Intelligent Systems with Applications最新文献

{"title":"A corporate credit evaluation method considering strong feature privacy with non-private label: A vertical heterogeneous feature fusion approach","authors":"Xifeng Ning ,&nbsp;Chao Yang ,&nbsp;Hailu Sun ,&nbsp;Xinyuan Song ,&nbsp;Zifan Hu ,&nbsp;Yu Feng ,&nbsp;Jiawei Li ,&nbsp;Yifan Zhu","doi":"10.1016/j.iswa.2025.200603","DOIUrl":"10.1016/j.iswa.2025.200603","url":null,"abstract":"<div><div>In modern monitoring and operational management, whether in industrial systems, financial risk control, or infrastructure maintenance, decision-making increasingly relies on integrating heterogeneous data from multiple sources. However, due to data privacy regulations, distributed storage, communication constraints, and sensor failures, it is often difficult to centralize modeling when dealing with high-dimensional, incomplete datasets held by different institutions. Federated learning offers a privacy-preserving joint modeling solution, yet still faces challenges such as high communication overhead, low robustness to participant dropout, and risks of gradient leakage. In certain incomplete-data scenarios, not all data is private—labels such as equipment inspection results, fault reports, or corporate blacklists and whitelists published by authoritative bodies may be public—while feature data remains private and partially missing. To address this, we propose an innovative collaborative modeling framework tailored for incomplete-data monitoring and operations, in which each participant independently trains a model on its private features and exchanges only prediction results rather than gradients. Inspired by collective expert scoring, each “expert” evaluates based on its own data, then shares scores that are integrated into a comprehensive assessment. This approach offers multiple advantages: independent model training for each party, improved efficiency by migrating only prediction results, enhanced security by avoiding gradient transmission, and higher robustness since the failure of one participant does not halt others’ training. We present three variants of this prediction-result fusion method and evaluate them on representative datasets, including enterprise credit risk assessment as a case study, comparing against vertical federated logistic regression. Experimental results validate the effectiveness of the proposed approach, which can be widely applied to diverse monitoring and operational scenarios under incomplete data conditions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200603"},"PeriodicalIF":4.3,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-based fuzzy neural networks for self-supervised data annotation","authors":"Md Rakibul Islam,&nbsp;Shahina Begum,&nbsp;Mobyen Uddin Ahmed,&nbsp;Shaibal Barua","doi":"10.1016/j.iswa.2025.200610","DOIUrl":"10.1016/j.iswa.2025.200610","url":null,"abstract":"<div><div>Annotating vibration data from heavy-duty pumps in the mining industry is highly challenging because it demands domain knowledge, a complex inspection setup, and, in many cases, remains infeasible. A self-supervised data annotation (SSDA) framework is therefore proposed and evaluated on historical data of slurry-pump vibration signals. The framework began with the collection of heterogeneous information, followed by information fusion using an autoencoder. This was then followed by a datafication step for preprocessing and achieving a better representation of features through a feature embedding technique. As a result, redundant information was pushed into an eight-dimensional latent space, achieving a reconstruction loss of 0.0023. Furthermore, Initial data annotation was obtained by combining the Isolation Forest and Kneedle algorithms to locate a data-driven knee or threshold, and it was found to be 0.58 for predicting labels. Partial samples were labeled and considered accurate. Lastly, an attention-based fuzzy neural network (AFNN) is trained on those labels where membership functions convert each latent feature into graded truth values. At the same time, an attention layer highlights the most relevant rules. An iterative self-training loop was implemented to refine the training set and obtain labeled data with higher model confidence. Here, we also tested six baseline models and found AFNN quite impressive. After seven iterations 2780 of 2872 samples were labeled and the remaining 92 are considered uncertain, still need some review from an expert, and the AFNN model confidence was (96.8%). Statistical analysis confirmed that the model predictions were significantly associated with true labels (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>) and not driven by chance.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200610"},"PeriodicalIF":4.3,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing printing processes with MCTS","authors":"Kadri Kukk ,&nbsp;Ants Torim ,&nbsp;Erki Eessaar ,&nbsp;Tarmo Kadak","doi":"10.1016/j.iswa.2025.200602","DOIUrl":"10.1016/j.iswa.2025.200602","url":null,"abstract":"<div><div>The printing industry benefits from digitalizing workflows such as customer quoting. Intelligent printing process planning is essential to determine the near-optimal price for automated quoting. This paper addresses the automation of sheet imposition, a critical and computationally intensive step in optimizing the printing process that belongs to the general class of cutting and packing problems. We propose a simple recursive sheet imposition representation as the basis for our algorithms. The Brute Force algorithm for optimizing sheet imposition guarantees the cheapest solution but is computationally infeasible for complex tasks. As alternatives, we investigate heuristic algorithms, specifically Monte Carlo Tree Search (MCTS) and Simulated Annealing (SA). Our findings show that while Brute Force is prohibitively slow, MCTS strikes a robust balance between computational performance and solution quality, consistently finding solutions within a 5% margin of optimal price. Although SA can occasionally find superior solutions, MCTS provides a more reliable and efficient approach by consistently delivering results close to the optimal price.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200602"},"PeriodicalIF":4.3,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Online Grooming detection employing Context Determination and Message-Level Analysis","authors":"Jake Street,&nbsp;Isibor Kennedy Ihianle,&nbsp;Funminiyi Olajide,&nbsp;Ahmad Lotfi","doi":"10.1016/j.iswa.2025.200607","DOIUrl":"10.1016/j.iswa.2025.200607","url":null,"abstract":"<div><div>Online Grooming (OG) is a prevalent threat facing predominately children online, with groomers using deceptive methods to prey on the vulnerability of children on social media/messaging platforms. These attacks can have severe psychological and physical impacts, including a tendency towards revictimization. Current technical measures are inadequate, especially with the advent of end-to-end encryption which hampers message monitoring. Existing solutions focus on the signature analysis of child abuse media, which does not effectively address real-time OG detection. This paper proposes that OG attacks are complex, requiring the identification of specific communication patterns between adults and children alongside identifying other insights (e.g. Sexual language) to make an accurate determination. It introduces a novel approach leveraging advanced models such as BERT and RoBERTa for Message-Level Analysis and a Context Determination approach for classifying actor interactions, between adults attempting to groom children and honeypot children actors. This approach included the introduction of Actor Significance Thresholds and Message Significance Thresholds to make these determinations. The proposed method aims to enhance accuracy and robustness in detecting OG by considering the dynamic and multi-faceted nature of these attacks. Cross-dataset experiments evaluate the robustness and versatility of our approach. This paper’s contributions include improved detection methodologies and the potential for application in various scenarios, addressing gaps in current literature and practices.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200607"},"PeriodicalIF":4.3,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic SLAM: A comprehensive survey of methods and applications","authors":"Houssein Kanso ,&nbsp;Abhilasha Singh ,&nbsp;Etaf El Zarif ,&nbsp;Nooruldeen Almohammed ,&nbsp;Jinane Mounsef ,&nbsp;Noel Maalouf ,&nbsp;Bilal Arain","doi":"10.1016/j.iswa.2025.200591","DOIUrl":"10.1016/j.iswa.2025.200591","url":null,"abstract":"<div><div>This paper surveys the different approaches in semantic Simultaneous Localization and Mapping (SLAM), exploring how the incorporation of semantic information has enhanced performance in both indoor and outdoor settings, while highlighting key advancements in the field. It also identifies existing gaps and proposes potential directions for future improvements to address these issues. We provide a detailed review of the fundamentals of semantic SLAM, illustrating how incorporating semantic data enhances scene understanding and mapping accuracy. The paper presents semantic SLAM methods and core techniques that contribute to improved robustness and precision in mapping. A comprehensive overview of commonly used datasets for evaluating semantic SLAM systems is provided, along with a discussion of performance metrics used to assess their efficiency and accuracy. To demonstrate the reliability of semantic SLAM methodologies, we reproduce selected results from existing studies offering insights into the reproducibility of these approaches. The paper also addresses key challenges such as real-time processing, dynamic scene adaptation, and scalability while highlighting future research directions. Unlike prior surveys, this paper uniquely combines (i) a systematic taxonomy of semantic SLAM approaches across different sensing modalities and environments, (ii) a comparative review of datasets and evaluation metrics, and (iii) a reproducibility study of selected methods. To our knowledge, this is the first survey that integrates methods, datasets, evaluation practices, and application insights into a single comprehensive review, thereby offering a unified reference for researchers and practitioners. In conclusion, this review underscores the vital role of semantic SLAM in driving advancements in autonomous systems and intelligent navigation by analyzing recent developments, validating findings, and highlighting future research directions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200591"},"PeriodicalIF":4.3,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced radiology report: Leveraging image enhancement and multi-label transfer learning with attention-based text generation","authors":"Hilya Tsaniya ,&nbsp;Chastine Fatichah ,&nbsp;Nanik Suciati ,&nbsp;Takashi Obi ,&nbsp;Joong-sun Lee","doi":"10.1016/j.iswa.2025.200605","DOIUrl":"10.1016/j.iswa.2025.200605","url":null,"abstract":"<div><div>Current research in radiology report generation tend to overlook the utilization of abnormalities depicted in medical images. This study introduces a novel radiology report generator that integrates a multi-label learning approach for predicting abnormality tags and employs transformer models for generating reports. Additionally, the research explores contrast-based image enhancement to mitigate noise in medical images, evaluating its impact on model performance. The multi-label learning is trained on a dataset with 180 abnormality labels and the features used as initial weights for MIMIC<img>CXR, as a visual feature extractor.Imbalance handling and ensemble methods are employed to optimize multi-label model performance for abnormality tag prediction. Multi-head attention, in conjunction with GPT-2, facilitates context building for medical report generation, utilizing BERT embeddings for text feature extraction. Evaluation metrics demonstrate that the proposed model achieves superior performance in both multi-label prediction accuracy 77 % and text generation, showing an increase in similarity 28 % in average compared to the baseline model. These findings suggest that leveraging transfer learning with an ensemble classifier, combined with a transformer for context building and decoding, effectively utilizes visual and text features. Furthermore, the incorporation of image enhancement techniques significantly impacts model performance.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200605"},"PeriodicalIF":4.3,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated learning using quality-based aggregation method for brain tumour segmentation on multimodality medical images","authors":"Rim El Badaoui ,&nbsp;Ester Bonmati ,&nbsp;Vasileios Argyriou ,&nbsp;Barbara Villarini","doi":"10.1016/j.iswa.2025.200601","DOIUrl":"10.1016/j.iswa.2025.200601","url":null,"abstract":"<div><div>Deep learning for medical imaging has shown great potential in improving patient outcomes due to its high accuracy in disease diagnosis. However, a major challenge preventing the widespread adoption of such models in clinical settings is data accessibility, which conflicts with the General Data Protection Regulation (GDPR) in a traditional centralised training environment. Hence, to address this issue, Federated Learning (FL) was introduced as a decentralised alternative that enables collaborative model training among data owners without sharing any private data. Despite its significance in healthcare, limited research has explored FL for medical imaging, particularly in multimodal brain tumour segmentation, due to challenges such as data heterogeneity.</div><div>In this study, we present Federated E-CATBraTS, an advanced federated deep learning model derived from the existing E-CATBraTS framework. This model is designed to segment brain tumours from multimodal magnetic resonance imaging (MRI) while preserving data privacy. Our framework introduces a novel aggregation method, DaQAvg, which optimally combines model weights based on data size and quality, demonstrating resilience against corrupted medical images.</div><div>We evaluated the performance of Federated E-CATBraTS using two publicly available datasets: UPenn-GBM and UCSF-PDGM, including a degraded version of the latter to assess the efficacy of our aggregation method. The results indicate a 6% overall improvement over traditional centralised approaches. Furthermore, we conducted a comprehensive comparison against state-of-the-art FL aggregation algorithms, including FedAVG, FedProx and FedNova. While FedNova demonstrated the highest overall DSC, DaQAvg demonstrated superior robustness to noisy conditions, showcasing its specific advantage in maintaining performance with variable data quality, a critical aspect in medical imaging.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200601"},"PeriodicalIF":4.3,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond algorithms: Artificial intelligence driven talent identification with human insight","authors":"Tiago Jacob Fernandes França ,&nbsp;José Henrique Pereira São Mamede ,&nbsp;João Manuel Pereira Barroso ,&nbsp;Vítor Manuel Pereira Duarte dos Santos","doi":"10.1016/j.iswa.2025.200604","DOIUrl":"10.1016/j.iswa.2025.200604","url":null,"abstract":"<div><div>The rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (<em>n</em> = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200604"},"PeriodicalIF":4.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alert correlation for intelligent threat detection and response","authors":"Bronagh Lanigan ,&nbsp;Zeinab Rezaeifar ,&nbsp;Federico Cruciani ,&nbsp;Michael Milliken ,&nbsp;Jordan Vincent ,&nbsp;Samuel Moore ,&nbsp;Muhammad Aaqib ,&nbsp;Alan Mills ,&nbsp;Pushpinder K. Chouhan ,&nbsp;Alfie Beard ,&nbsp;Chris D. Nugent ,&nbsp;Luke Chen ,&nbsp;Alex Healing","doi":"10.1016/j.iswa.2025.200606","DOIUrl":"10.1016/j.iswa.2025.200606","url":null,"abstract":"<div><div>With the increasing diversity of IoT devices, keeping IT systems secure is becoming increasingly difficult. Attackers exploit vulnerabilities within the system in order to access sensitive information, typically reaching their objective through several steps. Current Intrusion Detection Systems (IDSs) focus on low-level alerts, and tend to produce a high rate of false positives. This type of information alone is insufficient for the detection of sophisticated attack scenarios such Advanced Persistent Threats (APTs). Consequently, correlation techniques have recently been introduced to correlate alerts and reconstruct attack scenarios, however, various attack scenarios exist, with diverse characteristics. Also, different steps of the APTs scenarios may have their own characteristics. Therefore, finding a proper method that covers all cases remains a challenge. Moreover, after detecting APTs, how the system should respond to these attacks to avoid sabotage to the system remains a challenge. Thus, in this paper, first for detection of the attacks, we classify different cases, and then, a method based on different characteristics of attack patterns is proposed to detect APT scenarios. The proposed method consists of two main phases: APT detection and the intelligent hybrid response framework. In APT detection phase, similar alerts are aggregated and attack graphs are generated based on a similarity matrix. These graphs, combined with third party API data enable alert correlation and APT scenario detection. Entity graphs are then created to visualise host behaviour, and alert graphs are analysed to detect APT scenarios. In the response phase, attack graphs produced from the correlation inform the hybrid response framework, integrating knowledge and data-driven components that facilitate automated or recommended mitigation. The approach was evaluated on the ZeekData24 dataset. Obtained precision and recall on the malicious traffic was observed to be 96.65% and 87.04% respectively. The results show that our approach can effectively filter false positive alerts with a reduction of the data going from 10,063 alerts daily to 586 meta-alerts, pruned to 48 attack graphs and finally reduced to 20 suspicious attack graphs.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200606"},"PeriodicalIF":4.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment analysis: From rule-based lexicons to large language models","authors":"Maikel Leon","doi":"10.1016/j.iswa.2025.200599","DOIUrl":"10.1016/j.iswa.2025.200599","url":null,"abstract":"<div><div>This study provides a comprehensive review of two decades of research in opinion mining and sentiment analysis, addressing the fragmentation of prior work across methodologies, application domains, and data sources. The evolution of the field is traced from pre-1990 rule-based systems to lexicon heuristics, statistical learning, machine learning, deep learning, and the current wave of transformer-driven, multimodal, and generative models. Applications are examined across marketing, finance, politics, and social media, with emphasis on how methodological innovations have improved accuracy and enabled broader adoption. Best practices – including transformer fine-tuning, prompt engineering, zero-shot and few-shot learning, multimodal fusion, and domain adaptation – are analyzed to distill evidence-based guidelines for researchers and practitioners. The synthesis shows how sentiment analysis has shaped critical areas, including brand management, investor decision-making, political discourse, and online user engagement. Findings highlight the effectiveness of transformer-based approaches, particularly when combined with domain adaptation and prompt engineering, in delivering state-of-the-art performance. Beyond methodological and applied insights, the study identifies promising directions for future research, including real-time customer journey analytics, explainability in generative AI, robustness across multiple languages, ethical implications, and sustainability considerations. By consolidating dispersed knowledge into a unified account, this review provides both historical grounding and a structured roadmap that advances theoretical understanding and informs managerial practice.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200599"},"PeriodicalIF":4.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}