Výsledky vyhledávání - "Machine Learning approach"

Akademický článek

The impacts of income inequality, forest area, and technology innovations on ecological footprint in Indonesia: ARDL and ML approach

Autor: Vikniswari Vija Kumaran, Abdul Rahim Ridzuan, Abdelhak Senadjki, Abdulkarim M. Jamal Kanaan, Miguel Angel Esquivias

Publikováno v: Discover Sustainability, Vol 5, Iss 1, Pp 1-21 (2024)

Abstract Reducing humans’ ecological footprint (ECF) is critical for guaranteeing a sustainable future and sustaining the planet's health for future generations. Consequently, sustainability policies and development aim to minimize ECF and guarante

Externí odkaz: https://doaj.org/article/97ad06d389e845dd991b8d7d7084aee5

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Akademický článek

Mapping rapeseed (Brassica napus L.) aboveground biomass in different periods using optical and phenotypic metrics derived from UAV hyperspectral and RGB imagery

Autor: Chuanliang Sun, Weixin Zhang, Genping Zhao, Qian Wu, Wanjie Liang, Ni Ren, Hongxin Cao, Lidong Zou

Publikováno v: Frontiers in Plant Science, Vol 15 (2024)

Aboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in mo

Externí odkaz: https://doaj.org/article/c36598d7069b4a759b18d76df7e24085

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Akademický článek

OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions

Autor: Xin Lai, Min Liu, Yuqian Liu, Xiaoyan Zhu, Jiayin Wang

Publikováno v: Frontiers in Genetics, Vol 15 (2024)

Open chromatin regions (OCRs) play a crucial role in transcriptional regulation and gene expression. In recent years, there has been a growing interest in using plasma cell-free DNA (cfDNA) sequencing data to detect OCRs. By analyzing the characteris

Externí odkaz: https://doaj.org/article/d151a02571fb4a418fdab822ccead36b

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Akademický článek

Optimizing Business Intelligence System Using Big Data and Machine Learning

Autor: Gabriel Gregory James, Oise G P, Chukwu E G, Michael N A, Ekpo W F, Okafor P E

Publikováno v: Journal of Information Systems and Informatics, Vol 6, Iss 2, Pp 1215-1236 (2024)

The Business Intelligence (BI) and Data Warehouse (DW) system deployed in the Nigerian National Petroleum Corporation should provide cooperate decision makers with real-time information to help them identify and understand key business factors to mak

Externí odkaz: https://doaj.org/article/279b2d007145449a85ffba947915c799

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Akademický článek

Comparison of machine learning and deep learning models for the assessment of rondo wine grape quality with a hyperspectral camera

Autor: Khin Nilar Swe, Noboru Noguchi

Publikováno v: Smart Agricultural Technology, Vol 8, Iss , Pp 100474- (2024)

Hyperspectral images provide rich spectral/spatial data and have shown remarkable performance in precision viticulture. Standardized data-processing methods are necessary to reduce the dimensionality and to identify powerful wavelengths. Toward this

Externí odkaz: https://doaj.org/article/afd608646eb04568ac8bd253c220c766

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Akademický článek

The factors associated with the modified Fisher grade in patients with aneurysmal subarachnoid hemorrhage

Autor: Di Zhao, Yating Li, Jianzhong Cui

Publikováno v: Frontiers in Physiology, Vol 15 (2024)

BackgroundAneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening medical condition with a high fatality and morbidity rate. There was a substantial link between the modified Fisher grade of aSAH and the neurological function deficit. This st

Externí odkaz: https://doaj.org/article/982ae0d5d94f40c4bed4591e63cd826f

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Akademický článek

A data-driven model for damage evolution of bridge stay cable

Autor: Guowen Yao, Qianling Wang, Fengmin Chen, Li Ying, Xuanbo He, Shengbao Zhen, Xuanrui Yu

Publikováno v: Case Studies in Construction Materials, Vol 20, Iss , Pp e03209- (2024)

Chloride ion concentration is crucial for the reliability of stay cables, particularly when their protective covers are compromised. Previous studies have focused on the corrosion of single wires or cables over time, proposing estimation models that

Externí odkaz: https://doaj.org/article/75e76fa8f02b4d16b1534575b85bb178

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Akademický článek

Machine‐Learning Based Identification of the Critical Driving Factors Controlling Storm‐Time Outer Radiation Belt Electron Flux Dropouts

Autor: Man Hua, Jacob Bortnik, Donglai Ma

Publikováno v: Geophysical Research Letters, Vol 51, Iss 10, Pp n/a-n/a (2024)

Abstract Understanding and forecasting outer radiation belt electron flux dropouts is one of the top concerns in space physics. By constructing Support Vector Machine (SVM) models to predict storm‐time dropouts for both relativistic and ultra‐rel

Externí odkaz: https://doaj.org/article/d0a47a6a9f734136961b8983bc03004f

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Akademický článek

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Autor: Nagesh Tambake, Bhagyesh Deshmukh, Sujit Pardeshi, Haitham A. Mahmoud, Robert Cep, Sachin Salunkhe, Emad Abouel Nasr

Publikováno v: Frontiers in Materials, Vol 11 (2024)

Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize dow

Externí odkaz: https://doaj.org/article/cf82f08ee4664b13a993f4150a3ef4fb

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