) avoidance. …”
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Pseudo-Labeling and Time-Series Data Analysis Model for Device Status Diagnostics in Smart Agriculture by Minwoo Jung, Dae-Young Kim

“…This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. …”
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Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer by Kaiyuan Hou, Xiaotian Zhang, Junjie Yang, Jiyun Hu, Guangzhi Yao, Jiannan Zhang

“…The filtered features are then input into the FEDformer model, providing preliminary short-term and long-term sequence prediction results. Finally, a point-by-point forecasting method based on a tree model generates multi-point load prediction results by training multiple LightGBM models. …”
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Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals by Shuting Xu, Ravinesh C. Deo, Oliver Faust, Prabal D. Barua, Jeffrey Soar, Rajendra Acharya

“…Additionally, a majority voting ensemble technique was employed to aggregate the predictions of the various classifiers for improved diagnostic reliability. …”
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Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics by Priyanka Dhurve, Ayon Tarafdar, Vinkel Kumar Arora

“…In general, the prediction of drying kinetics and other drying parameters was more precise in the ANN technique as compared to semiempirical models. …”
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Universal Modeling for Non-Destructive Testing of Soluble Solids Content in Multi-Variety Blueberries Based on Hyperspectral Imaging Technology by Lingqi Meng, Guoliang Chen, Dayang Liu, Ning Tian

“…In view of the demand for rapid non-destructive testing of blueberry SSC and the shortcomings of the existing single-variety testing models in cross-variety applications, a universal prediction model construction method based on hyperspectral imaging (HSI) technology is proposed in this study. …”
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Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression by Heba M. Afify, Kamel K. Mohammed, Aboul Ella Hassanien

“…Abstract Leveraging deep learning (DL) approaches in genomics data has led to significant advances in cancer prediction. The continuous availability of gene expression datasets over the preceding years has made them one of the most accessible sources of genome-wide data, advancing cancer bioinformatics research and advanced prediction of cancer genomic data. …”
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