Search Results - subject:"Ensemble machine learning"

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

Deb, Jayanta Bhusan; Chowdhury, Shilpa; Ali, Nur Mohammad - 2024

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D … confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor … a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset …

Persistent link: https://www.econbiz.de/10015414408

An explainable AI-enabled granular ensemble machine learning framework to demystify fertilizer price movements

Jana, Rabin K.; Ghosh, Indranil; Kumar, P. N. Ram - In: Journal of the Operational Research Society 75 (2024) 8, pp. 1569-1586

Persistent link: https://www.econbiz.de/10015052581

DAViS: A unified solution for data collection, analyzation, and visualization in real-time stock market prediction

Lim, Sunghoon - In: Financial Innovation 7 (2021) 1, pp. 1-32

The explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous...

Persistent link: https://www.econbiz.de/10012602937

DAViS : a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

Suppawong Tuarob; Poom Wettayakorn; Ponpat Phetchai; … - In: Financial innovation : FIN 7 (2021), pp. 1-32

Persistent link: https://www.econbiz.de/10012594948

An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features

Ghosh, Indranil; Jana, Rabin K.; Abedin, Mohammad Zoynul - In: International journal of contemporary hospitality management 35 (2023) 10, pp. 3592-3611

Persistent link: https://www.econbiz.de/10014334333

Predicting stock splits using ensemble machine learning and SMOTE oversampling

Li, Ang; Liu, Mark H.; Sheather, Simon J. - In: Pacific-Basin finance journal 78 (2023), pp. 1-24

Persistent link: https://www.econbiz.de/10014463767

Prediction and interpretation of daily NFT and DeFi prices dynamics : inspection through ensemble machine learning & XAI

Ghosh, Indranil; Alfaro-Cortés, Esteban; Gámez, Matías; … - In: International review of financial analysis 87 (2023), pp. 1-21

Persistent link: https://www.econbiz.de/10014456330

Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008-2009

Ojha, Vikram; Lee, JeongHoe - In: Digital finance : smart data analytics, investment … 3 (2021) 3/4, pp. 249-271

Persistent link: https://www.econbiz.de/10012697966

Enhanced predictive models for construction costs : a case study of Turkish mass housing sector

Ugur, Latif Onur; Kanit, Recep; Erdal, Hamit; Namli, Ersin - In: Computational economics 53 (2019) 4, pp. 1403-1419

Persistent link: https://www.econbiz.de/10012135256

In Silico Strategy for Diagnosis of Chronic Kidney Disease

Basant, Nikita; Gupta, Shikha - In: International Journal of Quantitative … 2 (2017) 1, pp. 80-94

Chronic kidney disease (CKD) is the third deadliest reason for mortality worldwide. An early detection of CKD would help to decelerate the loss of kidney function. Computational approaches provide opportunities to screen large populations for diagnosis of CKD. In this study, qualitative and...

Persistent link: https://www.econbiz.de/10012047201