Information

24 pages, 967 KiB

Open AccessArticle

Enhancing E-Recruitment Recommendations Through Text Summarization Techniques

by Reham Hesham El-Deeb, Walid Abdelmoez and Nashwa El-Bendary

Information 2025, 16(4), 333; https://doi.org/10.3390/info16040333 - 21 Apr 2025

Viewed by 244

This research aims to enhance e-recruitment systems using text summarization techniques and pretrained large language models (LLMs). A job recommender system is built with integrated text summarization. The text summarization techniques that are selected are BART, T5 (Text-to-Text Transfer Transformer), BERT, and Pegasus. [...] Read more.

This research aims to enhance e-recruitment systems using text summarization techniques and pretrained large language models (LLMs). A job recommender system is built with integrated text summarization. The text summarization techniques that are selected are BART, T5 (Text-to-Text Transfer Transformer), BERT, and Pegasus. Content-based recommendation is the model chosen to be implemented. The LinkedIn Job Postings dataset is used. The evaluation of the text summarization techniques is performed using ROUGE-1, ROUGE-2, and ROUGE-L. The results of this approach deduce that the recommendation does improve after text summarization. BERT outperforms other summarization techniques. Recommendation evaluations show that, for MRR, BERT performs 256.44% better, indicating relevant recommendations at the top more effectively. For RMSE, there is a 29.29% boost, indicating recommendations closer to the actual values. For MAP, a 106.46% enhancement is achieved, presenting the highest precision in recommendations. Lastly, for NDCG, there is an 83.94% increase, signifying that the most relevant recommendations are ranked higher. Full article

(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)

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Graphical abstract

20 pages, 14434 KiB

Open AccessArticle

Optimized Marine Target Detection in Remote Sensing Images with Attention Mechanism and Multi-Scale Feature Fusion

by Xiantao Jiang, Tianyi Liu, Tian Song and Qi Cen

Information 2025, 16(4), 332; https://doi.org/10.3390/info16040332 - 21 Apr 2025

Viewed by 132

Abstract

With the continuous growth of maritime activities and the shipping trade, the application of maritime target detection in remote sensing images has become increasingly important. However, existing detection methods face numerous challenges, such as small target localization, recognition of targets with large aspect [...] Read more.

With the continuous growth of maritime activities and the shipping trade, the application of maritime target detection in remote sensing images has become increasingly important. However, existing detection methods face numerous challenges, such as small target localization, recognition of targets with large aspect ratios, and high computational demands. In this paper, we propose an improved target detection model, named YOLOv5-ASC, to address the challenges in maritime target detection. The proposed YOLOv5-ASC integrates three core components: an Attention-based Receptive Field Enhancement Module (ARFEM), an optimized SIoU loss function, and a Deformable Convolution Module (C3DCN). These components work together to enhance the model’s performance in detecting complex maritime targets by improving its ability to capture multi-scale features, optimize the localization process, and adapt to the large aspect ratios typical of maritime objects. Experimental results show that, compared to the original YOLOv5 model, YOLOv5-ASC achieves a 4.36 percentage point increase in mAP@0.5 and a 9.87 percentage point improvement in precision, while maintaining computational complexity within a reasonable range. The proposed method not only achieves significant performance improvements on the ShipRSImageNet dataset but also demonstrates strong potential for application in complex maritime remote sensing scenarios. Full article

(This article belongs to the Special Issue Computer Vision for Security Applications)

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15 pages, 5939 KiB

Open AccessArticle

Center-Guided Network with Dynamic Attention for Transmission Tower Detection

by Xiaobin Li, Zhuwei Liang, Jingbin Yang, Chuanlong Lyu and Yuge Xu

Information 2025, 16(4), 331; https://doi.org/10.3390/info16040331 - 21 Apr 2025

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Abstract

Transmission tower detection in aerial images is the critical step for the inspection of power transmission equipment, which is essential for the stable operation of the power system. However, transmission towers in aerial images pose numerous challenges for object detection due to their [...] Read more.

Transmission tower detection in aerial images is the critical step for the inspection of power transmission equipment, which is essential for the stable operation of the power system. However, transmission towers in aerial images pose numerous challenges for object detection due to their multi-scale elongated shapes, large aspect ratios, and visually similar backgrounds. To address these problems, we propose the Center-Guided network with Dynamic Attention (CGDA) for detecting TTs from aerial images. Specifically, we apply ResNet and FPN as the feature extractor to extract high-quality and multi-scale features. To obtain more discriminative information, the dynamic attention mechanism is employed to dynamically fuse multi-scale feature maps and place more attention on the object regions. In addition, a two-stage detection head is proposed to employ a two-stage detection process to perform more accurate detection. Extensive experiments are conducted on a subset of the public TTPLA dataset. The results show that CGDA achieves competitive performance in detecting TTs, demonstrating the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)

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32 pages, 4686 KiB

Open AccessArticle

Evaluating the Impact of Synthetic Data on Emotion Classification: A Linguistic and Structural Analysis

by István Üveges and Orsolya Ring

Information 2025, 16(4), 330; https://doi.org/10.3390/info16040330 - 21 Apr 2025

Viewed by 232

Abstract

Emotion classification in natural language processing (NLP) has recently witnessed significant advancements. However, class imbalance in emotion datasets remains a critical challenge, as dominant emotion categories tend to overshadow less frequent ones, leading to biased model predictions. Traditional techniques, such as undersampling and [...] Read more.

Emotion classification in natural language processing (NLP) has recently witnessed significant advancements. However, class imbalance in emotion datasets remains a critical challenge, as dominant emotion categories tend to overshadow less frequent ones, leading to biased model predictions. Traditional techniques, such as undersampling and oversampling, offer partial solutions. More recently, synthetic data generation using large language models (LLMs) has emerged as a promising strategy for augmenting minority classes and improving model robustness. In this study, we investigate the impact of synthetic data augmentation on German-language emotion classification. Using an imbalanced dataset, we systematically evaluate multiple balancing strategies, including undersampling overrepresented classes and generating synthetic data for underrepresented emotions using a GPT-4–based model in a few-shot prompting setting. Beyond enhancing model performance, we conduct a detailed linguistic analysis of the synthetic samples, examining their lexical diversity, syntactic structures, and semantic coherence to determine their contribution to overall model generalization. Our results demonstrate that integrating synthetic data significantly improves classification performance, particularly for minority emotion categories, while maintaining overall model stability. However, our linguistic evaluation reveals that synthetic examples exhibit reduced lexical diversity and simplified syntactic structures, which may introduce limitations in certain real-world applications. These findings highlight both the potential and the challenges of synthetic data augmentation in emotion classification. By providing a comprehensive evaluation of balancing techniques and the linguistic properties of generated text, this study contributes to the ongoing discourse on improving NLP models for underrepresented linguistic phenomena. Full article

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16 pages, 4154 KiB

Open AccessArticle

Synthetic User Generation in Games: Cloning Player Behavior with Transformer Models

by Alfredo Chapa Mata, Hisa Nimi and Juan Carlos Chacón

Information 2025, 16(4), 329; https://doi.org/10.3390/info16040329 - 21 Apr 2025

Viewed by 253

Abstract

User-centered design (UCD) commonly requires direct player participation, yet budget limitations or restricted access to users can impede this goal. To address these challenges, this research explores a transformer-based approach coupled with a diffusion process to replicate real player behavior in a 2D [...] Read more.

User-centered design (UCD) commonly requires direct player participation, yet budget limitations or restricted access to users can impede this goal. To address these challenges, this research explores a transformer-based approach coupled with a diffusion process to replicate real player behavior in a 2D side-scrolling action–adventure environment that emphasizes exploration. By collecting an extensive set of gameplay data from real participants in an open-source game, “A Robot Named Fight!”, this study gathered comprehensive state and input information for training. A transformer model was then adapted to generate button-press sequences from encoded game states, while the diffusion mechanism iteratively introduced and removed noise to refine its predictions. The results indicate a high degree of replication of the participant’s actions in contexts similar to the training data, as well as reasonable adaptation to previously unseen scenarios. Observational analysis further confirmed that the model mirrored essential aspects of the user’s style, including navigation strategies, the avoidance of unnecessary combat, and selective obstacle clearance. Despite hardware constraints and reliance on a single observer’s feedback, these findings suggest that a transformer–diffusion methodology can robustly approximate user behavior. This approach holds promise not only for automated playtesting and level design assistance in similar action–adventure games but also for broader domains where simulating user interaction can streamline iterative design and enhance player-centric outcomes. Full article

(This article belongs to the Special Issue Next-Generation Applications and Implementations of Gamification Systems)

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24 pages, 2533 KiB

Open AccessArticle

Research on Price Prediction of Stock Price Index Based on Combination Method with Introduction of Options Market Information

by Yi Hu, Xin Sui, Qi Zhang and Wei Zhang

Information 2025, 16(4), 328; https://doi.org/10.3390/info16040328 - 21 Apr 2025

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Abstract

This study establishes a combination method-based prediction model for the CSI 300 stock index price embedded with options market information. Firstly, utilizing options and spot market information, a BP neural network is employed to predict the CSI 300 stock index price. Secondly, a [...] Read more.

This study establishes a combination method-based prediction model for the CSI 300 stock index price embedded with options market information. Firstly, utilizing options and spot market information, a BP neural network is employed to predict the CSI 300 stock index price. Secondly, a logical framework based on a combination method is constructed to further optimize the CSI 300 stock index price prediction through decomposition–clustering, error adjustment, and weighted integration approaches. The results demonstrate the following: (1) Compared to price predictions based solely on spot market information, the introduction of options market information significantly enhances the forecasting performance for the CSI 300 index price. (2) From the perspective of options moneyness classification, after incorporating options information, different types of options contracts exhibit varying impacts on the CSI 300 index price prediction. Prior to optimization, predictions incorporating in-the-money call options with maximum trading volume yield the optimal performance based on the MSE metric. (3) Under the logical framework of the combination method, the prediction effect for the CSI 300 stock index price is gradually improved after introducing the decomposition–clustering method, the error adjustment method, and the price-weighted integration method, which shows that it is appropriate to use the combination method to optimize the price prediction. Overall, this study proposes a combination method for price forecasting incorporating options market information across diverse contract types. It allows for weighted integration of prediction results derived from various options information, offering a novel research angle for spot market price prediction. The study also underscores the importance of implicit information mining and multi-market information fusion for price prediction, which is expected to become a key research focus in this field. Full article

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26 pages, 8945 KiB

Open AccessArticle

Benchmarking Methods for Pointwise Reliability

by Cláudio Correia, Simão Paredes, Teresa Rocha, Jorge Henriques and Jorge Bernardino

Information 2025, 16(4), 327; https://doi.org/10.3390/info16040327 - 20 Apr 2025

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Abstract

The growing interest in machine learning in a critical domain like healthcare emphasizes the need for reliable predictions, as decisions based on these outputs can have significant consequences. This study benchmarks methods for assessing pointwise reliability, focusing on data-driven techniques based on the [...] Read more.

The growing interest in machine learning in a critical domain like healthcare emphasizes the need for reliable predictions, as decisions based on these outputs can have significant consequences. This study benchmarks methods for assessing pointwise reliability, focusing on data-driven techniques based on the density principle and the local fit principle. These methods evaluate the reliability of individual predictions by analyzing their similarity to training data and evaluating the performance of the model in local regions. Aiming to establish a standardized comparison, the study introduces a benchmark framework that combines error rate evaluations across reliability intervals with t-distributed Stochastic Neighbor Embedding visualizations to further validate the results. The results demonstrate that methods combining density and local fit principles generally outperform those relying on a single principle, achieving lower error rates for high-reliability predictions. Furthermore, the study identifies challenges such as the adjustment of method parameters and clustering limitations and provides insight into their impact on reliability assessments. Full article

(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)

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31 pages, 2141 KiB

Open AccessSystematic Review

Predicting and Preventing School Dropout with Business Intelligence: Insights from a Systematic Review

by Diana-Margarita Córdova-Esparza, Juan Terven, Julio-Alejandro Romero-González, Karen-Edith Córdova-Esparza, Rocio-Edith López-Martínez, Teresa García-Ramírez and Ricardo Chaparro-Sánchez

Information 2025, 16(4), 326; https://doi.org/10.3390/info16040326 - 19 Apr 2025

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Abstract

School dropout in higher education remains a significant global challenge with profound socioeconomic consequences. To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. This systematic review critically [...] Read more.

School dropout in higher education remains a significant global challenge with profound socioeconomic consequences. To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. This systematic review critically examines the application of BI and predictive analytics for analyzing and preventing student dropout, synthesizing evidence from 230 studies published globally between 1996 and 2025. We collected literature from the Google Scholar and Scopus databases using a comprehensive search strategy, incorporating keywords such as “business intelligence”, “machine learning”, and “big data”. The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. Common predictive variables included personal, socioeconomic, academic, institutional, and engagement-related factors, reflecting dropout’s multifaceted nature. Critical challenges identified include data privacy regulations (e.g., GDPR and FERPA), limited data integration capabilities, interpretability of advanced models, ethical considerations, and educators’ capacity to leverage BI effectively. Despite these challenges, BI applications significantly enhance institutions’ ability to predict dropout accurately and implement timely, targeted interventions. This review emphasizes the need for ongoing research on integrating ethical AI-driven analytics and scaling BI solutions across diverse educational contexts to reduce dropout rates effectively and sustainably. Full article

(This article belongs to the Special Issue ICT-Based Modelling and Simulation for Education)

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19 pages, 4290 KiB

Open AccessArticle

Active Distribution Network Source–Network–Load–Storage Collaborative Interaction Considering Multiple Flexible and Controllable Resources

by Sheng Li, Tianyu Chen and Rui Ding

Information 2025, 16(4), 325; https://doi.org/10.3390/info16040325 - 19 Apr 2025

Viewed by 112

Abstract

In the context of rapid advancement of smart cities, a distribution network (DN) serving as the backbone of urban operations is a way to confront multifaceted challenges that demand innovative solutions. Central among these, it is imperative to optimize resource allocation and enhance [...] Read more.

In the context of rapid advancement of smart cities, a distribution network (DN) serving as the backbone of urban operations is a way to confront multifaceted challenges that demand innovative solutions. Central among these, it is imperative to optimize resource allocation and enhance the efficient utilization of diverse energy sources, with particular emphasis on seamless integration of renewable energy systems into existing infrastructure. At the same time, considering that the traditional power system’s “rigid”, instantaneous, dynamic, and balanced law of electricity, “source-load”, is difficult to adapt to the grid-connection of a high proportion of distributed generations (DGs), the collaborative interaction of multiple flexible controllable resources, like flexible loads, are able to supplement the power system with sufficient “flexibility” to effectively alleviate the uncertainty caused by intermittent fluctuations in new energy. Therefore, an active distribution network (ADN) intraday, reactive, power optimization-scheduling model is designed. The dynamic reactive power collaborative interaction model, considering the integration of DG, energy storage (ES), flexible loads, as well as reactive power compensators into the IEEE 33-node system, is constructed with the goals of reducing intraday network losses, keeping voltage deviations to a minimum throughout the day, and optimizing static voltage stability in an active distribution network. Simulation outcomes for an enhanced IEEE 33-node system show that coordinated operation of source–network–load–storage effectively reduces intraday active power loss, improves voltage regulation capability, and achieves secure and reliable operation under ADN. Therefore, it will contribute to the construction of future smart city power systems to a certain extent. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Smart Cities)

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27 pages, 6641 KiB

Open AccessArticle

Automated Construction and Mining of Text-Based Modern Chinese Character Databases: A Case Study of Fujian

by Xueyan Jian, Wen Yuan, Wu Yuan, Xinqi Gao and Rong Wang

Information 2025, 16(4), 324; https://doi.org/10.3390/info16040324 - 18 Apr 2025

Viewed by 225

Abstract

Historical figures are crucial for understanding historical processes and social changes. However, existing databases of historical figures primarily focused on ancient Chinese individuals and are limited by the simplistic organization of textual information, lacking structured processing. Therefore, this study proposes an automatic method [...] Read more.

Historical figures are crucial for understanding historical processes and social changes. However, existing databases of historical figures primarily focused on ancient Chinese individuals and are limited by the simplistic organization of textual information, lacking structured processing. Therefore, this study proposes an automatic method for constructing a spatio-temporal database of modern Chinese figures. The character state transition matrix reveals the spatio-temporal evolution of historical figures, while the random walk algorithm identifies their primary migration patterns. Using historical figures from Fujian Province (1840–2009) as a case study, the results demonstrate that this method effectively constructs the spatio-temporal chain of figures, encompassing time, space, and events. The character state transition matrix indicates a fluctuating trend of state change from 1840 to 2009, initially increasing and then decreasing. By applying keyword extraction and the random walk method, this study finds that the state transitions and their causes align with the historical trends. The four-dimensional analytical framework of “character-time-space-event” established in this study holds significant value for the field of digital humanities. Full article

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23 pages, 6006 KiB

Open AccessArticle

Collaborative Modeling of BPMN and HCPN: Formal Mapping and Iterative Evolution of Process Models for Scenario Changes

by Zhaoqi Zhang, Feng Ni, Jiang Liu, Niannian Chen and Xingjun Zhou

Information 2025, 16(4), 323; https://doi.org/10.3390/info16040323 - 18 Apr 2025

Viewed by 100

Abstract

Dynamic and changeable business scenarios pose significant challenges to the adaptability and verifiability of process models. Despite its widespread adoption as an ISO-standard modeling language, Business Process Model and Notation (BPMN) faces inherent limitations in formal semantics and verification capabilities, hindering the mathematical [...] Read more.

Dynamic and changeable business scenarios pose significant challenges to the adaptability and verifiability of process models. Despite its widespread adoption as an ISO-standard modeling language, Business Process Model and Notation (BPMN) faces inherent limitations in formal semantics and verification capabilities, hindering the mathematical validation of process evolution behaviors under scenario changes. To address these challenges, this paper proposes a collaborative modeling framework integrating BPMN with hierarchical colored Petri nets (HCPNs), enabling the efficient iterative evolution and correctness verification of process change through formal mapping and localized evolution mechanism. First, hierarchical mapping rules are established with subnet-based modular decomposition, transforming BPMN elements into an HCPN executable model and effectively resolving semantic ambiguities; second, atomic evolution operations (addition, deletion, and replacement) are defined to achieve partial HCPN updates, eliminating the computational overhead of global remapping. Furthermore, an automated verification pipeline is constructed by analyzing state spaces, validating critical properties such as deadlock freeness and behavioral reachability. Evaluated through an intelligent AI-driven service scenario involving multi-gateway processes, the framework demonstrates behavioral effectiveness. This work provides a pragmatic solution for scenario-driven process evolution in domains requiring agile iteration, such as fintech and smart manufacturing. Full article

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17 pages, 2690 KiB

Open AccessArticle

Optimized Digital Watermarking for Robust Information Security in Embedded Systems

by Mohcin Mekhfioui, Nabil El Bazi, Oussama Laayati, Amal Satif, Marouan Bouchouirbat, Chaïmaâ Kissi, Tarik Boujiha and Ahmed Chebak

Information 2025, 16(4), 322; https://doi.org/10.3390/info16040322 - 18 Apr 2025

Viewed by 307

Abstract

With the exponential growth in transactions and exchanges carried out via the Internet, the risks of the falsification and distortion of information are multiplying, encouraged by widespread access to the virtual world. In this context, digital image watermarking has emerged as an essential [...] Read more.

With the exponential growth in transactions and exchanges carried out via the Internet, the risks of the falsification and distortion of information are multiplying, encouraged by widespread access to the virtual world. In this context, digital image watermarking has emerged as an essential solution for protecting digital content by enhancing its durability and resistance to manipulation. However, no current digital watermarking technology offers complete protection against all forms of attack, with each method often limited to specific applications. This field has recently benefited from the integration of deep learning techniques, which have brought significant advances in information security. This article explores the implementation of digital watermarking in embedded systems, addressing the challenges posed by resource constraints such as memory, computing power, and energy consumption. We propose optimization techniques, including frequency domain methods and the use of lightweight deep learning models, to enhance the robustness and resilience of embedded systems. The experimental results validate the effectiveness of these approaches for enhanced image protection, opening new prospects for the development of information security technologies adapted to embedded environments. Full article

(This article belongs to the Special Issue Digital Privacy and Security, 2nd Edition)

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17 pages, 6577 KiB

Open AccessArticle

Multi-Beam STAR MIMO Using Differential Arrays

by Yinyi Zhao, Satheesh Bojja Venkatakrishnan, Constantinos L. Zekios, Soumyajit Mandal and Arjuna Madanayake

Information 2025, 16(4), 321; https://doi.org/10.3390/info16040321 - 18 Apr 2025

Viewed by 284

Abstract

As we are witnessing the surge of ongoing wireless communication systems, simultaneous transmit and receive (STAR) schemes are proving to be advantageous due to the doubling of spectral efficiency. However, for the successful realization of STAR, we need to overcome a major bottleneck [...] Read more.

As we are witnessing the surge of ongoing wireless communication systems, simultaneous transmit and receive (STAR) schemes are proving to be advantageous due to the doubling of spectral efficiency. However, for the successful realization of STAR, we need to overcome a major bottleneck in suppressing the self-interference from the transmitter onto the colocated receiver. Currently, mitigating this interference requires complex hardware and advanced algorithms when employing an array for applications such as multiple input, multiple output (MIMO), and beamforming. This interference can arise from both near-field and far-field coupling in a MIMO beamforming system. Consequently, this paper presents a unique STAR approach that provides an average isolation of 40 dB between Tx and Rx ports across all elements of the MIMO beamforming system. The proposed approach can be extended to large antenna arrays. Full article

(This article belongs to the Special Issue Advances in Telecommunication Networks and Wireless Technology)

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22 pages, 4938 KiB

Open AccessArticle

Transfer Learning for Facial Expression Recognition

by Rajesh Kumar, Giacomo Corvisieri, Tullio Flavio Fici, Syed Ibrar Hussain, Domenico Tegolo and Cesare Valenti

Information 2025, 16(4), 320; https://doi.org/10.3390/info16040320 - 17 Apr 2025

Viewed by 383

Abstract

Facial expressions reflect psychological states and are crucial for understanding human emotions. Traditional facial expression recognition methods face challenges in real-world healthcare applications due to variations in facial structure, lighting conditions and occlusion. We present a methodology based on transfer learning with the [...] Read more.

Facial expressions reflect psychological states and are crucial for understanding human emotions. Traditional facial expression recognition methods face challenges in real-world healthcare applications due to variations in facial structure, lighting conditions and occlusion. We present a methodology based on transfer learning with the pre-trained models VGG-19 and ResNet-152, and we highlight dataset-specific preprocessing techniques that include resizing images to 124 × 124 pixels, augmenting the data and selectively freezing layers to enhance the robustness of the model. This study explores the application of deep learning-based facial expression recognition in healthcare, particularly for remote patient monitoring and telemedicine, where accurate facial expression recognition can enhance patient assessment and early diagnosis of psychological conditions such as depression and anxiety. The proposed method achieved an average accuracy of 0.98 on the CK+ dataset, demonstrating its effectiveness in controlled environments. However performance varied across datasets, with accuracy rates of 0.44 on FER2013 and 0.89 on JAFFE, reflecting the challenges posed by noisy and diverse data. Our findings emphasize the potential of deep learning-based facial expression recognition in healthcare applications while underscoring the importance of dataset-specific model optimization to improve generalization across different data distributions. This research contributes to the advancement of automated facial expression recognition in telemedicine, supporting enhanced doctor–patient communication and improving patient care. Full article

(This article belongs to the Special Issue Emerging Applications of Machine Learning in Healthcare, Industry, and Beyond)

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20 pages, 4029 KiB

Open AccessArticle

AI Narrative Modeling: How Machines’ Intelligence Reproduces Archetypal Storytelling

by Igor Kabashkin, Olga Zervina and Boriss Misnevs

Information 2025, 16(4), 319; https://doi.org/10.3390/info16040319 - 17 Apr 2025

Viewed by 390

Abstract

This study examines how large language models reproduce Jungian archetypal patterns in storytelling. Results indicate that AI excels at replicating structured, goal-oriented archetypes (Hero, Wise Old Man), but it struggles with psychologically complex and ambiguous narratives (Shadow, Trickster). Expert evaluations confirmed these patterns, [...] Read more.

This study examines how large language models reproduce Jungian archetypal patterns in storytelling. Results indicate that AI excels at replicating structured, goal-oriented archetypes (Hero, Wise Old Man), but it struggles with psychologically complex and ambiguous narratives (Shadow, Trickster). Expert evaluations confirmed these patterns, rating AI higher on narrative coherence and thematic alignment than on emotional depth and creative originality. Full article

(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)

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31 pages, 342 KiB

Open AccessReview

Perspectives on Managing AI Ethics in the Digital Age

by Lorenzo Ricciardi Celsi and Albert Y. Zomaya

Information 2025, 16(4), 318; https://doi.org/10.3390/info16040318 - 17 Apr 2025

Viewed by 374

Abstract

The rapid advancement of artificial intelligence (AI) has introduced unprecedented opportunities and challenges, necessitating a robust ethical and regulatory framework to guide its development. This study reviews key ethical concerns such as algorithmic bias, transparency, accountability, and the tension between automation and human [...] Read more.

The rapid advancement of artificial intelligence (AI) has introduced unprecedented opportunities and challenges, necessitating a robust ethical and regulatory framework to guide its development. This study reviews key ethical concerns such as algorithmic bias, transparency, accountability, and the tension between automation and human oversight. It discusses the concept of algor-ethics—a framework for embedding ethical considerations throughout the AI lifecycle—as an antidote to algocracy, where power is concentrated in those who control data and algorithms. The study also examines AI’s transformative potential in diverse sectors, including healthcare, Insurtech, environmental sustainability, and space exploration, underscoring the need for ethical alignment. Ultimately, it advocates for a global, transdisciplinary approach to AI governance that integrates legal, ethical, and technical perspectives, ensuring AI serves humanity while upholding democratic values and social justice. In the second part of the paper, the author offers a synoptic view of AI governance across six major jurisdictions—the United States, China, the European Union, Japan, Canada, and Brazil—highlighting their distinct regulatory approaches. While the EU’s AI Act as well as Japan’s and Canada’s frameworks prioritize fundamental rights and risk-based regulation, the US’s strategy leans towards fostering innovation with executive directives and sector-specific oversight. In contrast, China’s framework integrates AI governance with state-driven ideological imperatives, enforcing compliance with socialist core values, whereas Brazil’s framework is still lacking the institutional depth of the more mature ones mentioned above, despite its commitment to fairness and democratic oversight. Eventually, strategic and governance considerations that should help chief data/AI officers and AI managers are provided in order to successfully leverage the transformative potential of AI for value creation purposes, also in view of the emerging international standards in terms of AI. Full article

(This article belongs to the Special Issue Do (AI) Chatbots Pose any Special Challenges for Trust and Privacy?)

32 pages, 414 KiB

Open AccessReview

A Survey of Open-Source Autonomous Driving Systems and Their Impact on Research

by Nourdine Aliane

Information 2025, 16(4), 317; https://doi.org/10.3390/info16040317 - 17 Apr 2025

Viewed by 792

Abstract

Open-source autonomous driving systems (ADS) have become a cornerstone of autonomous vehicle development. By providing access to cutting-edge technology, fostering global collaboration, and accelerating innovation, these platforms are transforming the automated vehicle landscape. This survey conducts a comprehensive analysis of leading open-source ADS [...] Read more.

Open-source autonomous driving systems (ADS) have become a cornerstone of autonomous vehicle development. By providing access to cutting-edge technology, fostering global collaboration, and accelerating innovation, these platforms are transforming the automated vehicle landscape. This survey conducts a comprehensive analysis of leading open-source ADS platforms, evaluating their functionalities, strengths, and limitations. Through an extensive literature review, the survey explores their adoption and utilization across key research domains. Additionally, it identifies emerging trends shaping the field. The main contributions of this survey include (1) a detailed overview of leading open-source platforms, highlighting their strengths and weaknesses; (2) an examination of their impact on research; and (3) a synthesis of current trends, particularly in interoperability with emerging technologies such as AI/ML solutions and edge computing. This study aims to provide researchers and practitioners with a holistic understanding of open-source ADS platforms, guiding them in selecting the right platforms for future innovation. Full article

(This article belongs to the Special Issue Surveys in Information Systems and Applications)

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21 pages, 528 KiB

Open AccessReview

ChatGPT in ESL Higher Education: Enhancing Writing, Engagement, and Learning Outcomes

by Promethi Das Deep, Nara Martirosyan, Nitu Ghosh and Md. Shiblur Rahaman

Information 2025, 16(4), 316; https://doi.org/10.3390/info16040316 - 17 Apr 2025

Viewed by 481

Abstract

Artificial intelligence (AI) in education has become increasingly common in higher education, particularly in learning English as a second language (ESL). ChatGPT is a conversational AI model frequently used to support language acquisition by creating personalized, interactive learning experiences. This narrative review explored [...] Read more.

Artificial intelligence (AI) in education has become increasingly common in higher education, particularly in learning English as a second language (ESL). ChatGPT is a conversational AI model frequently used to support language acquisition by creating personalized, interactive learning experiences. This narrative review explored the impact of ChatGPT on ESL in higher education within the past three years. It employed a qualitative literature review using EBSCOhost, ERIC, and JSTOR databases. A total of 29 peer-reviewed articles published between 2023 and 2025 were selected for review. The Scale for the Assessment of Narrative Review Articles (SANRA) was applied as an assessment tool for quality and reliability. The results indicated that ChatGPT enhances learning outcomes in ESL by helping students improve their writing skills, grammar proficiency, and speaking fluency. Moreover, it fostered student engagement due to its personalized feedback and accessible learning resources. There were, however, concerns about plagiarism, factual errors, and dependency on AI tools. Although ChatGPT and similar models present promising opportunities and benefits in ESL education, there is a need for structured implementation and ethical guidance. Full article

(This article belongs to the Special Issue Advancing Educational Innovation with Artificial Intelligence)

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16 pages, 16551 KiB

Open AccessArticle

Camera Pose Generation Based on Unity3D

by Hao Luo, Wenjie Luo and Wenzhu Yang

Information 2025, 16(4), 315; https://doi.org/10.3390/info16040315 - 16 Apr 2025

Viewed by 203

Abstract

Deep learning models performing complex tasks require the support of datasets. With the advancement of virtual reality technology, the use of virtual datasets in deep learning models is becoming more and more widespread. Indoor scenes represents a significant area of interest for the [...] Read more.

Deep learning models performing complex tasks require the support of datasets. With the advancement of virtual reality technology, the use of virtual datasets in deep learning models is becoming more and more widespread. Indoor scenes represents a significant area of interest for the application of machine vision technologies. Existing virtual indoor datasets exhibit deficiencies with regard to camera poses, resulting in problems such as occlusion, object omission, and objects having too small of a proportion of the image, and perform poorly in the training for object detection and simultaneous localization and mapping (SLAM) tasks. Aiming at the problems regarding the capacity of cameras to comprehensively capture scene objects, this study presents an enhanced algorithm based on rapidly exploring random tree star (RRT*) for the generation of camera poses in a 3D indoor scene. Meanwhile, in order to generate multimodal data for various deep learning tasks, this study designs an automatic image acquisition module under the Unity3D platform. The experimental results from running the model on several mainstream virtual indoor datasets—such as 3D-FRONT and Hypersim—indicate that the image sequences generated in this study show enhancements in terms of object capture rate and efficiency. Even in cluttered environments such as those in SceneNet RGB-D, the object capture rate remains stable at around 75%. Compared with the image sequences from the original datasets, those generated in this study achieve improvements in the object detection and SLAM tasks, with increases of up to approximately 30% in mAP for the YOLOv10 object detection task and up to approximately 10% in SR for the ORB-SLAM algorithm. Full article

(This article belongs to the Topic Visual Computing and Understanding: New Developments and Trends)

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15 pages, 1708 KiB

Open AccessArticle

ET-Mamba: A Mamba Model for Encrypted Traffic Classification

by Jian Xu, Liangbing Chen, Wenqian Xu, Longxuan Dai, Chenxi Wang and Lei Hu

Information 2025, 16(4), 314; https://doi.org/10.3390/info16040314 - 16 Apr 2025

Viewed by 193

Abstract

With the widespread use of encryption protocols on network data, fast and effective encryption traffic classification can improve the efficiency of traffic analysis. A resampling method combining Wasserstein GAN and random selection is proposed for solving the dataset imbalance problem, and it uses [...] Read more.

With the widespread use of encryption protocols on network data, fast and effective encryption traffic classification can improve the efficiency of traffic analysis. A resampling method combining Wasserstein GAN and random selection is proposed for solving the dataset imbalance problem, and it uses Wasserstein GAN for oversampling and random selection for undersampling to achieve class equalization. Based on Mamba, an ultra-low parametric quantity model, we propose an encrypted traffic classification model, ET-Mamba, which has a pre-training phase and a fine-tuning phase. During the pre-training phase, positional embedding is used to characterize the blocks of the traffic grayscale image, and random masking is used to strengthen the learning of the intrinsic correlation among the blocks of the traffic grayscale image. During the fine-tuning phase, the agent attention mechanism is adopted in the feature extraction phase to achieve global information modeling at a low computational cost, and the SmoothLoss function is designed to solve the problem of the insufficient generalization ability of cross-entropy loss function during training. The experimental results show that the proposed model significantly reduces the number of parameters and outperforms other models in terms of classification accuracy on non-VPN datasets. Full article

(This article belongs to the Special Issue Machine Learning and Data Mining for User Classification)

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25 pages, 12144 KiB

Open AccessArticle

Accurately Estimate and Analyze Human Postures in Classroom Environments

by Zhaoyu Shou, Yongbo Yu, Dongxu Li, Jianwen Mo, Huibing Zhang, Jingwei Zhang and Ziyong Wu

Information 2025, 16(4), 313; https://doi.org/10.3390/info16040313 - 15 Apr 2025

Viewed by 156

Abstract

Estimating human posture in crowded smart teaching environments is a fundamental technical challenge for measuring learners’ engagement levels. This work presents a model for detecting critical points in human posture using ECAv2-HRNet in crowded situations. The paper introduces a method called ECAv2Net, which [...] Read more.

Estimating human posture in crowded smart teaching environments is a fundamental technical challenge for measuring learners’ engagement levels. This work presents a model for detecting critical points in human posture using ECAv2-HRNet in crowded situations. The paper introduces a method called ECAv2Net, which combines a channel feature reinforcement method with the ECANet attention mechanism network, this innovation improves the performance of the network. Additionally, ECAv2Net is integrated into the high-resolution network HRNet to create ECAv2-HRNet. This fusion allows for the incorporation of more useful feature information without increasing the model parameters. The paper also presents a human posture dataset called GUET CLASS PICTURE, which is designed for dense scenes. The experimental results when using this dataset, as well as a public dataset, demonstrate the superior performance of the human posture estimation model based on ECAv2-HRNet proposed in this paper. Full article

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16 pages, 2397 KiB

Open AccessArticle

Trust-Enabled Framework for Smart Classroom Ransomware Detection: Advancing Educational Cybersecurity Through Crowdsourcing

by Qatrunnada Ismail, Shatha Almutairi and Heba Kurdi

Information 2025, 16(4), 312; https://doi.org/10.3390/info16040312 - 14 Apr 2025

Viewed by 203

Abstract

The proliferation of e-learning has exposed smart classroom devices and online learning platforms to ransomware attacks, threatening the integrity of educational processes. This study introduced a novel trust-based crowdsourcing framework to mitigate such attacks in smart classrooms. We evaluated our framework using two [...] Read more.

The proliferation of e-learning has exposed smart classroom devices and online learning platforms to ransomware attacks, threatening the integrity of educational processes. This study introduced a novel trust-based crowdsourcing framework to mitigate such attacks in smart classrooms. We evaluated our framework using two trust management algorithms, EigenTrust and Trust Network Analysis with Subjective Logic (TNaSL), comparing them against a baseline scenario without trust management. Experimental results, based on success rate, accuracy, precision, and recall metrics, demonstrated the significant enhancement of security in crowdsourcing processes. Both implementations exhibited resilience against increasing proportions of malicious nodes. This study contributes to cybersecurity in smart educational settings by demonstrating the efficacy of trust-based crowdsourcing in ransomware detection. Our framework paves the way for more secure digital learning spaces, addressing the cybersecurity challenges in IoT-enabled educational environments. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence and Internet of Things Security: Shaping the Future of Secure Connected Systems)

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20 pages, 8049 KiB

Open AccessArticle

GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions

by Haixiong Ye, Wei Wang and Xiliang Zhang

Information 2025, 16(4), 311; https://doi.org/10.3390/info16040311 - 14 Apr 2025

Viewed by 233

Abstract

In complex marine environments, intelligent vessels require a high level of dynamic perception to process multiple types of information for mitigating collision risks. To ensure the safety of maritime traffic and enhance the efficiency of navigation information, vessel trajectory prediction is crucial for [...] Read more.

In complex marine environments, intelligent vessels require a high level of dynamic perception to process multiple types of information for mitigating collision risks. To ensure the safety of maritime traffic and enhance the efficiency of navigation information, vessel trajectory prediction is crucial for Automatic Identification Systems (AIS). This study introduces a Graph Convolutional Mamba Network (GC-MT) utilizing AIS data for predicting vessel trajectories. To capture motion interaction characteristics, we employed a Graph Convolutional Network (GCN) to construct a spatiotemporal graph that reflects the interaction relationships among various vessels within the maritime information flow. Furthermore, high-level spatiotemporal features were extracted using a Mamba Neural Network (MNN) to incorporate time-related dynamics. Validation against real-world historical AIS data demonstrates that the proposed model achieved improvements of approximately 35% and 28% in the Average Displacement Error (ADE) and Final Displacement Error (FDE), respectively, compared to the leading baseline model. The predictive capability of the proposed method demonstrates its effectiveness in improving maritime navigation safety in a shipping environment with multiple information sources. Full article

(This article belongs to the Special Issue New Deep Learning Approach for Time Series Forecasting)

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24 pages, 1018 KiB

Open AccessArticle

Analysis and Synthesis of Theoretical and Practical Implications of Case Management Model and Notation

by Mateja Bule and Gregor Polančič

Information 2025, 16(4), 310; https://doi.org/10.3390/info16040310 - 14 Apr 2025

Viewed by 281

Abstract

Case Management Model and Notation (CMMN) is a graphical notation used to model less predictable, highly flexible processes that may behave differently in each instance. It uses an event-centred approach and expands on what can be modelled with procedural modelling notations. Nearly a [...] Read more.

Case Management Model and Notation (CMMN) is a graphical notation used to model less predictable, highly flexible processes that may behave differently in each instance. It uses an event-centred approach and expands on what can be modelled with procedural modelling notations. Nearly a decade since the occurrence of CMMN, its practical use is questionable. We performed this research to identify possible reasons for this and to classify the potential advantages and disadvantages of CMMN. With the aforementioned objectives, we conducted a systematic literature review, which provided a broad insight into the state of the investigated object along with techniques for analysing qualitative data, coding, and successive approximation. From an initial set of 942 articles, 43 remain relevant. The results of the analysis and synthesis of the obtained data from relevant articles were generalised codes, which were used to explicitly answer the research questions. The results indicate that CMMN has good foundations in the declarative modelling approach and within the Case Management paradigm. Nevertheless, some issues were identified with the notation and elements of CMMN and with its complement—Business Process Model and Notation (BPMN). Full article

(This article belongs to the Section Information Applications)

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13 pages, 3105 KiB

Open AccessArticle

AI-Based Detection of Optical Microscopic Images of Pseudomonas aeruginosa in Planktonic and Biofilm States

by Bidisha Sengupta, Mousa Alrubayan, Manideep Kolla, Yibin Wang, Esther Mallet, Angel Torres, Ravyn Solis, Haifeng Wang and Prabhakar Pradhan

Information 2025, 16(4), 309; https://doi.org/10.3390/info16040309 - 14 Apr 2025

Viewed by 326

Abstract

Biofilms are resistant microbial cell aggregates that pose risks to the health and food industries and produce environmental contamination. The accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep [...] Read more.

Biofilms are resistant microbial cell aggregates that pose risks to the health and food industries and produce environmental contamination. The accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy. Aptamer DNA-templated silver nanocluster (Ag-NC) was used to prevent biofilm formation, which produced images of the planktonic states of the bacteria. Large-volume bright-field images of bacterial biofilms were used to design the AI model. In particular, we used U-Net with ResNet encoder enhancement to segment biofilm images for AI analysis. Different degrees of biofilm structures can be efficiently detected using ResNet18 and ResNet34 backbones. The potential applications of this technique are also discussed. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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22 pages, 2200 KiB

Open AccessArticle

MultiLTR: Text Ranking with a Multi-Stage Learning-to-Rank Approach

by Hua Yang and Teresa Gonçalves

Information 2025, 16(4), 308; https://doi.org/10.3390/info16040308 - 13 Apr 2025

Viewed by 203

Abstract

The division of retrieval into multiple stages has evolved to balance efficiency and effectiveness among various ranking models. Faster but less accurate models are used to retrieve results from the entire corpus. Slower yet more precise models refine the ranking within the top [...] Read more.

The division of retrieval into multiple stages has evolved to balance efficiency and effectiveness among various ranking models. Faster but less accurate models are used to retrieve results from the entire corpus. Slower yet more precise models refine the ranking within the top candidate list. This study proposes a multi-stage learning-to-rank (MultiLTR) method. MultiLTR applies learning-to-rank techniques across multiple stages. It incorporates text from different fields such as titles, body content, and abstracts to produce a more comprehensive and accurate ranking. MultiLTR iteratively refines ranking accuracy through sequential processing phases. It dynamically selects top-performing rankers from a diverse candidate pool at each stage. Experiments were carried out on benchmark datasets, MQ2007 and MQ2008, using three categories of learning-to-rank algorithms. The results demonstrate that MultiLTR outperforms state-of-the-art ranking approaches, particularly in field-based ranking tasks. This study improves ranking accuracy and offers new insights into enhancing multi-stage ranking strategies. Full article

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11 pages, 316 KiB

Open AccessArticle

The Effect of Technical Fouls on Momentum Change in Basketball: A Comparison of Regular Season vs. Playoffs in the NBA

by Gershon Tenenbaum, Yaniv K. Maymon, Tomer Ben-Zion and Assaf Lev

Information 2025, 16(4), 307; https://doi.org/10.3390/info16040307 - 13 Apr 2025

Viewed by 293

Abstract

Spanning two decades (2000–2021), this study delves into how TF-induced momentum shifts differ between regular season and playoff matchups, factoring in home-court advantage and team score status at the time of the call. Analyzing 4196 cases of technical fouls (TFs) called against coaches, [...] Read more.

Spanning two decades (2000–2021), this study delves into how TF-induced momentum shifts differ between regular season and playoff matchups, factoring in home-court advantage and team score status at the time of the call. Analyzing 4196 cases of technical fouls (TFs) called against coaches, we employ big data analytics to uncover distinct patterns in momentum shifts and their strategic implications. Using advanced statistical modeling, we identify how these effects vary across game contexts, demonstrating how big data enhances decision-making in competitive sports. Logistic regression revealed a significant season-by-location interaction (p < 0.03). The findings revealed that, in the regular season, TFs in home games were associated with a 44%-win rate, compared to 28% in away games. However, in the playoffs, this shifted to 50% at home and 23% away. These results provide valuable insights into the TF–momentum shift phenomenon. Leveraging game analytics to identify patterns in TF-related momentum shifts can help coaches make more informed decisions, including pinpointing the optimal moments for TFs and other strategic actions. Full article

(This article belongs to the Special Issue Information Behaviors: Social Media Challenges and Analytics)

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18 pages, 873 KiB

Open AccessArticle

How Facebook Mediated COVID-19 Risk Communication: Evidence from Chinese External Media During the Winter Olympics

by Liwen Zhang, Yi-Xin Zhou and Ke-ke Shang

Information 2025, 16(4), 306; https://doi.org/10.3390/info16040306 - 13 Apr 2025

Viewed by 165

Abstract

With the widespread adoption of social media worldwide, countries are increasingly using these platforms to manage potential risks and disseminate their content. This study examines the communication effectiveness of six Chinese external media outlets on Facebook during the Winter Olympics, focusing on their [...] Read more.

With the widespread adoption of social media worldwide, countries are increasingly using these platforms to manage potential risks and disseminate their content. This study examines the communication effectiveness of six Chinese external media outlets on Facebook during the Winter Olympics, focusing on their COVID-19 coverage. Using structural equation modelling, we analysed how information presentation and dialogue intervention impacted communication effectiveness. The results indicated that scientific risk description, effective risk information dissemination, and heightened risk awareness in information presentation, as well as dialogue expansion in dialogue intervention, significantly enhanced the communication effectiveness of Facebook. However, dialogic contraction had no significant effect. Technical functionality mediated the relationship between information presentation and communication effectiveness but did not show a significant mediating effect for dialogic intervention. Achieving optimal communication outcomes through social media requires a comprehensive consideration of contextual and motivational factors. Full article

(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)

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33 pages, 2390 KiB

Open AccessReview

Green NFTs: Technologies Related to Energy-Efficient Non-Fungible Tokens

by Zacharoula Sereti, Emmanouil Mavrikos, Christos Cholevas and George E. Tsekouras

Information 2025, 16(4), 305; https://doi.org/10.3390/info16040305 - 11 Apr 2025

Viewed by 273

Abstract

Non-fungible tokens (NFTs) are unique digital assets powered by blockchain technology, enabling secure and decentralized ownership and monetization across diverse industries. However, their energy consumption is primarily linked to energy-intensive consensus mechanisms and has raised significant environmental concerns. This survey provides a comprehensive [...] Read more.

Non-fungible tokens (NFTs) are unique digital assets powered by blockchain technology, enabling secure and decentralized ownership and monetization across diverse industries. However, their energy consumption is primarily linked to energy-intensive consensus mechanisms and has raised significant environmental concerns. This survey provides a comprehensive analysis of the evolution and use of token standards and blockchain workflows in developing green NFT technologies, emphasizing the transition to energy-efficient consensus mechanisms. By seamlessly blending technological acumen with a discerning gaze, the current analysis suggests that, apart from the blockchain consensus mechanisms, the environmental impact of NFTs should also be investigated and linked to certain blockchain factors such as interoperability, scalability, sustainability, and Layer-2 scaling solutions. As such, the current endeavor offers a perspective on the symbiotic relationship between blockchain and NFTs by identifying pathways to balance innovation with environmental stewardship. Finally, this paper offers valuable insights into the role of green NFTs in fostering sustainable digital economies by exploring under-represented applications in various economic and industrial domains. Full article

(This article belongs to the Special Issue Blockchain and AI: Innovations and Applications in ICT)

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31 pages, 5349 KiB

Open AccessArticle

A Mixed-Method Approach for Domain Analysis in Interdisciplinary Fields Using Bibliometrics: The Case of Global Studies

by Carolina Rozo-Higuera

Information 2025, 16(4), 304; https://doi.org/10.3390/info16040304 - 11 Apr 2025

Viewed by 166

Abstract

This study answers how bibliometrics and the analysis of terminology in selected theoretical books and reference sources can allow domain analysis in interdisciplinary fields of knowledge, taking as a case study the global studies (GS) field. A mixed-methods approach was applied to answer [...] Read more.

This study answers how bibliometrics and the analysis of terminology in selected theoretical books and reference sources can allow domain analysis in interdisciplinary fields of knowledge, taking as a case study the global studies (GS) field. A mixed-methods approach was applied to answer this. First, an analysis of GS’s lexicon from three sources: (1) The Encyclopedia of Global Studies (2012), (2) The Global Studies Encyclopedic Dictionary (2014), and (3) The Palgrave Dictionary of Transnational History (2009). Second, the analysis of GS topic tendencies using bibliometrics. The results show (1) the validity of the methods used for domain analysis under the lenses of library and information science (LIS) and (2) the importance of a manual selection of sources for domain analysis and the correspondence between the methods and the application of results using integrative level classification (ILC). The author concludes that domain analysis for emergent interdisciplinary fields of knowledge benefit from quantitative approaches based on a methodology that considers terminology in various formats and can be applied not just for the global studies field. Finally, we emphasize the need for collaboration between librarians and scholars for a better understanding of the dynamics of interdisciplinary vocabularies in science. Full article

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