Information

28 pages, 1083 KiB

Open AccessArticle

Machine Learning with Self-Assessment Manikin Valence Scale for Fine-Grained Sentiment Analysis

by Lindung Parningotan Manik, Harry Susianto, Arawinda Dinakaramani, Niken Pramanik and Totok Suhardijanto

Information 2025, 16(7), 562; https://doi.org/10.3390/info16070562 (registering DOI) - 30 Jun 2025

Abstract

Traditional sentiment analysis methods use lexicons or machine learning models to classify text as positive or negative. These approaches are unable to capture nuance or intensity in short or informal texts. We propose a novel method that uses the Self-Assessment Manikin (SAM) valence [...] Read more.

Traditional sentiment analysis methods use lexicons or machine learning models to classify text as positive or negative. These approaches are unable to capture nuance or intensity in short or informal texts. We propose a novel method that uses the Self-Assessment Manikin (SAM) valence scale, which provides a continuous measurement of sentiment, ranging from extremely positive to extremely negative. We describe the development of a lexicon of emotion-laden words with SAM valence scales and investigate its application to fine-grained sentiment analysis. We also propose a lexicon-based polarity approach to complement textual features in machine learning models trained to predict a numerical sentiment label for a given text. This method is evaluated using a new dataset of short texts with sentiment labels based on expert ratings, which are predicted using various machine learning fusion mechanisms. The lexicon-based polarity method is found to provide improvements of 0.250, 0.999, and 0.261 in the mean squared error for classical machine learning, RNN, and transformer-based architectures, respectively. Full article

14 pages, 4562 KiB

Open AccessArticle

DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network

by Na Su, Jingtao Wang, Jingjing Zhang, Ying Li and Yun Pan

Information 2025, 16(7), 561; https://doi.org/10.3390/info16070561 - 30 Jun 2025

Abstract

The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the [...] Read more.

The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between content and style features after transfer, making effective separation challenging. To address this issue, we propose a Dual-Branch Decoupled Image Style Transfer Network (DBDST-Net) to better disentangle content and style representations. The network consists of two branches: a Content Feature Decoupling Branch, which captures fine-grained content structures for more precise content separation, and a Style Feature Decoupling Branch, which enhances sensitivity to style-specific attributes. To further improve the decoupling performance, we introduce a dense-regressive loss that minimizes the discrepancy between the original content image and the content reconstructed from the stylized output, thereby promoting the independence of content and style features while enhancing image quality. Additionally, to mitigate the limited availability of style data, we employ the Stable Diffusion model to generate stylized samples for data augmentation. Extensive experiments demonstrate that our method achieves a better balance between content preservation and style rendering compared to existing approaches. Full article

16 pages, 2795 KiB

Open AccessArticle

CMHFE-DAN: A Transformer-Based Feature Extractor with Domain Adaptation for EEG-Based Emotion Recognition

by Manal Hilali, Abdellah Ezzati and Said Ben Alla

Information 2025, 16(7), 560; https://doi.org/10.3390/info16070560 - 30 Jun 2025

Abstract

EEG-based emotion recognition (EEG-ER) through deep learning models has gained more attention in recent years, with more researchers focusing on architecture, feature extraction, and generalisability. This paper presents a novel end-to-end deep learning framework for EEG-ER, combining temporal feature extraction, self-attention mechanisms, and [...] Read more.

EEG-based emotion recognition (EEG-ER) through deep learning models has gained more attention in recent years, with more researchers focusing on architecture, feature extraction, and generalisability. This paper presents a novel end-to-end deep learning framework for EEG-ER, combining temporal feature extraction, self-attention mechanisms, and adversarial domain adaptation. The architecture entails a multi-stage 1D CNN for spatiotemporal features from raw EEG signals, followed by a transformer-based attention module for long-range dependencies, and a domain-adversarial neural network (DANN) module with gradient reversal to enable a powerful subject-independent generalisation by learning domain-invariant features. Experiments on benchmark datasets (DEAP, SEED, DREAMER) demonstrate that our approach achieves a state-of-the-art performance, with a significant improvement in cross-subject recognition accuracy compared to non-adaptive frameworks. The architecture tackles key challenges in EEG emotion recognition, including generalisability, inter-subject variability, and temporal dynamics modelling. The results highlight the effectiveness of combining convolutional feature learning with adversarial domain adaptation for robust EEG-ER. Full article

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21 pages, 1016 KiB

Open AccessArticle

Exploring the Adoption of Collaborative Robots for the Preparation of Galenic Formulations

by Luigi Gargioni, Daniela Fogli and Pietro Baroni

Information 2025, 16(7), 559; https://doi.org/10.3390/info16070559 - 30 Jun 2025

Abstract

Galenic preparations are patient-centered medicines prepared by pharmacists or veterinarians, which allow personalizing dosages, overcoming allergy problems, reducing costs, and dealing with rare diseases. However, the current manual production process of galenic preparations poses several challenges to human workers. This paper proposes the [...] Read more.

Galenic preparations are patient-centered medicines prepared by pharmacists or veterinarians, which allow personalizing dosages, overcoming allergy problems, reducing costs, and dealing with rare diseases. However, the current manual production process of galenic preparations poses several challenges to human workers. This paper proposes the use of collaborative robots to help pharmacists carry out the most tiresome, precise, error-prone, and time-consuming tasks. In particular, an end-user development (EUD) environment called PRAISE (pharmaceutical robotic and AI system for end users) has been designed to support pharmacists in programming the tasks to be performed by a collaborative robot. The EUD environment integrates artificial intelligence (AI) features based on large language models but ensures that end users always have complete control over the generated output, that is, a robot program. The paper focuses on the application of a human-centered methodology adopted to design PRAISE by involving representative end users (experts in the pharmaceutical sector) from system ideation to its evaluation. Design implications related to AI-enabled EUD for collaborative robots are the main findings of the paper. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)

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11 pages, 243 KiB

Open AccessArticle

The Landau-Feynman Transiently Open Quantum System: Entanglement and Density Operators

by Alain Deville and Yannick Deville

Information 2025, 16(7), 558; https://doi.org/10.3390/info16070558 - 30 Jun 2025

Abstract

Users of quantum mechanics are familiar with the concept of a statistical mixture as introduced by von Neumann, and with the use of a density operator in that context. A density operator may also be used in another situation, introduced by Landau, with [...] Read more.

Users of quantum mechanics are familiar with the concept of a statistical mixture as introduced by von Neumann, and with the use of a density operator in that context. A density operator may also be used in another situation, introduced by Landau, with a transient coupling between the two parts of a quantum bipartite system. But more than fifty years after a clarifying work by Feynman on the subject, a confusion still persists about what we call the Landau-Feynman situation. In this paper we establish that, when facing that situation, the right concept to be used is not the one of a mixed state - be it qualified as proper or improper -, but the one of entanglement. Full article

(This article belongs to the Section Information Theory and Methodology)

23 pages, 1575 KiB

Open AccessArticle

An Integrated Blockchain Framework for Secure Autonomous Vehicle Communication System

by Juan de Anda-Suárez, José Luis López-Ramírez, Daniel Jimenez-Mendoza, José Manuel Benitez-Quintero, Eli Gabriel Avina-Bravo, David Asael Gutierrez-Hernandez and Juan Gabriel Avina-Cervantes

Information 2025, 16(7), 557; https://doi.org/10.3390/info16070557 - 30 Jun 2025

Abstract

Autonomous Vehicles (AV) have been extensively studied in both scientific and social contexts. Over the past two decades, there has been a significant rise in their real-world applications, including neural networks, Blockchain, Internet of Things, autonomous navigation, computer vision, automation processes, and various [...] Read more.

Autonomous Vehicles (AV) have been extensively studied in both scientific and social contexts. Over the past two decades, there has been a significant rise in their real-world applications, including neural networks, Blockchain, Internet of Things, autonomous navigation, computer vision, automation processes, and various other areas. Hence, it is imperative to investigate the interplay between software, hardware, and individuals. To guarantee secure and unaffected interactions within autonomous vehicle devices and networks, decentralized Blockchain technology is proposed. This study presents the introduction of a framework we named “DEMU-NAV” for an ecosystem that includes Artificial Intelligence (AI), humans, and robots. The framework makes use of a decentralized Blockchain, Smart-Contract (SC), and Internet of things (IoT) network. Our framework was implemented using Ethereum and Python, enabling us to oversee Blockchain, Smart-Contracts, and the IoT for the facilitation of autonomous vehicle navigation. Full article

(This article belongs to the Special Issue Blockchain, Technology and Its Application)

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20 pages, 8948 KiB

Open AccessArticle

An Architecture for Intelligent Tutoring in Virtual Reality: Integrating LLMs and Multimodal Interaction for Immersive Learning

by Mohamed El Hajji, Tarek Ait Baha, Anas Berka, Hassan Ait Nacer, Houssam El Aouifi and Youssef Es-Saady

Information 2025, 16(7), 556; https://doi.org/10.3390/info16070556 - 29 Jun 2025

Abstract

Immersive learning has been recognized as a promising paradigm for enhancing educational experiences through the integration of VR. We propose an architecture for intelligent tutoring in immersive VR environments that employs LLM-based non-playable characters. Key system capabilities are identified, including natural language understanding, [...] Read more.

Immersive learning has been recognized as a promising paradigm for enhancing educational experiences through the integration of VR. We propose an architecture for intelligent tutoring in immersive VR environments that employs LLM-based non-playable characters. Key system capabilities are identified, including natural language understanding, real-time adaptive dialogue, and multimodal interaction through hand tracking, gaze detection, and haptic feedback. The system synchronizes speech output with NPC animations, enhancing both interactional realism and cognitive immersion. This design demonstrates that AI-driven VR interactions can significantly improve learner engagement. System performance was generally stable; however, minor latency was observed during speech processing, indicating areas for technical refinement. Overall, this research highlights the transformative potential of VR in education and emphasizes the importance of ongoing optimization to maximize its effectiveness in immersive learning contexts. Full article

(This article belongs to the Special Issue Interactive Learning: Human in the Loop System Design for Active Human–Computer Interactions)

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29 pages, 5909 KiB

Open AccessArticle

Mapping the Evolution: A Bibliometric Analysis of Employee Engagement and Performance in the Age of Artificial Intelligence-Based Solutions

by Chetan Sharma, Nisha Chanana and Hsin-Yuan Chen

Information 2025, 16(7), 555; https://doi.org/10.3390/info16070555 - 29 Jun 2025

Abstract

Organizational behavior examines the interactions of individuals and groups within businesses, while human resource management (HRM) focuses on enhancing workforce efficiency through recruitment, training, and employee relations. The success of an organization depends on the relationship between employee engagement and performance, as engaged [...] Read more.

Organizational behavior examines the interactions of individuals and groups within businesses, while human resource management (HRM) focuses on enhancing workforce efficiency through recruitment, training, and employee relations. The success of an organization depends on the relationship between employee engagement and performance, as engaged individuals enhance productivity and innovation. This study aims to conduct a comprehensive bibliometric analysis of the academic research on the relationship between artificial intelligence (AI), employee engagement, and performance. This study highlights trends, countries, sources, and keywords in this field. The authors analyzed 11,291 articles in the first phase, 42,358 articles were analyzed in the second phase, and 606 articles were analyzed in the third phase. This study highlights the growth of the research in this area and identifies the most productive years and regional contributions. The citation analysis is used to identify the relevant research and renowned authors. This study also addresses ethical concerns related to the implementation of artificial intelligence (AI) in the workplace. This study indicates theme variations among national contributions, highlighting differing socio-cultural and theoretical perspectives on AI adoption in HRM, from behavioral leadership models to efficiency-oriented frameworks. In summary, this bibliometric study provides valuable insights into the evolution of the research topics related to AI’s impact on employee engagement and productivity, spanning multiple disciplines, such as psychology, organizational behavior, and computer science. It is relevant for the researchers, practitioners, and businesses interested in understanding and utilizing AI in the workplace. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)

21 pages, 1761 KiB

Open AccessArticle

A FIT4NER Generic Approach for Framework-Independent Medical Named Entity Recognition

by Florian Freund, Philippe Tamla, Frederik Wilde and Matthias Hemmje

Information 2025, 16(7), 554; https://doi.org/10.3390/info16070554 - 29 Jun 2025

Abstract

This article focuses on assisting medical professionals in analyzing domain-specific texts and selecting and comparing Named Entity Recognition (NER) frameworks. It details the development and evaluation of a system that utilizes a generic approach alongside the structured Nunamaker methodology. This system empowers medical [...] Read more.

This article focuses on assisting medical professionals in analyzing domain-specific texts and selecting and comparing Named Entity Recognition (NER) frameworks. It details the development and evaluation of a system that utilizes a generic approach alongside the structured Nunamaker methodology. This system empowers medical professionals to train, evaluate, and compare NER models across diverse frameworks, such as Stanford CoreNLP, spaCy, and Hugging Face Transformers, independent of their specific implementations. Additionally, it introduces a concept for modeling a general training and evaluation process. Finally, experiments using various ontologies from the CRAFT corpus are conducted to assess the effectiveness of the current prototype. Full article

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

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21 pages, 6865 KiB

Open AccessArticle

Elegante+: A Machine Learning-Based Optimization Framework for Sparse Matrix–Vector Computations on the CPU Architecture

by Muhammad Ahmad, Sardar Usman, Ameer Hamza, Muhammad Muzamil and Ildar Batyrshin

Information 2025, 16(7), 553; https://doi.org/10.3390/info16070553 - 29 Jun 2025

Abstract

Sparse matrix–vector multiplication (SpMV) plays a significant role in the computational costs of many scientific applications such as 2D/3D robotics, power network problems, and computer vision. Numerous implementations using different sparse matrix formats have been introduced to optimize this kernel on CPUs and [...] Read more.

Sparse matrix–vector multiplication (SpMV) plays a significant role in the computational costs of many scientific applications such as 2D/3D robotics, power network problems, and computer vision. Numerous implementations using different sparse matrix formats have been introduced to optimize this kernel on CPUs and GPUs. However, due to the sparsity patterns of matrices and the diverse configurations of hardware, accurately modeling the performance of SpMV remains a complex challenge. SpMV computation is often a time-consuming process because of its sparse matrix structure. To address this, we propose a machine learning-based tool, namely Elegante+, that predicts optimal scheduling policies by analyzing matrix structures. This approach eliminates the need for repetitive trial and error, minimizes errors, and finds the best solution of the SpMV kernel, which enables users to make informed decisions about scheduling policies that maximize computational efficiency. For this purpose, we collected 1000+ sparse matrices from the SuiteSparse matrix market collection and converted them into the compressed sparse row (CSR) format, and SpMV computation was performed by extracting 14 key sparse matrix features. After creating a comprehensive dataset, we trained various machine learning models to predict the optimal scheduling policy, significantly enhancing the computational efficiency and reducing the overhead in high-performance computing environments. Our proposed tool, Elegante+ (XGB with all SpMV features), achieved the highest cross-validation score of 79% and performed five times faster than the default scheduling policy during SpMV in a high-performance computing (HPC) environment. Full article

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11 pages, 502 KiB

Open AccessArticle

Robust and Scalable Quantum Repeaters Using Machine Learning

by Diego Fuentealba, Jackson Dahn, James Steck and Elizabeth Behrman

Information 2025, 16(7), 552; https://doi.org/10.3390/info16070552 - 28 Jun 2025

Abstract

Quantum repeaters are integral systems to quantum computing and quantum communication as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that general quantum states cannot be directly copied, one cannot perform signal [...] Read more.

Quantum repeaters are integral systems to quantum computing and quantum communication as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that general quantum states cannot be directly copied, one cannot perform signal amplification in the usual way. The standard approach uses entanglement swapping, in which quantum states are teleported from one (short) segment to the next, using at each step a shared entangled pair. This is the job of the repeater. In general, this requires reliable quantum memories and shared entanglement resources, which are vulnerable to noise and decoherence. It is also difficult to manually create and implement the quantum algorithm for the swap circuit as the size of the system increases. Here, we propose a different approach: to use machine learning to train a repeater node. To demonstrate the feasibility of this method, the system is simulated in MATLAB 2022a. Training is conducted for a system of 2 qubits. It is then scaled up, with no additional training, to systems of 4, 6, and 8 qubits using transfer learning. Finally, the systems are tested in noisy conditions. The results show that the scale-up is very effective and relatively easy, and the effects of noise and decoherence are

r e d u c e d

as the size of the system increases. Full article

(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)

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24 pages, 3359 KiB

Open AccessArticle

Real-Time Electrical Energy Optimization in E-Commerce Systems Based on IoT and Mobile Agents

by Mohamed Shili and Sajid Anwar

Information 2025, 16(7), 551; https://doi.org/10.3390/info16070551 - 27 Jun 2025

Abstract

The integration of the Internet of Things (IoT) into mobile agent technology has fundamentally transformed the landscape of e-commerce by enabling intelligent, adaptive, and energy-efficient solutions. In this paper, we present a new system for integrating the information-sharing capability of IoT-enabled devices with [...] Read more.

The integration of the Internet of Things (IoT) into mobile agent technology has fundamentally transformed the landscape of e-commerce by enabling intelligent, adaptive, and energy-efficient solutions. In this paper, we present a new system for integrating the information-sharing capability of IoT-enabled devices with the advanced abilities of mobile agents for the optimal utilization of energy when conducting e-commerce activity. The mobile agents are used as a mediating agent in the transaction and will capture operation data to share with stakeholders (not in the transaction) who might be able to provide services in association with that transaction. The operational data is collected, stored, and analyzed in real-time via IoT devices, facilitating adaptive decision-making while providing continuous monitoring of the system and servicing to improve energy management, efficiency, and operational performance. The combined IoT and energy capacity will enhance data sharing and provide more energy-efficient activities. The evaluation of the system was completed through simulations, as well as through real-world scenarios, achieving a decrease of approximately 27.8% in total energy consumption and savings of over 30% on operational costs. Moreover, the proposed architecture achieved a reduction of up to 38.9% for response times for resource management, under load, while also demonstrating a 50% reduction in response time for real-time event handling. Therefore, the effects of the proposed approach have been proven to be effective through simulations and real-world case studies, showing improvements in energy consumption and costs, as well as flexibility and adaptability. The findings of this study show that this framework not only minimizes energy consumption but also maximizes scalability, responsiveness to user demands, and robustness against variability in an e-commerce workload. This effort illustrates the potential for extending the lifetimes of e-commerce infrastructures and developing sustainable e-commerce models, demonstrating how IoT-based architectures can facilitate better resource allocation while achieving sustainability goals. Full article

(This article belongs to the Section Internet of Things (IoT))

31 pages, 8644 KiB

Open AccessArticle

Development of a UR5 Cobot Vision System with MLP Neural Network for Object Classification and Sorting

by Szymon Kluziak and Piotr Kohut

Information 2025, 16(7), 550; https://doi.org/10.3390/info16070550 - 27 Jun 2025

Abstract

This paper presents the implementation of a vision system for a collaborative robot equipped with a web camera and a Python-based control algorithm for automated object-sorting tasks. The vision system aims to detect, classify, and manipulate objects within the robot’s workspace using only [...] Read more.

This paper presents the implementation of a vision system for a collaborative robot equipped with a web camera and a Python-based control algorithm for automated object-sorting tasks. The vision system aims to detect, classify, and manipulate objects within the robot’s workspace using only 2D camera images. The vision system was integrated with the Universal Robots UR5 cobot and designed for object sorting based on shape recognition. The software stack includes OpenCV for image processing, NumPy for numerical operations, and scikit-learn for multilayer perceptron (MLP) models. The paper outlines the calibration process, including lens distortion correction and camera-to-robot calibration in a hand-in-eye configuration to establish the spatial relationship between the camera and the cobot. Object localization relied on a virtual plane aligned with the robot’s workspace. Object classification was conducted using contour similarity with Hu moments, SIFT-based descriptors with FLANN matching, and MLP-based neural models trained on preprocessed images. Conducted performance evaluations encompassed accuracy metrics for used identification methods (MLP classifier, contour similarity, and feature descriptor matching) and the effectiveness of the vision system in controlling the cobot for sorting tasks. The evaluation focused on classification accuracy and sorting effectiveness, using sensitivity, specificity, precision, accuracy, and F1-score metrics. Results showed that neural network-based methods outperformed traditional methods in all categories, concurrently offering more straightforward implementation. Full article

(This article belongs to the Section Information Applications)

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24 pages, 1237 KiB

Open AccessReview

Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks

by James C. L. Chow and Kay Li

Information 2025, 16(7), 549; https://doi.org/10.3390/info16070549 - 27 Jun 2025

Abstract

Large language models (LLMs) are transforming the capabilities of medical chatbots by enabling more context-aware, human-like interactions. This review presents a comprehensive analysis of their applications, technical foundations, benefits, challenges, and future directions in healthcare. LLMs are increasingly used in patient-facing roles, such [...] Read more.

Large language models (LLMs) are transforming the capabilities of medical chatbots by enabling more context-aware, human-like interactions. This review presents a comprehensive analysis of their applications, technical foundations, benefits, challenges, and future directions in healthcare. LLMs are increasingly used in patient-facing roles, such as symptom checking, health information delivery, and mental health support, as well as in clinician-facing applications, including documentation, decision support, and education. However, as a study from 2024 warns, there is a need to manage “extreme AI risks amid rapid progress”. We examine transformer-based architectures, fine-tuning strategies, and evaluation benchmarks specific to medical domains to identify their potential to transfer and mitigate AI risks when using LLMs in medical chatbots. While LLMs offer advantages in scalability, personalization, and 24/7 accessibility, their deployment in healthcare also raises critical concerns. These include hallucinations (the generation of factually incorrect or misleading content by an AI model), algorithmic biases, privacy risks, and a lack of regulatory clarity. Ethical and legal challenges, such as accountability, explainability, and liability, remain unresolved. Importantly, this review integrates broader insights on AI safety, drawing attention to the systemic risks associated with rapid LLM deployment. As highlighted in recent policy research, including work on managing extreme AI risks, there is an urgent need for governance frameworks that extend beyond technical reliability to include societal oversight and long-term alignment. We advocate for responsible innovation and sustained collaboration among clinicians, developers, ethicists, and regulators to ensure that LLM-powered medical chatbots are deployed safely, equitably, and transparently within healthcare systems. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU))

18 pages, 1663 KiB

Open AccessArticle

Trustworthy Load Prediction for Cantilever Roadheader Robot Without Imputation

by Pengjiang Wang, Yuxin Li, Yunwang Li, Yang Shen, Weixiong Zheng and Shigen Fu

Information 2025, 16(7), 548; https://doi.org/10.3390/info16070548 - 27 Jun 2025

Abstract

We propose a trustworthy load prediction method for a cantilever roadheader robot without imputation. Specifically, we design a load-trustworthy-boosting (LTB) algorithm for coal and rock cutting loads that accounts for missing data in complex underground environments. We introduce a trustworthy decision tree that [...] Read more.

We propose a trustworthy load prediction method for a cantilever roadheader robot without imputation. Specifically, we design a load-trustworthy-boosting (LTB) algorithm for coal and rock cutting loads that accounts for missing data in complex underground environments. We introduce a trustworthy decision tree that integrates mixed-integer programming (MIP) and Missing Incorporated in Attributes (MIA) as the base predictor, which can handle missing data, thereby accelerating load prediction and improving prediction accuracy. Furthermore, we utilize boosting techniques to enhance the prediction performance of the base predictor by incorporating cutting safety–trust constraints during the prediction process. We derive the convergence of the algorithm theoretically and verify the accuracy and reliability of the algorithm through experiments. The experimental results show that the proposed algorithm is superior to state-of-the-art load prediction algorithms both without and with missing data considered. This method can provide a reliable decision-making basis for underground unmanned intelligent excavation. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

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20 pages, 1288 KiB

Open AccessArticle

Intrusion Alert Analysis Method for Power Information Communication Networks Based on Data Processing Units

by Rui Zhang, Mingxuan Zhang, Yan Liu, Zhiyi Li, Weiwei Miao and Sujie Shao

Information 2025, 16(7), 547; https://doi.org/10.3390/info16070547 - 27 Jun 2025

Abstract

Leveraging Data Processing Units (DPUs) deployed at network interfaces, the DPU-accelerated Intrusion Detection System (IDS) enables microsecond-latency initial traffic inspection through hardware offloading. However, while generating high-throughput alerts, this mechanism amplifies the inherent redundancy and noise issues of traditional IDS systems. This paper [...] Read more.

Leveraging Data Processing Units (DPUs) deployed at network interfaces, the DPU-accelerated Intrusion Detection System (IDS) enables microsecond-latency initial traffic inspection through hardware offloading. However, while generating high-throughput alerts, this mechanism amplifies the inherent redundancy and noise issues of traditional IDS systems. This paper proposes an alert correlation method using multi-similarity factor aggregation and a suffix tree model. First, alerts are preprocessed using LFDIA, employing multiple similarity factors and dynamic thresholding to cluster correlated alerts and reduce redundancy. Next, an attack intensity time series is generated and smoothed with a Kalman filter to eliminate noise and reveal attack trends. Finally, the suffix tree models attack activities, capturing key behavioral paths of high-severity alerts and identifying attacker patterns. Experimental evaluations on the CPTC-2017 and CPTC-2018 datasets validate the proposed method’s effectiveness in reducing alert redundancy, extracting critical attack behaviors, and constructing attack activity sequences. The results demonstrate that the method not only significantly reduces the number of alerts but also accurately reveals core attack characteristics, enhancing the effectiveness of network security defense strategies. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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41 pages, 1353 KiB

Open AccessArticle

Improving Survey Data Interpretation: A Novel Approach to Analyze Single-Item Ordinal Responses with Non-Response Categories

by Ewa Roszkowska

Information 2025, 16(7), 546; https://doi.org/10.3390/info16070546 - 27 Jun 2025

Abstract

Questionnaire data plays a key role in social research, especially when evaluating public attitudes using Likert-type scales. Yet, traditional analyses often merge some ordinal categories and exclude responses such as Don’t Know, No Answer, or Refused—risking the loss of valuable information. This study [...] Read more.

Questionnaire data plays a key role in social research, especially when evaluating public attitudes using Likert-type scales. Yet, traditional analyses often merge some ordinal categories and exclude responses such as Don’t Know, No Answer, or Refused—risking the loss of valuable information. This study introduces BS-TOSIE (Belief Structure-Based TOPSIS for Survey Item Evaluation), a novel method that preserves and integrates all response types, including ambiguous ones. By combining the Belief Structure framework with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, BS-TOSIE offers a structured approach to ranking and evaluating individual survey items measured on an ordinal scale, even in the presence of missing or incomplete data. Response distributions are transformed into a belief structure vector, enabling comparison against ideal and anti-ideal benchmarks. We demonstrate this approach using data from the Quality of Life in European Cities survey to assess perceptions of local governance in European cities. This study analyzes changes in citizen satisfaction with local public administration across five key dimensions—timeliness, procedural clarity, fairness of fees, digital accessibility, and perceived corruption—in 83 European cities between 2019 and 2023. The findings reveal persistent regional disparities, with Northern and Western European cities consistently outperforming those in Southern and Eastern Europe, although some cities in Central Europe show signs of improvement. Zurich consistently received high satisfaction scores, while other cities, such as Rome and Palermo, showed lower scores. Unlike traditional methods, our approach preserves the full spectrum of responses, yielding more nuanced and interpretable insights. The results show that BS-TOSIE enhances both the clarity and depth of survey analysis, making a methodological contribution to the evaluation of ordinal data and offering empirical insights into public perceptions of local city administration. Full article

(This article belongs to the Special Issue New Applications in Multiple Criteria Decision Analysis, 3rd Edition)

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21 pages, 8895 KiB

Open AccessArticle

Opioid Crisis Detection in Social Media Discourse Using Deep Learning Approach

by Muhammad Ahmad, Grigori Sidorov, Maaz Amjad, Iqra Ameer and Ildar Batyrshin

Information 2025, 16(7), 545; https://doi.org/10.3390/info16070545 - 27 Jun 2025

Abstract

The opioid drug overdose death rate remains a significant public health crisis in the U.S., where an opioid epidemic has led to a dramatic rise in overdose deaths over the past two decades. Since 1999, opioids have been implicated in approximately 75% of [...] Read more.

The opioid drug overdose death rate remains a significant public health crisis in the U.S., where an opioid epidemic has led to a dramatic rise in overdose deaths over the past two decades. Since 1999, opioids have been implicated in approximately 75% of the nearly one million drug-related deaths. Research indicates that the epidemic is caused by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and social isolation. Impeding this research is the lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolution. To address this issue, we sourced data from Reddit, where people share self-reported experiences with opioid substances, specifically using opioid drugs through different routes of administration. To achieve this objective, an opioid overdose dataset is created and manually annotated in binary and multi-classification, along with detailed annotation guidelines. In traditional manual investigations, the route of administration is determined solely through biological laboratory testing. This study investigates the efficacy of an automated tool leveraging natural language processing and transformer model, such as RoBERTa, to analyze patterns of substance use. By systematically examining these patterns, the model contributes to public health surveillance efforts, facilitating the identification of at-risk populations and informing the development of targeted interventions. This approach ultimately aims to enhance prevention and treatment strategies for opioid misuse through data-driven insights. The findings show that our proposed methodology achieved the highest cross-validation score of 93% for binary classification and 91% for multi-class classification, demonstrating performance improvements of 9.41% and 10.98%, respectively, over the baseline model (XGB, 85% in binary class and 81% in multi-class). Full article

(This article belongs to the Special Issue Learning and Knowledge: Theoretical Issues and Biological Applications)

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16 pages, 307 KiB

Open AccessArticle

Being (Not) Successful in Internationalisation After Receiving Export Support: Which Predictors Are Able to Forecast It and How Accurately?

by Oliver Lukason and Tiia Vissak

Information 2025, 16(7), 544; https://doi.org/10.3390/info16070544 - 27 Jun 2025

Abstract

This paper aims to outline which predictors are able to forecast being (not) successful in internationalisation after receiving export support and how accurately they can perform this task. Using data on export grant recipients from an Estonian export support programme, 15 theoretically motivated [...] Read more.

This paper aims to outline which predictors are able to forecast being (not) successful in internationalisation after receiving export support and how accurately they can perform this task. Using data on export grant recipients from an Estonian export support programme, 15 theoretically motivated predictors grouped into four domains are used to forecast 24 different proxies of (non-)success with logistic regression and neural networks. The domains focus on firms’ general characteristics, earlier financial and export performance, and export-grant-specific characteristics. The highest areas under the curve exceed the 0.9 threshold, therefore indicating excellent predictive abilities, while more specific (non-)success proxies can be predicted less accurately than general ones. Predictors portraying firm size and export support size emerge as the best in the case of both methods, while in different neural networks, at least one predictor from each of the four domains is among the most important ones. These results lead to multiple practical implications concerning how to select firms into export grant programmes. Full article

(This article belongs to the Special Issue Data-Driven Decision-Making in Intelligent Systems)

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