Information

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 (registering DOI) - 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 (registering DOI) - 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 (registering DOI) - 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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Graphical abstract

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 (registering DOI) - 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 (registering DOI) - 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, 3165 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 (registering DOI) - 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)

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 (registering DOI) - 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 (registering DOI) - 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, 996 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 (registering DOI) - 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)

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 (registering DOI) - 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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15 pages, 8334 KiB

Open AccessArticle

An AI Agent-Based System for Retrieving Compound Information in Traditional Chinese Medicine

by Feifan Zhao, Qianjin Li, Meng Wang and Xingchuang Xiong

Information 2025, 16(7), 543; https://doi.org/10.3390/info16070543 - 26 Jun 2025

Abstract

Traditional Chinese medicine (TCM), as a vital component of traditional healthcare systems, relies heavily on its chemical constituents, which serve as a bridge between ancient therapeutic theories and modern biomedical science. Efficient access to compound-related information is crucial for promoting the modernization and [...] Read more.

Traditional Chinese medicine (TCM), as a vital component of traditional healthcare systems, relies heavily on its chemical constituents, which serve as a bridge between ancient therapeutic theories and modern biomedical science. Efficient access to compound-related information is crucial for promoting the modernization and scientific understanding of TCM. However, existing approaches primarily rely on fragmented databases and literature-based retrieval methods, which suffer from low intelligence, poor data integration, and limited retrieval efficiency.This study presents a novel AI agent-based retrieval system tailored for compound information in TCM. The core innovation of the system lies in its hybrid retrieval-augmented generation framework, which seamlessly combines structured database queries with semantic vector retrieval. Furthermore, it integrates knowledge from three complementary sources—locally built knowledge bases, domain-specific APIs, and open web search—allowing for comprehensive coverage and adaptive handling of diverse natural language queries. Experiments conducted on a benchmark dataset of 150 compound-related queries demonstrate that the system achieves a peak accuracy of 96.67% across multiple mainstream LLMs. Ablation studies further reveal that removing either the hybrid RAG or multi-source knowledge module leads to a notable accuracy decline, while the full system outperforms typical RAG baselines by over 25%. These results confirm the effectiveness and robustness of the proposed architecture in TCM compound retrieval, and highlight the advantage of combining structured matching with dynamic knowledge access in specialized biomedical applications. Full article

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22 pages, 5753 KiB

Open AccessArticle

Leveraging Degradation Events for Enhanced Remaining Useful Life Prediction

by Zeeshan Abbas, Muhammad Sharif, Musrat Hussain, Naeem Hussain, Mehboob Hussain and Naveed Ahmad Khan

Information 2025, 16(7), 542; https://doi.org/10.3390/info16070542 - 26 Jun 2025

Abstract

The remaining useful life (RUL) of complex mechanical systems is the primary aspect of prognostics and health management, which is critical for ensuring reliability and safety. Recent developments have shifted towards a data-driven approach, emphasizing empirical insights over expert opinions. The similarity-based data-driven [...] Read more.

The remaining useful life (RUL) of complex mechanical systems is the primary aspect of prognostics and health management, which is critical for ensuring reliability and safety. Recent developments have shifted towards a data-driven approach, emphasizing empirical insights over expert opinions. The similarity-based data-driven approach operates on the premise that systems with similar historical behaviors will likely exhibit similar future behaviors, making it suitable for RUL estimation. Conventionally, most similarity-based approaches utilize all historical data to identify reference systems for RUL estimations. However, not all historical events within a system hold equal significance for RUL. Certain events have a substantial impact on the remaining lifespan of a system. These significant and impactful events are called degradation events (DEs) in this study. Based on the hypothesis that systems undergoing similar DEs may share the same RUL, this study presents an innovative framework for RUL estimation that leverages only the DEs of a test system to identify reference systems that have experienced similar DEs. Furthermore, the model incorporates novel strategies for adjusting the RUL of the reference system based on the initial wear and degradation rates, thereby improving estimation accuracy. The effectiveness of the proposed model, in comparison with similar state-of-the-art models, is demonstrated through experiments on widely recognized jet engine datasets provided by NASA and bearing degradation data from the XJTU-SY. Full article

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18 pages, 29416 KiB

Open AccessArticle

Novel Matching Algorithm for Effective Drone Detection and Identification by Radio Feature Extraction

by Teng Wu, Yan Du, Runze Mao, Hui Xie, Shengjun Wei and Changzhen Hu

Information 2025, 16(7), 541; https://doi.org/10.3390/info16070541 - 25 Jun 2025

Abstract

With the rapid advancement of drone technology, the demand for the precise detection and identification of drones has been steadily increasing. Existing detection methods, such as radio frequency (RF), radar, optical, and acoustic technologies, often fail to meet the accuracy and speed requirements [...] Read more.

With the rapid advancement of drone technology, the demand for the precise detection and identification of drones has been steadily increasing. Existing detection methods, such as radio frequency (RF), radar, optical, and acoustic technologies, often fail to meet the accuracy and speed requirements of real-world counter-drone scenarios. To address this challenge, this paper proposes a novel drone detection and identification algorithm based on transmission signal analysis. The proposed algorithm introduces an innovative feature extraction method that enhances signal analysis by extracting key characteristics from the signals, including bandwidth, power, duration, and interval time. Furthermore, we developed a signal processing algorithm that achieves efficient and accurate drone identification through bandwidth filtering and the matching of duration and interval time sequences. The effectiveness of the proposed approach is validated using the DroneRF820 dataset, which is specifically designed for drone identification and counter-drone applications. The experimental results demonstrate that the proposed method enables highly accurate and rapid drone detection. Full article

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29 pages, 1682 KiB

Open AccessReview

The Impact of AI-Driven Application Programming Interfaces (APIs) on Educational Information Management

by David Pérez-Jorge, Miriam Catalina González-Afonso, Anthea Gara Santos-Álvarez, Zeus Plasencia-Carballo and Carmen de los Ángeles Perdomo-López

Information 2025, 16(7), 540; https://doi.org/10.3390/info16070540 - 25 Jun 2025

Abstract

In today’s digitalized educational landscape, the intelligent use of information is essential for personalizing learning, improving assessment accuracy, and supporting data-driven pedagogical decisions. This systematic review examines the integration of Application Programming Interfaces (APIs) powered by Artificial Intelligence (AI) to enhance educational information [...] Read more.

In today’s digitalized educational landscape, the intelligent use of information is essential for personalizing learning, improving assessment accuracy, and supporting data-driven pedagogical decisions. This systematic review examines the integration of Application Programming Interfaces (APIs) powered by Artificial Intelligence (AI) to enhance educational information management and learning processes. A total of 27 peer-reviewed studies published between 2013 and 2025 were analyzed. First, a general description of the selected works was provided, followed by a breakdown by dimensions in order to identify recurring patterns, stated interests and gaps in the current scientific literature on the use of AI-driven APIs in Education. The findings highlight five main benefits: data interoperability, personalized learning, automated feedback, real-time student monitoring, and predictive performance analytics. All studies addressed personalization, 74.1% focused on platform integration, and 37% examined automated feedback. Reported outcomes include improvements in engagement (63%), comprehension (55.6%), and academic achievement (48.1%). However, the review also identifies concerns about privacy, algorithmic bias, and limited methodological rigor in existing research. The study concludes with a conceptual model that synthesizes these findings from pedagogical, technological, and ethical perspectives, providing guidance for more adaptive, inclusive, and responsible uses of AI in education. Full article

(This article belongs to the Special Issue New Information Communication Technologies in the Digital Era)

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28 pages, 13294 KiB

Open AccessArticle

Detection of Wild Mushrooms Using Machine Learning and Computer Vision

by Christos Chaschatzis, Chrysoula Karaiskou, Chryssanthi Iakovidou, Panagiotis Radoglou-Grammatikis, Stamatia Bibi, Sotirios K. Goudos and Panagiotis G. Sarigiannidis

Information 2025, 16(7), 539; https://doi.org/10.3390/info16070539 - 25 Jun 2025

Abstract

The increasing global demand for sustainable and high-quality agricultural products has driven interest in precision agriculture technologies. This study presents a novel approach to wild mushroom detection, particularly focusing on Macrolepiota procera as a focal species for demonstration and benchmarking. The proposed approach [...] Read more.

The increasing global demand for sustainable and high-quality agricultural products has driven interest in precision agriculture technologies. This study presents a novel approach to wild mushroom detection, particularly focusing on Macrolepiota procera as a focal species for demonstration and benchmarking. The proposed approach utilises unmanned aerial vehicles (UAVs) equipped with multispectral imaging and the YOLOv5 object detection algorithm. A custom dataset, the wild mushroom detection dataset (WOES), comprising 907 annotated aerial and ground images, was developed to support model training and evaluation. Our method integrates low-cost hardware with advanced deep learning and vegetation index analysis (NDRE) to enable real-time identification of mushrooms in forested environments. The proposed system achieved an identification accuracy exceeding 90% and completed detection tasks within 30 min per field survey. Although the dataset is geographically limited to Western Macedonia, Greece, and focused primarily on a morphologically distinctive species, the methodology is designed to be extendable to other wild mushroom types. This work contributes a replicable framework for scalable, cost-effective mushroom monitoring in ecological and agricultural applications. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

21 pages, 540 KiB

Open AccessArticle

The Effect of Organizational Factors on the Mitigation of Information Security Insider Threats

by Nader Sohrabi Safa and Hossein Abroshan

Information 2025, 16(7), 538; https://doi.org/10.3390/info16070538 - 25 Jun 2025

Abstract

Insider threats pose significant challenges to organizations, seriously endangering information security and privacy protection. These threats arise when employees with legitimate access to systems and databases misuse their privileges. Such individuals may alter, delete, or insert data into datasets, sell customer or client [...] Read more.

Insider threats pose significant challenges to organizations, seriously endangering information security and privacy protection. These threats arise when employees with legitimate access to systems and databases misuse their privileges. Such individuals may alter, delete, or insert data into datasets, sell customer or client email addresses, leak strategic company plans, or transfer industrial and intellectual property information. These actions can severely damage a company’s reputation, result in revenue losses and loss of competitive advantage, and, in extreme cases, lead to bankruptcy. This study presents a novel solution that examines how organizational factors such as job satisfaction and security, organizational support, attachment, commitment, involvement in information security, and organizational norms influence employees’ attitudes and intentions, thereby mitigating insider threats. A key strength of this research is its integration of two foundational theories: the Social Bond Theory (SBT) and the Theory of Planned Behavior (TPB). The results reveal that job satisfaction and security, affective and normative commitment, information security training, and personal norms all contribute to reducing insider threats. Furthermore, the findings indicate that employees’ attitudes, perceived behavioral control, and subjective norms significantly influence their intentions to mitigate insider threats. However, organizational support and continuance commitment were not found to have a significant impact. Full article

(This article belongs to the Special Issue Human Factors in Cyber Security: Social Engineering, Behavioural Aspects and Resilience Solutions)

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29 pages, 1205 KiB

Open AccessArticle

A Comprehensive Evaluation of Machine Learning and Deep Learning Models for Churn Prediction

by Nabil M. AbdelAziz, Mostafa Bekheet, Ahmad Salah, Nissreen El-Saber and Wafaa T. AbdelMoneim

Information 2025, 16(7), 537; https://doi.org/10.3390/info16070537 - 25 Jun 2025

Abstract

Churn prediction has become one of the core concepts in customer relationship management within the insurances, telecom, and internet service provider industries, which is essential in customer retention. Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep [...] Read more.

Churn prediction has become one of the core concepts in customer relationship management within the insurances, telecom, and internet service provider industries, which is essential in customer retention. Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep learning models for churn prediction in the evaluation of the models’ performance across different sectors. This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. The work includes three datasets: namely, insurance churn, internet service provider customer churn, and Telecom churn datasets. The implementation and comparison conducted in this study of models include XGBoost, Convolutional Neural Networks (CNNs), and Ensemble Deep Learning with the pre-trained hybrid approach. The results show that the ensemble deep learning model outperforms other models in terms of accuracy and F1-score, achieving accuracies of up to 95.96% in the insurance churn dataset and of 98.42% in the telecom churn dataset. Moreover, traditional machine learning models like XGBoost also produced competitive results for selected datasets. The proposed deep learning ensembles reveal the strength and possibility for churn prediction and provide a benchmark for future research relevant to customer retention strategies. Also, the proposed ensemble deep learning model shows stable performance across different sectors, which reflects its ability to capture the varied churn patterns of different sectors. Full article

(This article belongs to the Section Information Processes)

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