Suchergebnisse - recursive algorithms for radial polynomial generation

Fast and Robust Simulation of Atmospheric Phase Screen by Zernike Polynomials with Recursive Radial Formulas

Autoren: Yuefeng Li Benchu Lu Huijie Xue et al.

Quelle: Physics ; Volume 7 ; Issue 4 ; Pages: 58

Schlagwörter: phase screen, atmospheric simulation, Zernike polynomials, numerical stability in high-order Zernike polynomial evaluation, recursive algorithms for radial polynomial generation

Dateibeschreibung: application/pdf

Relation: Computational Physics; https://dx.doi.org/10.3390/physics7040058

Verfügbarkeit: https://doi.org/10.3390/physics7040058

Finite precision error analysis of Zernike moments computation schemes and a new, efficient, robust recursive algorithm.

Autoren: Chalatsis, Constantinos Papaodysseus, Constantin Arabadjis, Dimitris et al.

Quelle: Digital Signal Processing. Aug2018, Vol. 79, p75-90. 16p.

Schlagwörter: *ZERNIKE polynomials, *SIGNAL quantization, *SIGNAL processing, *DIGITAL signal processing, *RECURSIVE functions

Optimizing accuracy and dimensionality: a swarm intelligence strategy for robust cancer genomics classification.

Autoren: Yaqoob, Abrar Mir, Mushtaq Ahmad Lakshmi, R. Vijaya et al.

Quelle: BioData Mining; 11/18/2025, Vol. 18 Issue 1, p1-23, 23p

Schlagwörter: FEATURE selection, SUPPORT vector machines, GENE expression profiling, CLASSIFICATION, SWARM intelligence, CANCER genes, INDIVIDUALIZED medicine

The White Puzzle: A Framework for Computation as a Recursive‑Harmonic Phenomenon

Autoren: KULIK, DEAN

Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex—An Approach with Machine Learning Algorithms.

Autoren: Morgan-Benita, Jorge A. Celaya-Padilla, José M. Luna-García, Huizilopoztli et al.

Quelle: Diagnostics (2075-4418); Aug2024, Vol. 14 Issue 15, p1623, 43p

Schlagwörter: ARTIFICIAL neural networks, GENDER differences (Sociology), MACHINE learning, TYPE 2 diabetes, DIASTOLIC blood pressure

Prediction of preoperative tumor-related epilepsy using XGBoost radiomics models with 4 MRI sequences.

Autoren: George, Reuben Chow, Li Sze Lim, Kheng Seang et al.

Quelle: Biomedical Physics & Engineering Express; May2025, Vol. 11 Issue 3, p1-17, 17p

Implementation of Chaotic Synchronization and Artificial Neural Networks in Modified OTP Scheme for Image Encryption.

Autoren: Stoycheva, Hristina Mihalev, Georgi Sadinov, Stanimir et al.

Quelle: Journal of Imaging; Apr2025, Vol. 11 Issue 4, p121, 31p

The Silent Language: A Review of Mathematics in the Realm of Gas Sensing.

Autoren: Hingangavkar, Gajanan M.

Quelle: Sensing & Imaging; 9/23/2025, Vol. 26 Issue 1, p1-41, 41p

Single_cell_GRN: gene regulatory network identification based on supervised learning method and Single-cell RNA-seq data.

Autoren: Yang, Bin Bao, Wenzheng Chen, Baitong et al.

Quelle: BioData Mining; 6/11/2022, Vol. 15 Issue 1, p1-18, 18p

Schlagwörter: GENE regulatory networks, SUPERVISED learning, RNA sequencing, RADIAL basis functions, SUPPORT vector machines, K-nearest neighbor classification

Learning image derived PDE-phenotypes from fMRI data.

Autoren: Bica, Ion Trang, Ryan Hu, Rui et al.

Quelle: Brain Informatics; 12/8/2025, Vol. 12 Issue 1, p1-16, 16p

The 34th Anual Meeting of the Asian Pacific Association for the study of the liver.

Quelle: Hepatology International; 2025 Suppl 1, Vol. 19, p1-1554, 1554p

Inferring inherent optical properties of sea ice using 360° camera radiance measurements.

Autoren: Larouche, Raphaël Raulier, Bastian Katlein, Christian et al.

Quelle: Cryosphere; 2025, Vol. 19 Issue 10, p4785-4804, 20p

Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms.

Autoren: Das, Prabal Sachindra, D. A. Chanda, Kironmala

Quelle: Water Resources Management; Dec2022, Vol. 36 Issue 15, p6043-6071, 29p

Schlagwörter: FEATURE selection, KRIGING, GENETIC programming, PRECIPITABLE water, BAYESIAN analysis, ALGORITHMS

Geografische Kategorien: PERSIAN Gulf

Beyond imbalance: advancing breast cancer diagnosis with synthetic data and ML modeling.

Autoren: Abbaszadeh Bavil Soflaei, Mohammad Reza Samadzamini, Karim Salehpour, Arash

Quelle: Network Modeling & Analysis in Health Informatics & Bioinformatics; 5/21/2025, Vol. 14 Issue 1, p1-20, 20p

Applications of Machine Learning and Remote Sensing in Soil and Water Conservation.

Autoren: Kim, Ye Inn Park, Woo Hyeon Shin, Yongchul et al.

Quelle: Hydrology (2306-5338); Nov2024, Vol. 11 Issue 11, p183, 26p

Schlagwörter: SOIL conservation, MACHINE learning, WATER conservation, SOIL management, SOIL moisture

Transforming Cancer Classification: The Role of Advanced Gene Selection.

Autoren: Yaqoob, Abrar Mir, Mushtaq Ahmad Jagannadha Rao, G. V. V. et al.

Quelle: Diagnostics (2075-4418); Dec2024, Vol. 14 Issue 23, p2632, 19p

Schlagwörter: PARTICLE swarm optimization, TUMOR classification, SUPPORT vector machines, DISEASE management, CANCER research

Data‐driven artificial intelligence‐based streamflow forecasting, a review of methods, applications, and tools.

Autoren: Jahanbani, Heerbod Ahmed, Khandakar Gu, Bruce

Quelle: Journal of the American Water Resources Association; Dec2024, Vol. 60 Issue 6, p1095-1119, 25p

Schlagwörter: MACHINE learning, ARTIFICIAL intelligence, STREAM measurements, STREAMFLOW, ACQUISITION of manuscripts

An Overview of Machine-Learning Methods for Soil Moisture Estimation.

Autoren: Taheri, Mercedeh Bigdeli, Mostafa Imanian, Hanifeh et al.

Quelle: Water (20734441); Jun2025, Vol. 17 Issue 11, p1638, 35p

Schlagwörter: SOIL moisture, ARTIFICIAL intelligence, SUSTAINABLE agriculture, REMOTE sensing, HYDROLOGY

Unsupervised image velocimetry for automated computation of river flow velocities.

Autoren: Perks, Matthew T. Hortobágyi, Borbála Everard, Nick et al.

Quelle: Hydrology & Earth System Sciences; 2025, Vol. 29 Issue 15, p3727-3743, 17p

From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications.

Autoren: Gkintoni, Evgenia Aroutzidis, Anthimos Antonopoulou, Hera et al.

Quelle: Brain Sciences (2076-3425); Mar2025, Vol. 15 Issue 3, p220, 74p

Schlagwörter: MACHINE learning, EMOTION recognition, COGNITIVE neuroscience, RECURRENT neural networks, DEEP learning