Information

19 pages, 3509 KiB

Open AccessArticle

Explainable Machine Learning Model for Source Type Identification of Mine Inrush Water

by Yong Yang, Jing Li, Huawei Tao, Yong Cheng and Li Zhao

Information 2025, 16(8), 648; https://doi.org/10.3390/info16080648 - 30 Jul 2025

The prevention and control of mine inrush water has always been a major challenge for safety. By identifying the type of water source and analyzing the real-time changes in water composition, sudden water inrush accidents can be monitored in a timely manner to [...] Read more.

The prevention and control of mine inrush water has always been a major challenge for safety. By identifying the type of water source and analyzing the real-time changes in water composition, sudden water inrush accidents can be monitored in a timely manner to avoid major accidents. This paper proposes a novel explainable machine learning model for source type identification of mine inrush water. The paper expands the original monitoring system into the XinJi No.2 Mine in Huainan Mining Area. Based on the online water composition data, using the Spearman coefficient formula, it analyzes the water chemical characteristics of different aquifers to extract key discriminant factors. Then, the Conv1D-GRU model was built to deeply connect factors for precise water source identification. The experimental results show an accuracy rate of 85.37%. In addition, focused on the interpretability, the experiment quantified the impact of different features on the model using SHAP (Shapley Additive Explanations). It provides new reference for the source type identification of mine inrush water in mine disaster prevention and control. Full article

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24 pages, 6025 KiB

Open AccessArticle

Uniform Manifold Approximation and Projection Filtering and Explainable Artificial Intelligence to Detect Adversarial Machine Learning

by Achmed Samuel Koroma, Sara Narteni, Enrico Cambiaso and Maurizio Mongelli

Information 2025, 16(8), 647; https://doi.org/10.3390/info16080647 - 29 Jul 2025

Abstract

Adversarial machine learning exploits the vulnerabilities of artificial intelligence (AI) models by inducing malicious distortion in input data. Starting with the effect of adversarial methods on well-known MNIST and CIFAR-10 open datasets, this paper investigates the ability of Uniform Manifold Approximation and Projection [...] Read more.

Adversarial machine learning exploits the vulnerabilities of artificial intelligence (AI) models by inducing malicious distortion in input data. Starting with the effect of adversarial methods on well-known MNIST and CIFAR-10 open datasets, this paper investigates the ability of Uniform Manifold Approximation and Projection (UMAP) in providing useful representations of both legitimate and malicious images and analyzes the attacks’ behavior under various conditions. By enabling the extraction of decision rules and the ranking of important features from classifiers such as decision trees, eXplainable AI (XAI) achieves zero false positives and negatives in detection through very simple if-then rules over UMAP variables. Several examples are reported in order to highlight attacks behaviour. The data availability statement details all code and data which is publicly available to offer support to reproducibility. Full article

(This article belongs to the Special Issue Application of Machine Learning in Data Science and Computational Intelligence)

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28 pages, 2939 KiB

Open AccessArticle

Trajectory Prediction and Decision Optimization for UAV-Assisted VEC Networks: An Integrated LSTM-TD3 Framework

by Jiahao Xie and Hao Hao

Information 2025, 16(8), 646; https://doi.org/10.3390/info16080646 - 29 Jul 2025

Abstract

With the rapid development of intelligent transportation systems (ITSs) and Internet of Things (IoT), vehicle-mounted edge computing (VEC) networks are facing the challenge of handling increasingly growing computation-intensive and latency-sensitive tasks. In the UAV-assisted VEC network, by introducing mobile edge servers, the coverage [...] Read more.

With the rapid development of intelligent transportation systems (ITSs) and Internet of Things (IoT), vehicle-mounted edge computing (VEC) networks are facing the challenge of handling increasingly growing computation-intensive and latency-sensitive tasks. In the UAV-assisted VEC network, by introducing mobile edge servers, the coverage of ground infrastructure is effectively supplemented. However, there is still the problem of decision-making lag in a highly dynamic environment. This paper proposes a deep reinforcement learning framework based on the long short-term memory (LSTM) network for trajectory prediction to optimize resource allocation in UAV-assisted VEC networks. Uniquely integrating vehicle trajectory prediction with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, this framework enables proactive computation offloading and UAV trajectory planning. Specifically, we design an LSTM network with an attention mechanism to predict the future trajectory of vehicles and integrate the prediction results into the optimization decision-making process. We propose state smoothing and data augmentation techniques to improve training stability and design a multi-objective optimization model that incorporates the Age of Information (AoI), energy consumption, and resource leasing costs. The simulation results show that compared with existing methods, the method proposed in this paper significantly reduces the total system cost, improves the information freshness, and exhibits better environmental adaptability and convergence performance under various network conditions. Full article

21 pages, 1133 KiB

Open AccessArticle

Research on China’s Innovative Cybersecurity Education System Oriented Toward Engineering Education Accreditation

by Yimei Yang, Jinping Liu and Yujun Yang

Information 2025, 16(8), 645; https://doi.org/10.3390/info16080645 - 29 Jul 2025

Abstract

This study, based on engineering education accreditation standards, addresses the supply–demand imbalance in China’s cybersecurity talent cultivation by constructing a sustainable “education-industry-society” collaborative model. Through case studies at Huaihua University and other institutions, employing methods such as literature analysis, field research, and empirical [...] Read more.

This study, based on engineering education accreditation standards, addresses the supply–demand imbalance in China’s cybersecurity talent cultivation by constructing a sustainable “education-industry-society” collaborative model. Through case studies at Huaihua University and other institutions, employing methods such as literature analysis, field research, and empirical investigation, we systematically explore reform pathways for an innovative cybersecurity talent development system. The research proposes a “three-platform, four-module” practical teaching framework, where the coordinated operation of the basic skills training platform, comprehensive ability development platform, and innovation enhancement platform significantly improves students’ engineering competencies (practical courses account for 41.6% of the curriculum). Findings demonstrate that eight industry-academia practice bases established through deep collaboration effectively align teaching content with industry needs, substantially enhancing students’ innovative and practical abilities (172 national awards, 649 provincial awards). Additionally, the multi-dimensional evaluation mechanism developed in this study enables a comprehensive assessment of students’ professional skills, practical capabilities, and innovative thinking. These reforms have increased the employment rate of cybersecurity graduates to over 90%, providing a replicable solution to China’s talent shortage. The research outcomes offer valuable insights for discipline development under engineering education accreditation and contribute to implementing sustainable development concepts in higher education. Full article

(This article belongs to the Topic Explainable AI in Education)

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25 pages, 3625 KiB

Open AccessArticle

Automated Classification of Public Transport Complaints via Text Mining Using LLMs and Embeddings

by Daniyar Rakhimzhanov, Saule Belginova and Didar Yedilkhan

Information 2025, 16(8), 644; https://doi.org/10.3390/info16080644 - 29 Jul 2025

Abstract

The proliferation of digital public service platforms and the expansion of e-government initiatives have significantly increased the volume and diversity of citizen-generated feedback. This trend emphasizes the need for classification systems that are not only tailored to specific administrative domains but also robust [...] Read more.

The proliferation of digital public service platforms and the expansion of e-government initiatives have significantly increased the volume and diversity of citizen-generated feedback. This trend emphasizes the need for classification systems that are not only tailored to specific administrative domains but also robust to the linguistic, contextual, and structural variability inherent in user-submitted content. This study investigates the comparative effectiveness of large language models (LLMs) alongside instruction-tuned embedding models in the task of categorizing public transportation complaints. LLMs were tested using a few-shot inference, where classification is guided by a small set of in-context examples. Embedding models were assessed under three paradigms: label-only zero-shot classification, instruction-based classification, and supervised fine-tuning. Results indicate that fine-tuned embeddings can achieve or exceed the accuracy of LLMs, reaching up to 90 percent, while offering significant reductions in inference latency and computational overhead. E5 embeddings showed consistent generalization across unseen categories and input shifts, whereas BGE-M3 demonstrated measurable gains when adapted to task-specific distributions. Instruction-based classification produced lower accuracy for both models, highlighting the limitations of prompt conditioning in isolation. These findings position multilingual embedding models as a viable alternative to LLMs for classification at scale in data-intensive public sector environments. Full article

(This article belongs to the Special Issue Text Mining: Challenges, Algorithms, Tools and Applications)

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27 pages, 1128 KiB

Open AccessArticle

Adaptive Multi-Hop P2P Video Communication: A Super Node-Based Architecture for Conversation-Aware Streaming

by Jiajing Chen and Satoshi Fujita

Information 2025, 16(8), 643; https://doi.org/10.3390/info16080643 - 28 Jul 2025

Abstract

This paper proposes a multi-hop peer-to-peer (P2P) video streaming architecture designed to support dynamic, conversation-aware communication. The primary contribution is a decentralized system built on WebRTC that eliminates reliance on a central media server by employing super node aggregation. In this architecture, video [...] Read more.

This paper proposes a multi-hop peer-to-peer (P2P) video streaming architecture designed to support dynamic, conversation-aware communication. The primary contribution is a decentralized system built on WebRTC that eliminates reliance on a central media server by employing super node aggregation. In this architecture, video streams from multiple peer nodes are dynamically routed through a group of super nodes, enabling real-time reconfiguration of the network topology in response to conversational changes. To support this dynamic behavior, the system leverages WebRTC data channels for control signaling and overlay restructuring, allowing efficient dissemination of topology updates and coordination messages among peers. A key focus of this study is the rapid and efficient reallocation of network resources immediately following conversational events, ensuring that the streaming overlay remains aligned with ongoing interaction patterns. While the automatic detection of such events is beyond the scope of this work, we assume that external triggers are available to initiate topology updates. To validate the effectiveness of the proposed system, we construct a simulation environment using Docker containers and evaluate its streaming performance under dynamic network conditions. The results demonstrate the system’s applicability to adaptive, naturalistic communication scenarios. Finally, we discuss future directions, including the seamless integration of external trigger sources and enhanced support for flexible, context-sensitive interaction frameworks. Full article

(This article belongs to the Special Issue Second Edition of Advances in Wireless Communications Systems)

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19 pages, 2871 KiB

Open AccessArticle

Strategic Information Patterns in Advertising: A Computational Analysis of Industry-Specific Message Strategies Using the FCB Grid Framework

by Seung Chul Yoo

Information 2025, 16(8), 642; https://doi.org/10.3390/info16080642 - 28 Jul 2025

Abstract

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we [...] Read more.

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article

(This article belongs to the Special Issue AI Tools for Business and Economics)

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41 pages, 3023 KiB

Open AccessArticle

Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings

by Ahmad Mutahhar, Tariq J. S. Khanzada and Muhammad Farrukh Shahid

Information 2025, 16(8), 641; https://doi.org/10.3390/info16080641 - 28 Jul 2025

Abstract

Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by [...] Read more.

Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities. Full article

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41 pages, 2850 KiB

Open AccessArticle

DIKWP Semantic Judicial Reasoning: A Framework for Semantic Justice in AI and Law

by Yingtian Mei and Yucong Duan

Information 2025, 16(8), 640; https://doi.org/10.3390/info16080640 - 27 Jul 2025

Abstract

Semantic modeling of legal reasoning is an important research direction in the field of artificial intelligence and law (AI and law), aiming to enhance judicial transparency, fairness, and the consistency of legal applications through structured semantic representations. This paper proposes a semantic judicial [...] Read more.

Semantic modeling of legal reasoning is an important research direction in the field of artificial intelligence and law (AI and law), aiming to enhance judicial transparency, fairness, and the consistency of legal applications through structured semantic representations. This paper proposes a semantic judicial reasoning framework based on the “Data–Information–Knowledge–Wisdom–Purpose” (DIKWP) model, which transforms the conceptual expressions of traditional legal judgment into DIKWP graphs enriched with semantics. The framework integrates the objective content of legal norms with stakeholders’ subjective cognition through a DIKWP×DIKWP bidirectional mapping mechanism, achieving “semantic justice”. Specifically, we define a DIKWP-based legal knowledge representation method and design a mapping algorithm from traditional legal concepts to the DIKWP semantic structure. To validate the effectiveness of the framework, we use a real administrative law case as an example and construct DIKWP (normative content) and DIKWP (subjective cognition) graphs to model legal rules, evidence, and various perspectives. The results indicate that the intention-driven semantic transformation mechanism can harmonize legal reasoning with stakeholders’ cognitive backgrounds, thereby enhancing the interpretability and fairness of judicial interpretation. Case analysis further demonstrates that reasoning within the DIKWP semantic space can reveal underlying assumptions, bridge cognitive gaps, and promote judicial fairness by aligning legal intentions. This study provides new theoretical and methodological support for the explainable reasoning of intelligent judicial systems. Full article

(This article belongs to the Special Issue Natural Language Argumentation: Semantics, Pragmatics and Inference)

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16 pages, 1471 KiB

Open AccessArticle

Leveraging Machine Learning Techniques to Predict Cardiovascular Heart Disease

by Remzi Başar, Öznur Ocak, Alper Erturk and Marcelle de la Roche

Information 2025, 16(8), 639; https://doi.org/10.3390/info16080639 - 27 Jul 2025

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. [...] Read more.

Cardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. Implemented on the Orange data mining platform, the ANN was trained using backpropagation and validated through 10-fold cross-validation. Dimensionality reduction via principal component analysis (PCA) enhanced computational efficiency, while Shapley additive explanations (SHAP) were used to interpret model outputs. Despite achieving 83.4% accuracy and high specificity, the model exhibited poor sensitivity to disease cases, identifying only 76 of 2233 positive samples, with a Matthews correlation coefficient (MCC) of 0.058. Comparative benchmarks showed that random forest and support vector machines significantly outperformed the ANN in terms of discrimination (AUC up to 91.6%). SHAP analysis revealed serum creatinine, diabetes, and hemoglobin levels to be the dominant predictors. To address the current study’s limitations, future work will explore LIME, Grad-CAM, and ensemble techniques like XGBoost to improve interpretability and balance. This research emphasizes the importance of explainability, data representativeness, and robust evaluation in the development of clinically reliable AI tools for heart disease detection. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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27 pages, 4682 KiB

Open AccessArticle

DERIENet: A Deep Ensemble Learning Approach for High-Performance Detection of Jute Leaf Diseases

by Mst. Tanbin Yasmin Tanny, Tangina Sultana, Md. Emran Biswas, Chanchol Kumar Modok, Arjina Akter, Mohammad Shorif Uddin and Md. Delowar Hossain

Information 2025, 16(8), 638; https://doi.org/10.3390/info16080638 - 27 Jul 2025

Abstract

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability [...] Read more.

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article

(This article belongs to the Special Issue Advanced Technologies in Intelligent Detection of Biological Information)

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25 pages, 781 KiB

Open AccessArticle

Secret Cryptographic Key Sharing Through the Integer Partition Function

by Daniel Fernandes da Nobrega, Marcio Luís Munhoz Amorim, Sérgio F. Lopes, João Paulo Carmo, José A. Afonso and Mario Gazziro

Information 2025, 16(8), 637; https://doi.org/10.3390/info16080637 - 25 Jul 2025

Abstract

Secret key exchange is a necessary function for modern cryptography. The integer partition function is a mathematical function that arises from number theory. New methods for computing the integer partition function were developed and evaluated in the context of this paper, as well [...] Read more.

Secret key exchange is a necessary function for modern cryptography. The integer partition function is a mathematical function that arises from number theory. New methods for computing the integer partition function were developed and evaluated in the context of this paper, as well as new methods for using the integer partition function in a secret key exchange. The methods were categorized into single-variable and multiple-variable methods. The single-variable methods were found to be insecure. The multiple-variable methods were shown to be vulnerable to attacks that solve a linear system. These methods were implemented in microcontrollers using the C++ programming language. Experiments were conducted to evaluate the security of the developed methods in a wireless key exchange scenario. It was concluded that the security provided by the key exchange of the developed methods was low. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: “Information Processes”, 2nd Edition)

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23 pages, 2380 KiB

Open AccessArticle

DEEPEIA: Conceptualizing a Generative Deep Learning Foreign Market Recommender for SMEs

by Nuno Calheiros-Lobo, Manuel Au-Yong-Oliveira and José Vasconcelos Ferreira

Information 2025, 16(8), 636; https://doi.org/10.3390/info16080636 - 25 Jul 2025

Abstract

This study introduces the concept of DEEPEIA, a novel deep learning (DL) platform designed to recommend the optimal export market, and its ideal foreign champion, for any product or service offered by a small and medium-sized enterprise (SME). Drawing on expertise in SME [...] Read more.

This study introduces the concept of DEEPEIA, a novel deep learning (DL) platform designed to recommend the optimal export market, and its ideal foreign champion, for any product or service offered by a small and medium-sized enterprise (SME). Drawing on expertise in SME internationalization and leveraging recent advances in generative artificial intelligence (AI), this research addresses key challenges faced by SMEs in global expansion. A systematic review of existing platforms was conducted to identify current gaps and inform the conceptualization of an advanced generative DL recommender system. The Discussion section proposes the conceptual framework for such a decision optimizer within the context of contemporary technological advancements and actionable insights. The conclusion outlines future research directions, practical implementation strategies, and expected obstacles. By mapping the current landscape and presenting an original forecasting tool, this work advances the field of AI-enabled SME internationalization while still acknowledging that more empirical validation remains a necessary next step. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)

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23 pages, 1667 KiB

Open AccessReview

Review of Advances in Multiple-Resolution Modeling for Distributed Simulation

by Luis Rabelo, Mario Marin, Jaeho Kim and Gene Lee

Information 2025, 16(8), 635; https://doi.org/10.3390/info16080635 - 25 Jul 2025

Abstract

Multiple-resolution modeling (MRM) has emerged as a foundational paradigm in modern simulation, enabling the integration of models with varying levels of granularity to address complex and evolving operational demands. By supporting seamless transitions between high-resolution and low-resolution representations, MRM facilitates scalability and interoperability, [...] Read more.

Multiple-resolution modeling (MRM) has emerged as a foundational paradigm in modern simulation, enabling the integration of models with varying levels of granularity to address complex and evolving operational demands. By supporting seamless transitions between high-resolution and low-resolution representations, MRM facilitates scalability and interoperability, particularly within distributed simulation environments such as military command and control systems. This paper provides a structured review and comparative analysis of prominent MRM methodologies, including multi-resolution entities (MRE), agent-based modeling (from a federation viewpoint), hybrid frameworks, and the novel MR mode, synchronizing resolution transitions with time advancement and interaction management. Each approach is evaluated across critical dimensions such as consistency, computational efficiency, flexibility, and integration with legacy systems. Emphasis is placed on the applicability of MRM in distributed military simulations, where it enables dynamic interplay between strategic-level planning and tactical-level execution, supporting real-time decision-making, mission rehearsal, and scenario-based training. The paper also explores emerging trends involving artificial intelligence (AI) and large language models (LLMs) as enablers for adaptive resolution management and automated model interoperability. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Systems")

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17 pages, 1603 KiB

Open AccessArticle

ICT Use by Educators for Addressing Diversity

by María-Carmen Ricoy, Vanessa Fernández-Prados and Joseba Delgado-Parada

Information 2025, 16(8), 634; https://doi.org/10.3390/info16080634 - 25 Jul 2025

Abstract

Information and Communication Technologies (ICTs) are increasingly necessary in the educational context. Digital resources could support socio-educational practices to intervene with vulnerable groups, such as people with disabilities, and improve their accessibility and inclusion. This study aims to analyse educators’ perceptions of ICT [...] Read more.

Information and Communication Technologies (ICTs) are increasingly necessary in the educational context. Digital resources could support socio-educational practices to intervene with vulnerable groups, such as people with disabilities, and improve their accessibility and inclusion. This study aims to analyse educators’ perceptions of ICT resources for socio-educational intervention with people with disabilities, as well as to determine their training needs and the possibilities and risks derived from their use. A qualitative methodology has been used to analyse the content of 12 semi-structured interviews with social educators. All of them work with students with disabilities in the extracurricular field. Based on the results, the educators habitually use popular digital devices, such as computers. They regularly search for content on the internet to obtain and disseminate ideas, perceiving an adequate domain. However, there is a need for training on specific digital resources to intervene with students with disabilities. The study highlights the need to investigate the causes that may limit some ICT uses by educators and foster the design of specific training programmes to harness the potential of ICT in socio-educational intervention. Full article

(This article belongs to the Special Issue Accessibility and Inclusion in Education: Enabling Digital Technologies)

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22 pages, 3429 KiB

Open AccessArticle

Indoor Positioning and Tracking System in a Multi-Level Residential Building Using WiFi

by Elmer Magsino, Joshua Kenichi Sim, Rica Rizabel Tagabuhin and Jan Jayson Tirados

Information 2025, 16(8), 633; https://doi.org/10.3390/info16080633 - 24 Jul 2025

Abstract

The implementation of an Indoor Positioning System (IPS) in a three-storey residential building employing WiFi signals that can also be used to track indoor movements is presented in this study. The movement of inhabitants is monitored through an Android smartphone by detecting the [...] Read more.

The implementation of an Indoor Positioning System (IPS) in a three-storey residential building employing WiFi signals that can also be used to track indoor movements is presented in this study. The movement of inhabitants is monitored through an Android smartphone by detecting the Received Signal Strength Indicator (RSSI) signals from WiFi Anchor Points (APs).Indoor movement is detected through a successive estimation of a target’s multiple positions. Using the K-Nearest Neighbors (KNN) and Particle Swarm Optimization (PSO) algorithms, these RSSI measurements are trained for estimating the position of an indoor target. Additionally, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) has been integrated into the PSO method for removing RSSI-estimated position outliers of the mobile device to further improve indoor position detection and monitoring accuracy. We also employed Time Reversal Resonating Strength (TRRS) as a correlation technique as the third method of localization. Our extensive and rigorous experimentation covers the influence of various weather conditions in indoor detection. Our proposed localization methods have maximum accuracies of 92%, 80%, and 75% for TRRS, KNN, and PSO + DBSCAN, respectively. Each method also has an approximate one-meter deviation, which is a short distance from our targets. Full article

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17 pages, 977 KiB

Open AccessArticle

Evaluation of Learning-Based Models for Crop Recommendation in Smart Agriculture

by Muhammad Abu Bakr, Ahmad Jaffar Khan, Sultan Daud Khan, Mohammad Haseeb Zafar, Mohib Ullah and Habib Ullah

Information 2025, 16(8), 632; https://doi.org/10.3390/info16080632 - 24 Jul 2025

Abstract

The use of intelligent crop recommendation systems has become crucial in the era of smart agriculture to increase yield and enhance resource utilization. In this study, we compared different machine learning (ML), and deep learning (DL) models utilizing structured tabular data for crop [...] Read more.

The use of intelligent crop recommendation systems has become crucial in the era of smart agriculture to increase yield and enhance resource utilization. In this study, we compared different machine learning (ML), and deep learning (DL) models utilizing structured tabular data for crop recommendation. During our experimentation, both ML and DL models achieved decent performance. However, their architectures are not suited for setting up conversational systems. To overcome this limitation, we converted the structured tabular data to descriptive textual data and utilized it to fine-tune Large Language Models (LLMs), including BERT and GPT-2. In comprehensive experiments, we demonstrated that GPT-2 achieved a higher accuracy of 99.55% than the best-performing ML and DL models, while maintaining precision of 99.58% and recall of 99.55%. We also demonstrated that GPT-2 not only keeps up competitive accuracy but also offers natural language interaction capabilities. Due to this capability, it is a viable option to be used for real-time agricultural decision support systems. Full article

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

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23 pages, 650 KiB

Open AccessArticle

Exercise-Specific YANG Profile for AI-Assisted Network Security Labs: Bidirectional Configuration Exchange with Large Language Models

by Yuichiro Tateiwa

Information 2025, 16(8), 631; https://doi.org/10.3390/info16080631 - 24 Jul 2025

Abstract

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that [...] Read more.

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article

(This article belongs to the Special Issue AI Technology-Enhanced Learning and Teaching)

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15 pages, 2123 KiB

Open AccessArticle

Multi-Class Visual Cyberbullying Detection Using Deep Neural Networks and the CVID Dataset

by Muhammad Asad Arshed, Zunera Samreen, Arslan Ahmad, Laiba Amjad, Hasnain Muavia, Christine Dewi and Muhammad Kabir

Information 2025, 16(8), 630; https://doi.org/10.3390/info16080630 - 24 Jul 2025

Abstract

In an era where online interactions increasingly shape social dynamics, the pervasive issue of cyberbullying poses a significant threat to the well-being of individuals, particularly among vulnerable groups. Despite extensive research on text-based cyberbullying detection, the rise of visual content on social media [...] Read more.

In an era where online interactions increasingly shape social dynamics, the pervasive issue of cyberbullying poses a significant threat to the well-being of individuals, particularly among vulnerable groups. Despite extensive research on text-based cyberbullying detection, the rise of visual content on social media platforms necessitates new approaches to address cyberbullying using images. This domain has been largely overlooked. In this paper, we present a novel dataset specifically designed for the detection of visual cyberbullying, encompassing four distinct classes: abuse, curse, discourage, and threat. The initial prepared dataset (cyberbullying visual indicators dataset (CVID)) comprised 664 samples for training and validation, expanded through data augmentation techniques to ensure balanced and accurate results across all classes. We analyzed this dataset using several advanced deep learning models, including VGG16, VGG19, MobileNetV2, and Vision Transformer. The proposed model, based on DenseNet201, achieved the highest test accuracy of 99%, demonstrating its efficacy in identifying the visual cues associated with cyberbullying. To prove the proposed model’s generalizability, the 5-fold stratified K-fold was also considered, and the model achieved an average test accuracy of 99%. This work introduces a dataset and highlights the potential of leveraging deep learning models to address the multifaceted challenges of detecting cyberbullying in visual content. Full article

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

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