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Application of Artificial Intelligence and Semantic Mining Technology
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Application of Artificial Intelligence and Semantic Mining Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 3971

Special Issue Editor

Dr. Haitao Zheng

E-Mail Website
Guest Editor
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: artificial intelligence and semantic mining

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and semantic mining technology have revolutionized numerous fields, offering unprecedented opportunities for innovation and advancement. This Special Issue is a comprehensive exploration of the extensive applications of AI and semantic mining technology across diverse domains, spanning engineering, physics, and multidisciplinary spheres. With AI's exponential growth and semantic mining's refined methodologies, the potential for transformative impacts has never been greater. This issue seeks to elucidate the multifaceted landscape of AI and semantic mining applications, showcasing innovative research, methodologies, and practical implementations.

AI and semantic mining techniques are reshaping traditional practices and enabling groundbreaking advancements in engineering. From predictive maintenance in industrial machinery to intelligent transportation systems optimizing traffic flow, integrating AI and semantic mining enhances efficiency, reliability, and safety across various engineering domains. Moreover, in the burgeoning field of renewable energy, AI-driven optimization algorithms facilitate efficient resource management, driving sustainable development and mitigating environmental impacts.

In physics, AI and semantic mining technologies are revolutionizing research methodologies, enabling data-driven discoveries and accelerating scientific breakthroughs. Machine learning algorithms empower physicists to analyze vast datasets, uncover hidden patterns, and simulate complex phenomena with unprecedented accuracy. Furthermore, semantic mining techniques facilitate knowledge discovery from large-scale scientific literature, expedite the dissemination of research findings, and foster interdisciplinary collaborations.

Beyond traditional disciplines, the multidisciplinary nature of AI and semantic mining fosters innovation at the intersection of diverse fields. In healthcare, AI-powered diagnostic systems are revolutionizing medical imaging, enabling early detection of diseases and personalized treatment plans. Semantic mining of electronic health records revolutionizes clinical research, uncovering novel insights into disease mechanisms and treatment outcomes. Similarly, AI-driven algorithms are revolutionizing investment strategies in finance, enabling real-time risk assessment and informed decision-making in volatile markets.

As the technological landscape continues to evolve, integrating AI and semantic mining technology holds immense promise for addressing global challenges and driving societal progress. This Special Issue aims to catalyze innovation, foster interdisciplinary dialogue, and pave the way for a future powered by intelligent technologies by harnessing the collective expertise and collaboration of researchers and practitioners across disciplines.

Dr. Haitao Zheng
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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.

Keywords

  • artificial intelligence
  • semantic mining technology
  • engineering applications
  • physics applications
  • multidisciplinary
  • predictive maintenance
  • renewable energy
  • machine learning
  • scientific discovery
  • healthcare
  • finance
  • interdisciplinary collaboration
  • innovation

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Published Papers (4 papers)

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Research

28 pages, 1250 KiB  
Article
A Variational-Mode-Decomposition-Cascaded Long Short-Term Memory with Attention Model for VIX Prediction
by Do-Hyeon Kim, Dong-Jun Kim and Sun-Yong Choi
Appl. Sci. 2025, 15(10), 5630; https://doi.org/10.3390/app15105630 - 18 May 2025
Viewed by 245
Abstract
Financial time-series forecasting presents a significant challenge due to the inherent volatility and complex patterns in market data. This study introduces a novel forecasting framework that integrates Variational Mode Decomposition (VMD) with a Cascaded Long Short-Term Memory (LSTM) network enhanced by an Attention [...] Read more.
Financial time-series forecasting presents a significant challenge due to the inherent volatility and complex patterns in market data. This study introduces a novel forecasting framework that integrates Variational Mode Decomposition (VMD) with a Cascaded Long Short-Term Memory (LSTM) network enhanced by an Attention mechanism. The primary objective is to enhance the predictive accuracy of the VIX, a key measure of market uncertainty, through advanced signal processing and deep learning techniques. VMD is employed as a preprocessing step to decompose financial time-series data into multiple Intrinsic Mode Functions (IMFs), effectively isolating short-term fluctuations from long-term trends. These decomposed features serve as inputs to a Cascaded LSTM model with an Attention mechanism, which enables the model to capture critical temporal dependencies, thereby improving forecasting performance. Experimental evaluations using VIX and S&P 500 data from January 2020 to December 2024 demonstrate the superior predictive capability of the proposed model compared to seven benchmark models. The results highlight the effectiveness of combining signal decomposition techniques with Attention-based deep learning architectures for financial market forecasting. This research contributes to the field by introducing a hybrid model that improves predictive accuracy, enhances robustness against market fluctuations, and underscores the importance of Attention mechanisms in capturing essential temporal dynamics. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Semantic Mining Technology)
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20 pages, 525 KiB  
Article
Representing Aspectual Meaning in Sentence: Computational Modeling Based on Chinese
by Hongchao Liu and Bin Liu
Appl. Sci. 2025, 15(7), 3720; https://doi.org/10.3390/app15073720 - 28 Mar 2025
Viewed by 297
Abstract
Situation types can be viewed as the foundation of representation of sentence meaning. Noting that situation types cannot be determined by verbs alone, recent studies often focus on situation type prediction in terms of the combination of different linguistic constituents at the sentence [...] Read more.
Situation types can be viewed as the foundation of representation of sentence meaning. Noting that situation types cannot be determined by verbs alone, recent studies often focus on situation type prediction in terms of the combination of different linguistic constituents at the sentence level instead of lexically marked situation types. However, in languages with a fully marked aspectual system, such as Mandarin Chinese, such an approach may miss the opportunity of leveraging lexical aspects as well as other distribution-based lexical cues of event types. Currently, there is a lack of resources and methods for the identification and validation of the lexical aspect, and this issue is particularly severe for Chinese. From a computational linguistics perspective, the main reason for this shortage stems from the absence of a verified lexical aspect classification system, and consequently, a gold-standard dataset annotated according to this classification system. Additionally, owing to the lack of such a high-quality dataset, it remains unclear whether semantic models, including large general-purpose language models, can actually capture this important yet complex semantic information. As a result, the true realization of lexical aspect analysis cannot be achieved. To address these two problems, this paper sets out two objectives. First, we aim to construct a high-quality lexical aspect dataset. Since the classification of the lexical aspect depends on how it interacts with aspectual markers, we establish a scientific classification and data construction process through the selection of vocabulary items, the compilation of co-occurrence frequency matrices, and hierarchical clustering. Second, based on the constructed dataset, we separately evaluate the ability of linguistic features and large language model word embeddings to identify lexical aspect categories in order to (1) verify the capacity of semantic models to infer complex semantics and (2) achieve high-accuracy prediction of lexical aspects. Our final classification accuracy is 72.05%, representing the best result reported thus far. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Semantic Mining Technology)
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20 pages, 4246 KiB  
Article
High-Reputation Food Formulas: A Heterogeneous Information Network Representation and Semantic Analysis Approach
by Hongfei Cui, Rui Yan, Qi Cao and Jingyu Zhang
Appl. Sci. 2025, 15(5), 2375; https://doi.org/10.3390/app15052375 - 23 Feb 2025
Viewed by 657
Abstract
In food research and development, fully capturing consumer preferences is crucial for enhancing ingredient selection and formulation. However, many decisions still rely heavily on expert judgment. To address this, we propose a semantic analysis framework that combines heterogeneous information network representation learning and [...] Read more.
In food research and development, fully capturing consumer preferences is crucial for enhancing ingredient selection and formulation. However, many decisions still rely heavily on expert judgment. To address this, we propose a semantic analysis framework that combines heterogeneous information network representation learning and lexicon-based sentiment analysis to uncover relationships between ingredients and consumer word-of-mouth. We apply our approach to 1062 breakfast products and 58,206 reviews from Amazon, extracting taste- and ingredient-related sentiment while excluding non-formulation factors (e.g., price, packaging, logistics). Through embedding-based visualization, our product space groups items according to similar formulation characteristics, while the ingredient space reveals functional clusters among ingredients. We find that high-reputation products and their related ingredients form distinctive clusters in both spaces, often emphasizing natural, organic, and gluten-free attributes, whereas items containing synthetic additives appear more frequently in low-reputation regions. This framework offers a data-driven complement to traditional R&D methods, providing a perspective distinct from conventional approaches for product formulation and ingredient selection. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Semantic Mining Technology)
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28 pages, 2499 KiB  
Article
Optimizing Aspect-Based Sentiment Analysis Using BERT for Comprehensive Analysis of Indonesian Student Feedback
by Ahmad Jazuli, Widowati and Retno Kusumaningrum
Appl. Sci. 2025, 15(1), 172; https://doi.org/10.3390/app15010172 - 28 Dec 2024
Cited by 1 | Viewed by 2082
Abstract
Evaluating the learning process requires a platform for students to express feedback and suggestions openly through online reviews. Sentiment analysis is often used to analyze review texts but typically captures only overall sentiment without identifying specific aspects. This study develops an aspect-based sentiment [...] Read more.
Evaluating the learning process requires a platform for students to express feedback and suggestions openly through online reviews. Sentiment analysis is often used to analyze review texts but typically captures only overall sentiment without identifying specific aspects. This study develops an aspect-based sentiment analysis (ABSA) model using IndoBERT, a pre-trained model tailored for the Indonesian language. The research uses 10,000 student reviews from Indonesian universities, processed through data labeling, text preprocessing, and splitting, followed by model training and performance evaluation. The model demonstrated superior performance with an aspect extraction accuracy of 0.973, an F1-score of 0.952, a sentiment classification accuracy of 0.979, and an F1-score of 0.974. Experimental results indicate that the proposed ABSA model surpasses previous state-of-the-art models in analyzing sentiment related to specific aspects of educational evaluation. By leveraging IndoBERT, the model effectively handles linguistic complexities and provides detailed insights into student experiences. These findings highlight the potential of the ABSA model in enhancing learning evaluations by offering precise, aspect-focused feedback, contributing to strategies for improving the quality of higher education. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Semantic Mining Technology)
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