Frontiers in Artificial Intelligence最新文献

{"title":"Machine learning approaches for phishing detection and cybersecurity awareness in digital infrastructure.","authors":"Parves Alam, Md Azizul Rahaman, Jubayer Uddin Shamim, Shakhawat Hossain Refat, Wali Ahmed, Pappu Roy, Shaon Reza, Samuel Kim","doi":"10.3389/frai.2026.1875321","DOIUrl":"https://doi.org/10.3389/frai.2026.1875321","url":null,"abstract":"<p><strong>Introduction: </strong>Phishing remains a dominant socio-technical threat to digital infrastructure, serving as an entry vector for credential-theft, ransomware, and supply-chain incidents. The machine learning literature on phishing-URL detection remains fragmented across heterogeneous datasets, inconsistent leakage controls, and explanation pipelines seldom cross-validated by independent attribution methods.</p><p><strong>Methods: </strong>An end-to-end, leakage-controlled machine learning framework was developed over a balanced corpus of 11,430 instances and 87 engineered URL-lexical, HTML-content, and external-reputation features, with all preprocessing transforms fit on the training partition only. Eleven heterogeneous classifiers were benchmarked; the best classical model (LightGBM) was repurposed as a deterministic feature extractor whose out-of-fold posterior probabilities augmented the input to a hybrid CNN-BiLSTM. Statistical significance was assessed via Wilcoxon signed-rank tests with Holm-Bonferroni correction on 10-fold cross-validation <i>F</i> ₁ scores, and two explainable-AI methods (Tree SHAP and Permutation Importance) were applied alongside ablation studies.</p><p><strong>Results: </strong>LightGBM attained <i>F</i> ₁ = 0.9699 and AUC = 0.9943, significantly outperforming every other classifier (<i>p</i> ≤ 0.002) except XGBoost (<i>p</i> = 0.375). The CNN-BiLSTM hybrid attained <i>F</i> ₁ = 0.9652 and recall = 0.9703. SHAP and Permutation Importance agreed on 11 of 15 top-ranked features, separating external-reputation indicators (Google indexing, page rank, domain age, web traffic) from URL-lexical complexity indicators (length, dots, hyphens, phishing hints).</p><p><strong>Discussion: </strong>Strict leakage control confirms that the attainable performance is intrinsically high rather than artifactual. The convergent dual-method explanations translate into six candidate cybersecurity-awareness guidelines for end-user training, connecting technical detection to the human factors that shape enterprise security posture.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1875321"},"PeriodicalIF":6.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated machine learning to foster AI development processes: an explorative study.","authors":"Anna Katharina Polzer, Stefan Thalmann","doi":"10.3389/frai.2026.1867883","DOIUrl":"https://doi.org/10.3389/frai.2026.1867883","url":null,"abstract":"<p><strong>Introduction: </strong>Communication barriers between citizen developers and AI experts are a serious challenge for AI system development in the age of No and Low Code (LCNC) AI development. Automated Machine Learning (AutoML) has emerged as a promising approach to democratize AI development by enabling non-technical stakeholders to participate in the creation of AI systems. However, its role in supporting collaboration between citizen developers and AI experts remains underexplored.</p><p><strong>Methods: </strong>This study examines AI experts' perceptions of AutoML's potential as a boundary object that facilitates communication and collaboration between citizen developers and AI experts. An exploratory qualitative interview study was conducted with 14 AI experts from various industries to examine their perceptions of how GUI-based low- or no-code AutoML prototypes may support collaboration.</p><p><strong>Results: </strong>The findings indicate that AutoML can provide a shared frame of reference that supports knowledge exchange, mutual understanding, and interdisciplinary teamwork. Interviewees highlighted that AutoML-generated insights and early-stage prototypes facilitate iterative, feedback-driven development processes and help overcome data and modeling barriers. However, the effective integration of AutoML requires appropriate governance structures, including clear responsibilities, guided processes, and structured stakeholder interaction.</p><p><strong>Discussion: </strong>This exploratory study highlights AI experts' perceptions that AutoML may serve not only as a technological tool but also as a potential facilitator of collaboration in AI development. However, democratizing AI development requires more than accessible tools alone and depends on well-managed communication and engagement processes. The study contributes to a deeper understanding of AutoML's role as a boundary object and highlights the need for further research into its responsible and inclusive deployment.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1867883"},"PeriodicalIF":6.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing multi-class intrusion detection in IIoT: advanced feature selection using grey wolf optimizer and machine learning classifiers.","authors":"Ibrahim Al-Shibly, Llorenç Burgas, Joaquim Massana","doi":"10.3389/frai.2026.1932015","DOIUrl":"https://doi.org/10.3389/frai.2026.1932015","url":null,"abstract":"<p><strong>Introduction: </strong>The rapid expansion of the Industrial Internet of Things (IIoT) has increased the cyberattack surface of industrial infrastructures, creating demand for intrusion detection systems (IDS) that combine predictive performance with deployment efficiency.</p><p><strong>Methods: </strong>This study proposes a Grey Wolf Optimizer (GWO)-guided fixed top-k feature-ranking framework for multi-class IIoT intrusion detection using candidate-dependent redundancy regularization, class-weighted learning, stratified cross-validation, repeated runs, and independent holdout evaluation. The framework was evaluated on the CIC IIoT Dataset 2025 and independently assessed on Edge-IIoTset as a secondary benchmark across six machine learning classifiers and several feature-selection baselines.</p><p><strong>Results: </strong>On CIC IIoT Dataset 2025, Random Forest and Decision Tree achieved holdout Macro-F1 scores of 0.9389 and 0.9330, respectively, using only 10 selected features, representing an 85.92% reduction from the 71-feature model-input space. Repeated training-only comparisons showed significant overall differences among GWO, BA, LightGBM, and Boruta (Friedman <i>χ</i>² = 16.920, <i>p</i> = 0.000734; Kendall's <i>W</i> = 0.564); GWO outperformed BA and LightGBM after Holm correction but did not differ significantly from Boruta. Ablation analysis further showed lower feature redundancy under adaptive regularization, although its predictive advantage was not statistically significant. Results on Edge-IIoTset showed that feature-selection effectiveness is dataset dependent.</p><p><strong>Discussion: </strong>Overall, the proposed framework provides a practical balance between predictive performance, feature compactness, and computational efficiency for resource-constrained IIoT intrusion detection.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1932015"},"PeriodicalIF":6.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feature-optimized EEG framework using multiscale entropy and metaheuristic optimization for cognitive analysis.","authors":"Keerthika N, Kiruthika V","doi":"10.3389/frai.2026.1888234","DOIUrl":"https://doi.org/10.3389/frai.2026.1888234","url":null,"abstract":"<p><strong>Background: </strong>Cognitive activity plays an important role by influencing working-memory, problem-solving, attention, response and decision making. EEG serves as a non-invasive approach to analyze neural activity associated with cognitive processing. The current study aims to identify the more contributing features to analyze the cognitive activity between sports and non-sports groups.</p><p><strong>Methods: </strong>The present study included 100 participants, consisting of 80 males and 20 females aged between 19 and 23 years. EEG signals were acquired and preprocessed using filtering methods to remove unwanted noise and artifacts. The four-entropy metrics dispersion entropy, permutation entropy, sample entropy, and fuzzy entropy were applied to the coarse-graining method across multiple temporal scales to analyze the complexity of EEG signals. To identify the most informative feature subsets and reduce redundancy, filter-based methods such as Mutual Information, Pearson Correlation, Minimum Redundancy Maximum Relevance and wrapper-based methods such as Genetic Algorithm and Particle Swarm Optimization were applied. Finally for differentiating the cognitive activity, five machine learning classifiers that include Support Vector Machine, Artificial Neural Network, <i>K</i>-nearest neighbor, Decision Tree and Random Forest were applied. Statistical methods such as <i>T</i>-test and Fleiss kappa were used to analyse the performance efficiency.</p><p><strong>Results: </strong>Feature extraction phase yielded a total of 160 multiscale entropy features. The entire features when fed into both filter-based and wrapper-based methods, Genetic Algorithm based optimization yielded a minimal contributing feature subset with high nested cross-validated accuracy of 93.0 ± 2.7% with Support Vector Machine. This contributing feature subset was used as input for five different machine learning classifiers for differentiating the cognitive activity between both groups. The five classifiers demonstrated high cross-validated agreement on held-out subjects, with a mean Fleiss' kappa of 0.874 ± 0.051 (95% CI: 0.811-0.937), depicting a good inter-classifier agreement for classification of samples into sports and non-sports individuals.</p><p><strong>Discussion: </strong>The study demonstrates that multiscale entropy features combined with metaheuristic-based feature optimization can effectively analyze the cognitive activity between sports and non-sports individuals. The proposed EEG framework provides a structured approach for real-time cognitive activity analysis using compact and informative entropy-based feature subsets.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1888234"},"PeriodicalIF":6.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-supervised Bayesian deep clustering with uncertainty-guided representation and robustness.","authors":"S Krishnamoorthy, B Jaganathan","doi":"10.3389/frai.2026.1935933","DOIUrl":"https://doi.org/10.3389/frai.2026.1935933","url":null,"abstract":"<p><strong>Introduction: </strong>In real-world data, tackling missing data, noisy observations, and uncertainty at the same time is hard since the number of labelled data is limited. Current deep clustering algorithms have considered the imputation, noise, semi-supervision, and uncertainty problems separately, such as without modelling the uncertainty in the noise, or without modelling the missingness in the missing data. There is still a need to develop an end-to-end deep clustering algorithm that incorporates all these four factors.</p><p><strong>Methods: </strong>To overcome the limitations, we propose to robust the semi-supervised Bayesian Deep Clustering (RBDC), which involves four parts, namely: The Student-t decoder does not explicitly impute for missing features and yields a robust reconstruction for heavy-tailed noise. Label-guided Gaussian mixture priors provide partial class label guidance in the learning process within the latent space, which is positioned between supervised and unsupervised learning. An uncertainty-weighted Evidence Lower Bound (ELBO) scales the contribution of each sample by the posterior uncertainty, effectively being noisy or contaminated. Sytems with Hybrid EM + SGD optimisation strategy. The mixture is trained using the hybrid EM + SGD optimisation strategies. The latent uncertainty is used in the mixture updates. The framework is tested on University of California, Irvine (UCI) Adult Income and Gym Members Fitness datasets, and the results are compared against strong deep clustering baselines.</p><p><strong>Results: </strong>Results show that RBDC outperforms baseline methods in calibration, discrimination/predictive ability and clustering quality in the low supervision regime. Ablation studies suggest the benefit of label informed priors and uncertainty weighting in enabling stable and interpretable clustering. Results also highlight the complementary benefits of semi-supervision and modelling of uncertainty.</p><p><strong>Discussion: </strong>Our results show that RBDC offers a strong and label-efficient calibration-aware framework to clustering of noisy, incomplete and partially labelled tabular data. RBDC attempts to model in a single generative framework the missingness, noise, uncertainty and weak supervision in the data, with a possible benefit to downstream applications in decision support such as risk stratification, anomaly detection and predictive modelling. Hence RBDC provides a conceptually integrated approach to semi-supervised deep clustering for challenging real-world datasets.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1935933"},"PeriodicalIF":6.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal phase coverage and patient cohort size in deep learning-based cardiac cine computed tomography angiography segmentation.","authors":"Andrew Heller, Li-Yueh Hsu, An-Yu Sun, Matthew Jacobs, Lin-Ching Chang, Marcus Y Chen","doi":"10.3389/frai.2026.1914255","DOIUrl":"https://doi.org/10.3389/frai.2026.1914255","url":null,"abstract":"<p><strong>Background: </strong>Annotating all phases of cine computed tomography angiography (CTA) is burdensome. We evaluated how temporal phase coverage and patient cohort size affect deep learning-based cardiac segmentation.</p><p><strong>Methods: </strong>Twelve 3D U-Net models were trained using a 110-patient single-phase CTA dataset or a 20-patient, 20-phase cine CTA dataset. Phase-coverage ablations used one, two, or 20 phases; patient-count ablations were performed within both datasets. One cine patient was used for validation and four independent patients were used for testing. Five cardiac structures were evaluated using Dice and the 95th-percentile Hausdorff distance (HD95). Phase-wise and patient-level Dice consistency and structure-specific volumetric variability within and between subjects were assessed.</p><p><strong>Results: </strong>Full cine training produced higher overall Dice (0.89 vs. 0.87) and lower overall HD95 (4.87 vs. 9.78 mm) than the single-phase reference (both <i>p</i> < 0.05). Right ventricular (RV) Dice was higher (0.91 vs. 0.84) and HD95 was lower (5.16 vs. 22.75 mm; both <i>p</i> < 0.05). The two-phase model achieved an overall Dice of 0.88 and an overall HD95 of 7.85 mm; neither metric differed significantly from the single-phase reference. HD95 generally increased with smaller cohorts, and both metrics deteriorated markedly in the five-patient configurations. Full cine training showed flatter phase-wise Dice profiles and lower across-phase variability. Between-subject volume differences exceeded phase-related within-subject differences for all structures (all <i>p</i> < 0.05).</p><p><strong>Conclusion: </strong>In this cross-cohort comparison, full cine training showed higher overall Dice, lower overall HD95, and greater phase-wise consistency than single-phase training. The evaluated two-phase configuration may inform annotation-efficient cine CTA dataset design, but broader validation is needed.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1914255"},"PeriodicalIF":6.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid semantic-lexical framework for ambiguity detection in software requirements using TF-IDF, character n-grams, and SBERT.","authors":"Fariha Khalid, Muhammad Yaseen, Muhammad Awais Sattar, Abdelhamid Ghoul, Aida Mustapha","doi":"10.3389/frai.2026.1901190","DOIUrl":"https://doi.org/10.3389/frai.2026.1901190","url":null,"abstract":"<p><p>Ambiguity in software requirements is a major cause of misinterpretation, design defects, rework, and project delays in software engineering. This study proposes a hybrid semantic-lexical ambiguity detection framework that integrates lexical, structural, and contextual representations for identifying ambiguous software requirement statements. Two benchmark datasets were employed, including a Software Requirements Specification (SRS) dataset and a Functional-Non-Functional Requirements (FR-NFR) dataset. Data pre-processing involved tokenisation, lemmatisation, and noise removal, while Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance within training partitions. Three modeling strategies were evaluated: baseline classifiers using TF-IDF and character n-gram features, semantic models based on Sentence-BERT (SBERT) embeddings, and a hybrid representation combining TF-IDF, character n-grams, and SBERT embeddings. Experimental results indicate that baseline models achieved high precision but comparatively lower recall, limiting their ability to identify ambiguous requirements under class imbalance. The proposed hybrid representation achieved the strongest and most balanced performance across both datasets in practical terms, particularly when combined with Support Vector Machine classification, resulting in improved recall and F1-score, although statistical testing indicated that these gains over the strongest lexical baseline were not significant at the 5% level. By jointly modeling lexical vagueness, structural ambiguity patterns, and contextual semantics, the framework captures both explicit and implicit forms of ambiguity that are frequently overlooked by single-representation approaches. To strengthen experimental validity, five-fold stratified cross-validation, transformer-based baseline comparison using fine-tuned BERT, statistical significance testing, and annotation reliability assessment using Cohen's Kappa coefficient were incorporated. The findings demonstrate that integrating lexical, structural, and semantic representations provides a robust and potentially scalable approach for ambiguity detection in software requirements engineering. The proposed framework contributes to improved requirements quality, reduced risk of misinterpretation, and more reliable automated support for requirements analysis.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1901190"},"PeriodicalIF":6.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond diagnostic accuracy: navigating the artificial intelligence data tsunami toward value-based implementation in gastroenterology.","authors":"Konstantinos Ekmektzoglou, Theodore Rokkas","doi":"10.3389/frai.2026.1943025","DOIUrl":"10.3389/frai.2026.1943025","url":null,"abstract":"<p><p>Artificial intelligence (AI) in gastrointestinal (GI) endoscopy has evolved from its early application in simple edge detection to highly sophisticated technologies designed to improve detection of neoplastic and preneoplastic lesions and support clinical decision-making. However, this rapid technological evolution has generated a \"<i>data tsunami</i>\" or \"<i>datanami</i>\": an overwhelming and continuously expanding volume of information that, frequently, exceeds the ability of clinicians and patients to process, interpret, and apply it effectively. Substantial investments in AI have been driven by expectations of improved efficiency and reduced healthcare costs. Nevertheless, the metrics used to justify the adoption of AI-assisted diagnostic and therapeutic strategies remain inadequately standardized, leaving the true cost-effectiveness of these technologies uncertain. Current AI research in gastroenterology has primarily focused on improving diagnostic performance rather than evaluating long-term clinical outcomes or economic value. To address this limitation, investigators employ stochastic microsimulation models, which simulate individual patient trajectories and enable more robust assessments of cost-effectiveness. As the field progresses from AI to extended intelligence and, ultimately, intelligent assistance, the role of humans becomes increasingly central. Rather than replacing clinical judgment, AI should augment human expertise, with healthcare professionals serving as the critical link between technological innovation and meaningful clinical benefit. This review examines whether AI applications in GI diseases can be implemented cost-effectively in routine clinical practice for diagnosis and treatment. It further explores the indispensable human dimension of AI adoption, emphasizing accountability, reliability, ethical oversight, and responsible implementation as essential safeguards for current and future clinical applications.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1943025"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-sensitive random forest with adaptive threshold optimization for imbalanced financial fraud detection.","authors":"Jafar Ali, Rahman Shafique, Daniel Gavilanes, Manuel Masias Vergara, Isabel de la Torre Diez, Dae Wan Kim","doi":"10.3389/frai.2026.1938836","DOIUrl":"10.3389/frai.2026.1938836","url":null,"abstract":"<p><strong>Background: </strong>Financial transaction fraud detection is a challenging binary classification problem characterized by severe class imbalance, rare fraudulent cases, and asymmetric misclassification costs. In real-world banking systems, failing to detect fraudulent transactions can lead to substantial financial losses, whereas false alarms mainly increase operational review costs. Therefore, fraud detection models should not be optimized only for overall accuracy, as accuracy-oriented classifiers may favor the majority non-fraud class and fail to identify minority fraud cases effectively. To address this issue, this study proposes a cost-sensitive ML framework for financial transaction fraud detection under class imbalance and asymmetric financial risk.</p><p><strong>Methods: </strong>In this study, a cost-sensitive RF framework with adaptive probability threshold optimization is proposed for binary fraud classification. Misclassification costs are incorporated during model training through class-weighted learning, allowing the classifier to assign greater importance to minority fraud cases. In addition, instead of using the default probability threshold of 0.5, the decision threshold is optimized using validation data to minimize the expected misclassification cost and improve minority-class detection. The proposed framework was evaluated on two publicly available financial fraud datasets with different transaction characteristics: the Credit Card Fraud Detection dataset and the Financial Fraud Prediction dataset. Its performance was compared with conventional ML baselines using fraud-oriented evaluation metrics, including precision, recall, F1-score, false-negative rate, false-positive rate, and expected misclassification cost.</p><p><strong>Results: </strong>The proposed framework achieved strong and consistent performance across both datasets. On the Credit Card Fraud Detection dataset, it achieved a precision of 94.4%, recall of 85.7%, and F1-score of 89.8%. On the Financial Fraud Prediction dataset, it achieved a precision of 93.1%, a recall of 83.8%, and an F1-score of 88.2%. Compared with conventional fixed-threshold ML models, the adaptive threshold strategy reduced false-negative predictions while maintaining high precision. These results indicate that the proposed cost-sensitive framework provides a more balanced decision strategy for fraud detection under highly imbalanced transaction distributions.</p><p><strong>Conclusion: </strong>The findings demonstrate that combining class-weighted learning with adaptive threshold optimization can improve fraud detection performance under asymmetric misclassification costs. The proposed framework offers a practical and interpretable decision-support approach for banking fraud detection systems, particularly in settings where reducing undetected fraudulent transactions is more important than maximizing overall accuracy alone.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1938836"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the black box in healthcare operations: a structured literature review and governance framework for explainable AI in clinical process intelligence systems.","authors":"Albert Adusei Brobbey, Narayan Bhosale, James Ackom","doi":"10.3389/frai.2026.1864565","DOIUrl":"10.3389/frai.2026.1864565","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Healthcare artificial intelligence has produced substantial diagnostic capabilities, yet clinical operations remain underserved by AI transparency research. Existing reviews of Explainable AI (XAI) in healthcare concentrate on diagnostic imaging, oncology, radiology, and clinical decision support. The governance needs of clinical process intelligence (CPI) systems, which support patient flow, resource allocation, throughput management, and care coordination, remain comparatively underdeveloped.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This original research article reports a structured literature review with systematic search and screening, followed by framework development. It characterizes healthcare XAI research across application domains and introduces the CPI-XAI Governance Framework, a theory-informed model for operationalizing algorithmic transparency as an organizational information-systems capability in clinical process intelligence environments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and CINAHL for peer-reviewed studies published from January 2015 to April 2026. Screening and extraction used transparent procedures adapted from PRISMA flow-reporting practices. Two authors independently screened records and extracted domain, XAI method, setting, evaluation, and governance variables; disagreements were resolved by author adjudication. Framework development used a documented hybrid deductive-inductive coding pipeline, source-contribution rules, negative-case review, and an auditable evidence-to-framework mapping anchored in IT Capability Theory, the DeLone-McLean IS Success Model, implementation science, sociotechnical systems theory, and health AI governance guidance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 187 included studies, 175 (93.6%) addressed diagnostic, disease-specific, imaging, or general clinical decision-support contexts, while operational and workflow-embedded AI accounted for 9 studies (4.8%). The primary result is descriptive: XAI research in healthcare remains concentrated in diagnostic domains, while explicit explainability-governance work for clinical operations is sparse within the search boundary. Five evidence gaps informed the framework: limited operational XAI evaluation standards, role-blind explanation design, weak causal and counterfactual support, poor workflow embedding, and absence of longitudinal XAI governance. A preliminary 0-15 scoring rubric and two hypothetical organizational assessments demonstrate how the framework can be applied analytically; they do not constitute external validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Healthcare XAI remains concentrated in diagnostic and clinical decision-support contexts, while explicit explainability-governance research for operational AI systems remains thinly represented. The CPI-XAI Governance Framework addresses this gap by treating explainability as an organizat","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1864565"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}