Advanced Applications of Text Mining and Cloud Computing

Dear Colleagues,

In the modern data-driven era, unstructured text data, such as those from scientific literature, patent documents, and policy reports, are growing exponentially. Recently, breakthroughs in large language models (LLMs) and advanced machine learning (ML) technologies have brought a paradigm shift in the semantic understanding, knowledge extraction, and generation of unstructured data. Traditional data processing methods are becoming increasingly ineffective in handling this massive volume of information. NLP techniques empowered by LLMs provide robust tools for precisely capturing technological intelligence and hidden relationships from complex texts. Simultaneously, cloud computing architectures offer highly flexible, scalable, and high-performance computing resources, serving as critical infrastructure for training and deploying complex ML models and processing large-scale datasets.

This Special Issue aims to explore the deep integration of LLMs, machine learning, and cloud computing technologies, focusing on their innovative applications across a variety of scientific disciplines and in technology policy research. We seek to bring together the latest methodologies, empirical studies, and theoretical frameworks to demonstrate how these advanced AI technologies can assist researchers, policymakers, and industry analysts in accurately forecasting technology trends, mapping knowledge networks, and facilitating evidence-based policymaking and innovation management.

Scope and Topics

This Special Issue welcomes original research articles and comprehensive review papers. Topics of interest include, but are not limited to, the following:

1. Applications of LLMs in technology trend forecasting and intelligence analysis: utilizing generative AI and LLMs for automated prior art search, technology management and industrial scientific applications.
2. Machine learning-driven advanced bibliometrics and knowledge network mapping: applying deep learning, graph neural networks (GNN), and classification/clustering algorithms for large-scale citation analysis, topic evolution tracking, emerging technology forecasting, and the construction of cross-disciplinary knowledge networks.
3. AI-assisted science and technology policy research and sustainability assessment: utilizing text mining and LLMs to analyze policy documents, news reports, and R&D publications to support technology policy evaluation, national innovation system analysis, and trend forecasting in green technologies and Sustainable Development Goals (SDGs).
4. Cloud computing and the deployment of large-scale ML models: exploring innovative practices for optimizing and deploying scalable machine learning models, distributed computing architectures, and high-performance data pipelines on cloud platforms tailored for patent and bibliometric data analysis.
5. Data-driven decision making in innovation management: exploring how enterprises or research institutions leverage cloud services, ML predictive models, and LLM tools to optimize patent portfolio management, technology licensing strategies, R&D resource allocation, and strategic decision-making.

Dr. Fan Chin-Yuan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• text mining
• cloud computing
• policy research
• large language models
• machine learning