Showing 781 - 800 results of 805 for search 'road developing algorithm', query time: 0.08s Refine Results
  1. 781
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    Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

    Published 2024-11-01
    “…These predictors were used to develop a prediction model, visually represented through a nomogram. …”
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  3. 783

    Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis by Gan Y, Yue W, Sun J, Yang D, Fang C, Zhou Z, Yin J, Zhou H

    Published 2025-04-01
    “…Yansha Gan,1,* Weihua Yue,2,* JiaoJiao Sun,1 DanTing Yang,1 ChunXia Fang,1 Zhenhe Zhou,1 JiaJun Yin,1 Hongliang Zhou3 1The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, People’s Republic of China; 2National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, 100191, People’s Republic of China; 3Department of Psychology, The Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, 214100, People’s Republic of China*These authors contributed equally to this workCorrespondence: JiaJun Yin, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, People’s Republic of China, Email yinjiajun@jiangnan.edu.cn Hongliang Zhou, Department of Psychology, The Affiliated Hospital of Jiangnan University, No. 200, Huihe Road, Binhu District, Wuxi City, Jiangsu Province, People’s Republic of China, Email Hongliangzh2022@hotmail.comObjective: This study aimed to identify DNA methylation patterns associated with Very Late-Onset Schizophrenia-like Psychosis (VLOSLP) and to develop methylation-based biomarkers that differentiate VLOSLP from Schizophrenia (SCZ) and Alzheimer’s Disease (AD).Methods: We analyzed methylation microarray datasets (n = 1218) from SCZ and AD patients obtained from the GEO database. …”
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  4. 784

    An Enhanced Framework for Assessing Pluvial Flooding Risk with Integrated Dynamic Population Vulnerability at Urban Scale by Xinyi Shu, Chenlei Ye, Zongxue Xu, Ruting Liao, Pengyue Song, Silong Zhang

    Published 2025-02-01
    “…This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. …”
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  5. 785

    Genetic Fuzzy System untuk Klasifikasi Tutupan Lahan Berdasarkan Foto Udara Unmanned Aerial Vehicle (UAV) by Budi Darma Setiawan, Alfi Nur Rusydi

    Published 2023-12-01
    “…The input of this program is the red (R), green (G), and blue (B) values of each pixel in the image, and the output is the class in which the pixels are grouped (soil, water, vegetation, buildings, and roads). From the experimental results, the highest fitness value was obtained up to 0.84 or 84%. …”
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  6. 786

    Optimization of Grain Flows in the “Field-Elevator” Chain During the Harvesting of Grain Seeds in the Republic of Kazakhstan by A. S. Alchimbaeva

    Published 2019-12-01
    “…(Results and discussion) The authors have proposed an algorithm for calculating the parameters of typical (model) seed farms in Kazakhstan based on statistics obtained from the Statistical Office of the Republic. …”
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    Pemodelan Spasial Lahan Terbangun Kota Jambi by Ayu Mardalena, Supriatna Supriatna, Muhammad Dimyati

    Published 2025-02-01
    “…This growth is primarily concentrated in flat to gently sloping areas, with proximity to roads identified as the most influential factor. …”
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  16. 796

    Multi-scenario Dynamic Simulation and Optimization of Urban Ventilation Environment: A Case Study of Taiyuan Metropolitan Area by Junda HUANG, Yuncai WANG

    Published 2025-05-01
    “…Secondly, based on previous research and considering the natural environment and socio-economic development of the research area, DEM, slope, distance to primary roads, distance to secondary roads, distance to tertiary roads, population density, GDP, and building density are selected as driving factors in this research to investigate the transformation pattern of land use types. …”
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  17. 797

    Sustainable Management of Natural Resources at Disaggregated Levels with Insights from Landscape Dynamics by T V Ramachandra, Paras Negi, Tulika Mondal, Bharath Settur

    Published 2025-07-01
    “…LU transitions are quantified using temporal-spatial data acquired through space-borne sensors through supervised machine learning classifiers based on the non-parametric algorithm Random Forest (RF). Land use dynamics assessment reveals that paved surfaces (area under buildings, roads) have increased from 186.22 sq. km (in 1973) to 1085.12 sq. km (in 2022). …”
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  18. 798

    Reliability and Security Analysis of Artificial Intelligence-Based Self-Driving Technologies in Saudi Arabia: A Case Study of Openpilot by Faisal S. Alsubaei

    Published 2022-01-01
    “…It can also help car manufacturers and developers improve self-driving algorithms to overcome their existing limitations, which will ultimately improve the safety and experience of driving.…”
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  19. 799

    Intelligent Infrastructure for Traffic Monitoring Based on Deep Learning and Edge Computing by Jaime Villa, Franz García, Rubén Jover, Ventura Martínez, José M. Armingol

    Published 2024-01-01
    “…This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. …”
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  20. 800

    Integrating Multi-Variable Driving Factors to Improve Land Use & Land Cover Classification Accuracy using Machine Learning Approaches: A Case Study from Lombok Island by Miftahul Irsyadi Purnama, Hüseyin Oğuz Çoban

    Published 2025-05-01
    “…The key variables that significantly influenced the land cover classification on Lombok Island include proximity to settlements, temperature, and distance to roads. These results provide essential insights for land management strategies, enabling policymakers and stakeholders to make informed decisions on sustainable development, urban planning, and environmental conservation in rapidly changing landscapes. …”
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