Výsledky vyhledávání - "gradient boosting regressor"

Akademický článek

Efficient Ensemble Learning-Based Models for Plastic Hinge Length Prediction of Reinforced Concrete Shear Walls

Autor: Naser Safaeian Hamzehkolaei, Mohammad Sadegh Barkhordari

Publikováno v: Journal of Soft Computing in Civil Engineering, Vol 8, Iss 3, Pp 135-161 (2024)

Reinforced concrete shear wall (RCSW) significantly improves the seismic performance of buildings. Accurate estimation of the plastic hinge length (PHL) of RCSWs is crucial as it significantly impacts the plastic deformation, ultimate displacement, a

Externí odkaz: https://doaj.org/article/bdac3c0073544b14a27809370aa40b42

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Akademický článek

Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments

Autor: Sophia Li, Emma Wang, Leia Pei, Sourodeep Deb, Prashanth Prabhala, Sai Hruday Reddy Nara, Raina Panda, Shiven Eltepu, Marx Akl, Larry McMahan, Edward Njoo

Publikováno v: Artificial Intelligence Chemistry, Vol 2, Iss 2, Pp 100078- (2024)

The introduction of fluorine in compounds plays a crucial role in drug development as it greatly influences their final pharmacokinetic and dynamic properties. Due to the prevalence of fluorine in FDA-approved drugs in recent years, identifying the m

Externí odkaz: https://doaj.org/article/8c8bb13b058d48de96b0962d2a1bb63a

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Akademický článek

Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning

Autor: Harsh Nagar, Rajendra Machavaram, Ambuj, Peeyush Soni, Vijay Mahore, Prakhar Patidar

Publikováno v: Cogent Engineering, Vol 11, Iss 1 (2024)

AbstractThe fuel consumption model serves as a valuable tool for estimating real-time fuel consumption levels. An accurate fuel consumption model is crucial in providing precise information regarding fuel utilisation and motivating experts to assess

Externí odkaz: https://doaj.org/article/c66a17eddf6648b2bb207b94b4ff6f02

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Akademický článek

Machine learning approach to predict the strength of concrete confined with sustainable natural FRP composites

Autor: Shabbir Ali Talpur, Phromphat Thansirichaisree, Nakhorn Poovarodom, Hisham Mohamad, Mingliang Zhou, Ali Ejaz, Qudeer Hussain, Panumas Saingam

Publikováno v: Composites Part C: Open Access, Vol 14, Iss , Pp 100466- (2024)

Recent earthquakes have highlighted the need to strengthen existing structures with substandard designs. NFRPs provide a sustainable, cost-effective alternative for strengthening, but accurately predicting their performance remains a challenge. This

Externí odkaz: https://doaj.org/article/9c455747ce894a269d36d49a32e4ecd4

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Akademický článek

Hybrid emerging model predictive data-driven forecasting of three-phase electrical signals of photovoltaic systems using GBR-LSTM

Autor: Ashkan Safari

Publikováno v: e-Prime: Advances in Electrical Engineering, Electronics and Energy, Vol 8, Iss , Pp 100549- (2024)

In numerous industrial contexts, precise analysis and forecasting of electrical signals within three-phase systems are indispensable. As a result, this work presents DeepPhase, a hybrid framework that combines Long Short-Term Memory (LSTM) neural net

Externí odkaz: https://doaj.org/article/0d25c1cc53ef416aaae636bdd344200d

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Akademický článek

Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa Sebagai Material Antikorosi

Autor: Nicholaus Verdhy Putranto, Muhamad Akrom, Gustina Alfa Trinapradika

Publikováno v: Jurnal Teknologi dan Manajemen Informatika, Vol 9, Iss 2, Pp 172-182 (2023)

Corrosion is an electrochemical process between the metal surface and a corrosive environment that can lead to significant losses in various industries, especially in the oil and gas sector. Experimental studies are conducted to evaluate the performa

Externí odkaz: https://doaj.org/article/e7d57849eeff4150ba4bde873ed08834

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Akademický článek

Machine learning methods for estimating reference evapotranspiration

Autor: AMIT BIJLWAN, SHWETA POKHRIYAL, RAJEEV RANJAN, R.K SINGH, ANKITA JHA

Publikováno v: Journal of Agrometeorology, Vol 26, Iss 1 (2024)

Precise estimation of evapotranspiration is crucial for optimizing crop water uses particularly in the context of agriculture and horticultural production. In this study, various machine learning techniques was used to determine reference evapotransp

Externí odkaz: https://doaj.org/article/dc8f6a80d56243898fa723ccabfef9d4

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Akademický článek

Using machine learning to predict gamma shielding properties: a comparative study

Autor: T A Nahool, A M Abdelmonem, M S Ali, A M Yasser

Publikováno v: New Journal of Physics, Vol 26, Iss 9, p 093035 (2024)

This study employed machine learning (ML) algorithms to predict the linear attenuation coefficients (LACs) of materials in inorganic scintillation detectors, which are crucial for evaluating self-shielding properties. Predictions from various ML mode

Externí odkaz: https://doaj.org/article/5d805dd8d5814057b5c107439fa33507

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Akademický článek

Forecasting transport flows using big data and machine learning technology

Autor: Tashmetov Komoliddin, Rasulmukhamedov Mukhamadaziz, Tashmetov Timur

Publikováno v: E3S Web of Conferences, Vol 531, p 02012 (2024)

Efficient management of urban transportation is crucial for addressing the growing challenges posed by increasing traffic and population. In this context, the utilization of big data and intelligent systems has become paramount. This paper introduces

Externí odkaz: https://doaj.org/article/f2e7fe7f873345509baff40398cdbdbb

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Akademický článek

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