Human motion pattern recognition based on the fused random forest algorithm

Cai, Chuang; Yang, Chunxi; Lu, Sheng; Gao, Guanbin; Na, Jing

doi:10.1016/j.measurement.2023.113540

Cited by 8 publications

(1 citation statement)

References 30 publications

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“…For that reason, the random forest method, belonging to the group of embedded feature selection methods, has been gaining increasing interest in different scientific areas. The main advantages of the random forest method underlined in current state-of-the-art scientific publications include the following [68][69][70][71][72]:…”

Section: Ai-driven Algorithms In Real Estate Industry Solution Analysismentioning

confidence: 99%

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

Walacik,

Chmielewska

2024

Sustainability

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As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.

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