Unveiling Technological Innovation in Construction Waste Recycling: Insights from Text Mining DOI Creative Commons
Mengqi Yuan, Sijin Chen,

Mai Liu

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(9), P. 1544 - 1544

Published: May 3, 2025

Dealing with solid waste has always been a global concern, and construction is one of the most important parts. Addressing how to properly dispose waste, reduce its negative environmental impact, achieve effective resource recycling emerged as an urgent problem be solved. Technological innovation underpins efficient reduction, reuse, recycling, but existing research often overlooks systematic quantitative measurements initiatives. This study uncovers development status trends (CWR) technology, identifies key points potential directions for technological development, also explores practical strategies promote industrial growth. Through patent analysis, this current within China’s CWR industry. A text mining approach employed analyze texts related core technologies, explore topic contents, identify intensities evolution trends. comparative analysis between China dominant countries in reveals strengths weaknesses. The results indicate that applications industry are substantial, rapid growth rate, while competitiveness remains weak. applicants widely distributed, traditional enterprises demonstrating strong capabilities, emerging small-to-medium lack vitality. advantages developing devices wastewater treatment foundation some other technologies offers overview initiatives industry, representing breakthrough research. findings will assist policymakers formulating evidence-driven CWR.

Language: Английский

Topic research in fuzzy domain: Based on LDA topic modelling DOI
Dejian Yu,

Anran Fang,

Zeshui Xu

et al.

Information Sciences, Journal Year: 2023, Volume and Issue: 648, P. 119600 - 119600

Published: Aug. 30, 2023

Language: Английский

Citations

31
Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling DOI
Yu-Cheng Wang, Toly Chen

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121369 - 121369

Published: Aug. 30, 2023

Language: Английский

Citations

25
Application of text mining and coupling theory to depth cognition of aviation safety risk DOI

Minglan Xiong,

Huawei Wang,

Changchang Che

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 245, P. 110032 - 110032

Published: Feb. 20, 2024

Language: Английский

Citations

14
Active learning inspired method in generative models DOI
Guipeng Lan, Shuai Xiao, Jiachen Yang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 249, P. 123582 - 123582

Published: March 11, 2024

Language: Английский

Citations

11
A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets DOI
Abhinav Sharma, Sanjay Dhanka, Ankur Kumar

et al.

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(3), P. 035209 - 035209

Published: June 28, 2024

Abstract Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within heart, leading irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is significant cause morbidity mortality global scale. About 80% deaths related disease are caused by ventricular arrhythmias. research investigated application an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated model’s performance dataset from UCI ML repository varying train-test splits (70:30, 80:20, 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, directory analysis were applied along Pearson correlation feature selection, all aimed enhancing model performance. proposed RF achieved impressive metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), specificity (100%). Furthermore, study compared approach existing models, demonstrating improvements across various measures.

Language: Английский

Citations

9
Food Recommendation Towards Personalized Wellbeing DOI

Guanhua Qiao,

Dachuan Zhang, Nana Zhang

et al.

Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104877 - 104877

Published: Jan. 1, 2025

Language: Английский

Citations

1
Artificial Intelligence for Enhancing Special Education for K-12: A Decade of Trends, Themes, and Global Insights (2013–2023) DOI
Yuqin Yang,

L Chen,

Wenmeng He

et al.

International Journal of Artificial Intelligence in Education, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 19, 2024

Language: Английский

Citations

8
Developing a supervised learning model for anticipating potential technology convergence between technology topics DOI
Wonchul Seo,

Mokh Afifuddin

Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 203, P. 123352 - 123352

Published: April 1, 2024

Language: Английский

Citations

7
Investigating the optimal number of topics by advanced text-mining techniques: Sustainable energy research DOI Creative Commons

Amer Farea,

Shailesh Tripathi, Galina Glazko

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 136, P. 108877 - 108877

Published: July 8, 2024

In recent years, there has been a growing interest in analyzing text data from different scientific fields. The significant advancement of Artificial Intelligence Natural Language Processing enables systematic categorization the wealth papers into fundamental thematic clusters. this context, topic modeling is playing crucial role. Unfortunately, comparative analysis between traditional and advanced methods, including well-established techniques like Latent Dirichlet Allocation (LDA) newer approaches BERTopic, remains significantly underexplored. This study addresses gap by conducting comprehensive extensive focused on sustainable energy research. To achieve this, we compile unique dataset consisting thousands abstracts sourced PubMed, Scopus, Web Science. Our involves comparison LDA transformer model BERTopic. Importantly, introduce novel approach to determine optimal number topics, achieved through maximization combined semantic scores, show that topics considerably lower than previous approaches. Overall, our not only contributes methodologically but also enhances understanding principal

Language: Английский

Citations

7
Patterns in the Growth and Thematic Evolution of Artificial Intelligence Research: A Study Using Bradford Distribution of Productivity and Path Analysis DOI Open Access
Solanki Gupta, Anurag Kanaujia, Hiran H. Lathabai

et al.

International Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 26

Published: March 14, 2024

Artificial intelligence (AI) has emerged as a transformative technology with applications across multiple domains. The corpus of work related to the field AI grown significantly in volume well terms application wider However, given wide diverse areas, measurement and characterization span research is often challenging task. Bibliometrics well-established method scientific community measure patterns impact research. It however also received significant criticism for its overemphasis on macroscopic picture inability provide deep understanding growth thematic structure knowledge-creation activities. Therefore, this study presents framework comprising two techniques, namely, Bradford’s distribution path analysis characterize evolution discipline. While Bradford provides view artificial growth, microscopic evolutionary trajectories, thereby completing analytical framework. Detailed insights into each subdomain are drawn, major techniques employed various identified, some relevant implications discussed demonstrate usefulness analyses.

Language: Английский

Citations

5