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Cited by 3Text and Document Classification TechnologiesFuzzy Logic and Control SystemsSoftware System Performance and Reliability

A Fuzzy Logic Framework for Text-Based Incident Prioritization: Mathematical Modeling and Case Study Evaluation

Open access
Peralta, Arturo, Olivas, José A., Navarro-Illana, Pedro·2025·Mathematics
DOI 10.3390/math13122014
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Abstract

Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle to interpret the linguistic uncertainty inherent in natural language reports. This paper proposes a fuzzy logic-based framework for incident categorization and prioritization, integrating natural language processing (NLP) with a formal system of fuzzy inference. The framework transforms semantic embeddings from incident reports into fuzzy sets, allowing incident severity and urgency to be represented as degrees of membership in multiple categories. A mathematical model based on Mamdani-type inference and triangular membership functions is developed to capture and process imprecise inputs. The proposed system is evaluated on a real-world dataset comprising 10,000 incident descriptions from a mid-sized technology enterprise. A comparative evaluation is conducted against two baseline models: a fine-tuned BERT classifier and a traditional support vector machine (SVM). Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%) for medium-severity incidents, where linguistic ambiguity is most prevalent. Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment. These findings suggest that fuzzy modeling offers a mathematically sound and operationally effective solution for managing uncertainty in text-based incident management, contributing to the broader understanding of mathematical modeling in enterprise-scale social phenomena.

AI snapshot

Written from the paper’s full text.

TL;DR

The paper proposes a Mamdani-type fuzzy logic framework that maps BERT-derived semantic embeddings of incident reports into fuzzy severity and urgency inputs for text-based incident prioritization. On a 10,000-report enterprise dataset, the fuzzy model reports higher F1 than fine-tuned BERT and SVM baselines, especially for ambiguous medium-severity incidents, while also being more interpretable and computationally lightweight.

Key findings

The fuzzy model achieved precision 91.8%, recall 92.3%, F1-score 92.1%, and accuracy 91.5%; the fine-tuned BERT baseline reached F1 85.7% and the SVM baseline 79.6%. The paper reports a 7.4 percentage-point F1 improvement over BERT and a 12.5-point improvement over SVM for medium-severity incidents, where linguistic ambiguity is most prevalent. Class-wise F1 was 90.2% for Low Priority, 92.1% for Medium Priority, 93.3% for High Priority, and 91.8% for Critical Priority. Expert assessment found 91% agreement with model outputs and an average interpretability score of 4.6/5; inference time was <10 ms per incident versus 120 ms for BERT and <5 ms for SVM.

Methods

The study used 10,000 incident descriptions from a mid-sized technology enterprise, with 2,000 manually annotated severity/urgency labels; inter-annotator agreement was Cohen’s Kappa 0.82. Text was preprocessed, represented with TF-IDF and 768-dimensional BERT embeddings, reduced via PCA to 50 components preserving 95% variance, and mapped to severity and urgency axes (r = 0.71 and r = 0.65 with labels). The fuzzy system used Mamdani inference, triangular membership functions, expert-elicited rules, and centroid defuzzification; it was compared against a fine-tuned BERT classifier and a TF-IDF SVM using precision, recall, F1, accuracy, inference time, robustness tests, and expert qualitative review.

Limitations

The paper states that the dataset comes from one mid-sized technology enterprise and may not generalize across finance, healthcare, or manufacturing; it also notes English-only inputs, a small annotated set of 2,000 samples, no explicit ablation isolating fuzzy inference from embedding quality, a static expert-defined rule base, reliance on linear PCA, and missing operational metadata such as incident duration or number of affected users. It further acknowledges adjacent-priority confusion, potential bias inherited from historical labels, and difficulty with very sparse or domain-specific incident descriptions. Evident from the text: the urgency variable is defined with three linguistic terms, but the rule base is described as 16 combinations from 4 severity terms × 4 urgency terms, an internal inconsistency.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/math13122014

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 222 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Incidents sharing similar linguistic structures tended to map to similar severity levels, even when explicit severity labels were absent.

    “Incidents sharing similar linguistic structures tended to map to similar severity levels, even when explicit severity labels were absent.”
    Quote in the paperSubstantial claim
  • Findingown data

    The proposed fuzzy logic approach achieves a 12.5% improvement in F1-score over a traditional SVM model (92.1% vs. 79.6%) for medium-severity incidents.

    “Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%)for medium-severity incidents, where linguistic ambiguity is most prevalent.”
    • Also stated in: § 1. Introduction · boosted
      “The experimental results demonstrate that the proposed fuzzy logic approach outperforms state-of-the-art BERT-based classifiers by 7.4% and traditional SVM models by 12.5% in F1-score for medium-severity incidents, where ambiguity is most prevalent.”
    Quote in the paperSubstantial claim
  • Findingreasoning

    The raw incident dataset exhibited a high degree of linguistic variability, including spelling errors, shorthand notations, inconsistent phrasing, and domain-specific terminology.

    “the raw dataset exhibited a high degree of linguistic variability, including spelling errors, shorthand notations, inconsistent phrasing, and domain-specific terminology.”
    Quote in the paperSubstantial claim
  • Findingown data

    The proposed fuzzy logic approach achieves a 7.4% improvement in F1-score over a fine-tuned BERT classifier (92.1% vs. 85.7%) for medium-severity incidents.

    “Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%)for medium-severity incidents, where linguistic ambiguity is most prevalent.”
    • Also stated in: § 1. Introduction · boosted
      “The experimental results demonstrate that the proposed fuzzy logic approach outperforms state-of-the-art BERT-based classifiers by 7.4% and traditional SVM models by 12.5% in F1-score for medium-severity incidents, where ambiguity is most prevalent.”
    Quote in the paperSubstantial claim
  • Findingown data

    Evaluators confirmed a 93% success rate in recovering grammatically coherent and semantically accurate texts after preprocessing.

    “the evaluators confirmed a 93% success rate in recovering grammatically coherent and semantically accurate texts after preprocessing.”
    Quote in the paperSubstantial claim
  • May combine two claims. 74
  • Classified as probably not a substantial claim. 64
  • The quote only partly states this claim. 7

  • May combine two claims.

    Findingown data

    The fuzzy model improved operator trust and alignment with human judgment, according to qualitative analysis by domain experts.

    “Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment.”
    • Also stated in: § 1. Introduction · plain
      “Moreover, expert evaluations confirm that the fuzzy model's decisions are more interpretable and align better with human reasoning compared to black-box machine learning methods.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Domain experts' qualitative assessments are valid and reliable indicators of interpretability and alignment with human judgment.

  • May combine two claims.

    Findingown data

    In the dataset, low-severity incidents accounted for approximately 36%, medium-severity 28%, high-severity 20%, and critical-severity 16%.

    “Low-severity incidents (e.g., minor delays and performance degradations) accounted for approximately 36% of the dataset, while mediumseverity incidents (intermittent failures) comprised 28%, high-severity incidents (system outages) comprised 20%, and critical-severity incidents (security breaches, total failures) comprised 16%.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Deduplication removed 4.7% of the dataset entries.

    “removing 4.7% of the dataset entries.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    Spell-checking corrected spelling mistakes in approximately 12% of the incident reports.

    “This step corrected spelling mistakes present in approximately 12% of the reports [11].”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    The fuzzy system provided confidence scores for each prediction, including an assessment of how strongly the incident description matched each severity level.

    “Beyond classification accuracy, the fuzzy system provided confidence scores for each prediction.Thanks to the nature of fuzzy membership functions, every prioritization decision included an assessment of how strongly the incident description matched each severity level.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation

    BERT models showed some resilience to linguistic drift, but their fine-tuned layers degraded noticeably after several months without retraining.

    “BERT models, benefiting from pre-training on massive corpora, showed some resilience to this effect, but their fine-tuned layers still degraded noticeably after several months without retraining [29].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Findingown data

    Reports using vague expressions like 'system weirdness' or 'things feel off' were sometimes misclassified as low priority by the fuzzy rules, whereas the BERT baseline inferred urgency based on prior patterns.

    “For example, reports using vague expressions like”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Triangular functions offer faster evaluation and simpler parameterization compared to trapezoidal or Gaussian alternatives, with negligible loss in accuracy observed during preliminary tests.

    “This choice also aligns with the operational need for real-time processing, as triangular functions offer faster evaluation and simpler parameterization compared to trapezoidal or Gaussian alternatives, with negligible loss in accuracy observed during preliminary tests.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The Medium Priority category showed a maximum F1 deviation of 1.6%, while Low and Critical categories varied by less than 1%.

    “The Medium Priority category, which is most sensitive to overlap, showed a maximum F1 deviation of 1.6%, while Low and Critical categories varied by less than 1%.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The fuzzy logic model achieved the highest F1-score (92.1%), outperforming the fine-tuned BERT model by 7.4 percentage points and the SVM model by 12.5 points.

    “The fuzzy logic model achieved the highest F1-score (92.1%), outperforming the finetuned BERT model by 7.4 percentage points and the SVM model by 12.5 points.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Incident reports are written in English.

  • May combine two claims.

    Findingown data

    The fuzzy system maintained low inference times (<10 milliseconds) comparable to the lightweight SVM, yet delivered markedly superior predictive accuracy.

    “In contrast, the fuzzy system maintained low inference times (<10 milliseconds) comparable to the lightweight SVM, yet delivered markedly superior predictive accuracy.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The SVM model's best results were obtained using a linear kernel and C = 1.

    “with the best results obtained using a linear kernel and C = 1.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    Under adversarial perturbations, the fuzzy model's F1-score dropped by only 2.5%, outperforming the SVM baseline and remaining competitive with the fine-tuned BERT model.

    “Under these adversarial conditions, the fuzzy model's F1-score dropped by only 2.5%, significantly outperforming the SVM baseline and remaining competitive with the fine-tuned BERT model [6,28,29].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [28]

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    • cites [29]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Findingcitation

    In a manual review of 500 incident reports, over 90% of cases with high normalized severity scores corresponded to genuinely severe disruptions, while low-severity scores matched minor issues.

    “a random sample of 500 incident reports was manually reviewed.In over 90% of cases, incidents with high normalized severity scores indeed corresponded to genuinely severe disruptions (e.g., full outages and security breaches), while low-severity scores matched minor issues (e.g., interface lags and customer inquiries) [6,20,28].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [20]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [28]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • The authors state this as an assumption: Incident reports are written in English.

  • May combine two claims.

    Findingown data

    The fuzzy model achieved precision 91.8%, recall 92.3%, F1-score 92.1%, and overall accuracy 91.5% on the held-out test set.

    “Results demonstrated that the fuzzy model achieved strong predictive performance, obtaining a precision of 91.8%, a recall of 92.3%, an F1-score of 92.1%, and an overall accuracy of 91.5%.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Incident reports are written in English.

  • May combine two claims.

    Findingown data

    The fuzzy logic framework achieved the highest F1-score and overall performance.

    “The fuzzy logic framework achieved the highest F1-score and overall performance.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    All predictions were delivered in real time by the fuzzy model, maintaining inference times below 10 milliseconds per incident even under concurrent load simulations.

    “From a computational standpoint, all predictions were delivered in real time by the fuzzy model, maintaining inference times below 10 milliseconds per incident even under concurrent load simulations.”
    Quote in the paperSubstantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    For Case 1, both BERT and SVM classified the incident as Low Priority.

    “BERT classifier output: Low Priority.”
    • Also stated in: § Case 1: Vague Language-"Login issues reported by multiple users intermittently." · plain
      “SVM classifier output: Low Priority.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing), S4 (based). May combine two claims.

    Findingown data

    For Case 3, BERT and SVM output Low Priority.

    “BERT classifier output: Low Priority.”
    • Also stated in: § Case 1: Vague Language-"Login issues reported by multiple users intermittently." · plain
      “SVM classifier output: Low Priority.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The study observed confusion between adjacent priority levels.

    “One particular limitation worth exploring is the observed confusion between adjacent priority levels.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Agreement rates between expert judgment and the model's outputs reached 91%, with a standard deviation of 4.2% across experts.

    “Agreement rates between expert judgment and the model's outputs reached 91%, with a standard deviation of 4.2% across experts”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    The model's robustness to imperfectly phrased or incomplete incident descriptions reassured experts about its reliability under real operational pressures.

    “Furthermore, the model's robustness to imperfectly phrased or incomplete incident descriptions reassured experts about its reliability under real operational pressures”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [28]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • The quote only partly states this claim.

    Findingown data

    The fuzzy logic model achieved an average interpretability score of 4.6 on a 5-point Likert scale.

    “The fuzzy logic model achieved an average interpretability score of 4.6”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    For Case 2, BERT and SVM output Medium Priority.

    “BERT classifier output: Medium Priority.”
    • Also stated in: § Case 2: Conflicting Signals-"Minor UI glitch causing critical payment delays for clients." · plain
      “SVM classifier output: Medium Priority.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Adversarial tests demonstrated that the fuzzy model maintained its performance with less than 2.5% degradation, while the BERT model exhibited about 4% degradation and the SVM about 7%.

    “Adversarial tests demonstrated that the fuzzy model maintained its performance with less than 2.5% degradation, while the BERT model exhibited about 4% degradation and the SVM about 7%.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Findingown data

    Several participants noted that even when they disagreed slightly with the final priority assigned, seeing how the system had weighted different aspects of the input made them more inclined to accept its recommendation or at least consider it seriously.

    “Several participants noted that even when they disagreed slightly with the final priority assigned, the fact that they could see how the system had weighted different aspects of the input made them more inclined to accept its recommendation or at least consider it seriously.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    The SVM baseline showed clear limitations in both predictive accuracy and robustness.

    “The SVM baseline, while computationally efficient, showed clear limitations in both predictive accuracy and robustness.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    A small subset of experts expressed a desire for even finer-grained explanations in borderline cases.

    “A small subset of experts expressed a desire for even finer-grained explanations in borderline cases”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    The BERT model requires periodic retraining to adapt to new linguistic patterns, while the fuzzy logic model showed remarkable stability over time.

    “While the BERT model requires periodic retraining to adapt to new linguistic patterns in incident descriptions, the fuzzy logic model, being rule-based, showed remarkable stability over time.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Findingcitation

    In high-pressure environments, being alerted to uncertainty rather than being presented with a false sense of certainty is a critical operational advantage.

    “They emphasized that in high-pressure environments, being alerted to uncertainty-rather than being presented with a false sense of certainty-is a critical operational advantage”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [29]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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    • cites [38]

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  • May combine two claims.

    Findingown data

    The fuzzy model remained stable, cautious, and interpretable in the case studies.

    “In contrast, the fuzzy model remained stable, cautious, and interpretable, all desirable characteristics for systems supporting critical operational decisions.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation

    Experts favored the fuzzy model's ability to expose partial certainty and found the membership distributions especially helpful in guiding their own final judgments.

    “Experts favored the fuzzy model's ability to expose partial certainty and found the membership distributions especially helpful in guiding their own final judgments.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    Inter-annotator agreement for manual severity and urgency labels was measured using Cohen's Kappa and yielded 0.82, indicating strong agreement.

    “To assess consistency, inter-annotator agreement was measured using Cohen's Kappa, yielding a value of 0.82, which indicates strong agreement.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Access to raw data was restricted to authorized personnel.

    “access to raw data was restricted to authorized personnel”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Personally identifiable information was removed or masked from all reports.

    “Personally identifiable information (PII), such as names, emails, IP addresses, and phone numbers, was removed or masked.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Fuzzy inference systems based on Mamdani-type reasoning and triangular membership functions have demonstrated success in various domains requiring decision-making under uncertainty.

    “Fuzzy inference systems, particularly those based on Mamdani-type reasoning and triangular membership functions, have demonstrated success in various domains requiring decision-making under uncertainty [23,24].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [23]

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    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Premisecitation

    Fuzzy logic allows degrees of membership across multiple categories, enabling a more nuanced and human-like representation of severity, urgency, and priority in incident reports.

    “fuzzy logic allows degrees of membership across multiple categories, enabling a more nuanced and human-like representation of severity, urgency, and priority in incident reports [21,22].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [21]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [22]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationown data

    The dataset from a mid-sized technology enterprise may not fully capture the diversity of incident types found in other industries such as finance, healthcare, or manufacturing.

    “While this setting offers a realistic and operationally complex environment for evaluation, we acknowledge that it may not fully capture the diversity of incident types found in other industries, such as finance, healthcare, or manufacturing.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    The proposed hybrid framework incorporating fuzzy logic and reinforcement learning (fuzzy Q-learning) allows the system to evolve and adapt to organizational needs over time.

    “This combination allows the system to evolve and adapt to organizational needs over time, which was not addressed in the previous work.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    All incident reports were anonymized following GDPR principles to ensure ethical compliance and data privacy.

    “To ensure ethical compliance and data privacy, all reports were anonymized following strict General Data Protection Regulation (GDPR) principles.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    Integrating fuzzy logic with semantic-rich NLP embeddings offers a promising pathway to bridge the gap between advanced text understanding and operational decision support in incident management.

    “Integrating fuzzy logic with semantic-rich NLP embeddings offers a promising pathway to bridge the gap between advanced text understanding and operational decision support in incident management.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Disagreements in annotation were resolved through consensus discussions with a third expert.

    “Disagreements were resolved through consensus discussions with a third expert.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationcitation

    The validation step was critical to ensure that the downstream fuzzy logic model would operate on high-quality, information-rich inputs, minimizing the potential propagation of preprocessing artifacts into the prioritization decisions.

    “This validation step was critical to ensure that the downstream fuzzy logic model would operate on high-quality, information-rich inputs, minimizing the potential propagation of preprocessing artifacts into the prioritization decisions [3,21].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [3]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [21]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Biased or poorly sanitized data could compromise both the performance and fairness of intelligent prioritization systems.

    “biased or poorly sanitized data could compromise both the performance and fairness of intelligent prioritization systems [9].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    TF-IDF vectors offered a sparse yet interpretable feature space, useful for baseline comparison purposes.

    “TF-IDF vectors offered a sparse yet interpretable feature space, useful for baseline comparison purposes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [16]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    Transformer models' decision-making process remains largely opaque, complicating interpretability and trust in enterprise-critical operations.

    “their decision-making process remains largely opaque, complicating the interpretability and trust required in enterprisecritical operations [13].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [13]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • The quote only partly states this claim.

    Study design and conductnone given

    The manual severity and urgency labels were annotated independently by two domain experts.

    “The annotation was performed independently by two domain experts.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    The proposed framework is empirically validated and firmly grounded in formal mathematical principles.

    “we ensure that the proposed framework is not only empirically validated but also firmly grounded in formal mathematical principles”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [3]

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    • cites [4]

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    • cites [16]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • Rests on an assumption: Natural language incident reports contain inherent uncertainty.
    • Rests on an assumption: The internal incident management system of a mid-sized technology enterprise provides a realistic and operationally complex environment for evaluating incident prioritization.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The study protocol was approved by the relevant Institutional Review Board (IRB).

    “the study protocol was approved by the relevant Institutional Review Board (IRB) [7,8].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [7]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [8]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Interpretationown data

    Many linguistic patterns and ambiguity issues observed in the dataset are common across sectors, supporting the potential applicability of the proposed framework.

    “many linguistic patterns and ambiguity issues observed in the dataset (e.g., vague severity descriptions, incomplete phrasing, and inconsistent terminology) are common across sectors, supporting the potential applicability of the proposed framework.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Duplicate reports could bias models by artificially inflating certain classes.

    “Duplicate reports, often arising from repeated user submissions or automated alerts, could have otherwise biased the models by artificially inflating certain classes [15].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [15]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Balancing computational tractability, ethical compliance, and semantic fidelity was essential to support the mathematical soundness and operational reliability of the fuzzy logic framework.

    “Balancing computational tractability, ethical compliance, and semantic fidelity was essential to support the mathematical soundness and operational reliability of the fuzzy logic framework proposed in this study [5,6,9,21].”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [5]

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    • cites [6]

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    • cites [9]

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    • cites [21]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationown data

    Semantic features can support robust, nuanced severity assessment, justifying their integration into the fuzzy inference process.

    “This observation suggested that semantic features could indeed support robust, nuanced severity assessment, justifying their integration into the fuzzy inference process.”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Interpretationreasoning

    The fuzzy logic framework offers a clear advantage in interpretability over the BERT classifier.

    “The fuzzy logic framework offers a clear advantage in interpretability.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    The PCA plus feature selection process allows easy recalibration if incident language evolves over time, improving long-term robustness.

    “Adaptability: The PCA + feature selection process allows easy recalibration if incident language evolves over time, improving the system's long-term robustness [19,38].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [19]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    By integrating semantic embeddings with fuzzy inference, the system is capable of mimicking expert reasoning patterns while remaining transparent and auditable.

    “By integrating semantic embeddings with fuzzy inference, the system is capable of mimicking expert reasoning patterns while remaining transparent and auditable-essential characteristics for adoption in real-world enterprise contexts [27,28,38].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [27]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [28]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    The fuzzy model is lightweight and suitable for real-time processing even on resource-constrained systems.

    “From a computational perspective, the fuzzy model is lightweight and suitable for real-time processing even on resource-constrained systems, a crucial factor in enterprise environments with thousands of daily incident reports [5,32].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [5]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims. The quote only partly states this claim.

    Study design and conductown data

    A sensitivity analysis was conducted by perturbing the triangular boundaries ±10% around the original quantile-based definitions, and F1-score was recomputed on the test set for each perturbed configuration.

    “To evaluate the robustness of the fuzzy model to variations in membership function parameters, we conducted a sensitivity analysis by perturbing the triangular boundaries ± 10% around the original quantile-based definitions.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    The BERT classifier, while highly accurate in well-defined cases, lacks inherent explainability and requires external techniques for post hoc interpretations.

    “In contrast, the BERT classifier, while highly accurate in well-defined cases, lacks inherent explainability and requires external techniques (e.g., SHAP values or attention visualization) to provide post hoc interpretations, which may not always be reliable [13,31].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [13]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [31]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    Dense embeddings and principal component-based mappings are computationally efficient, enabling real-time incident processing in large-scale enterprise environments.

    “Scalability: Dense embeddings and principal component-based mappings are computationally efficient, enabling real-time incident processing even in large-scale enterprise environments [32].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    Rules with consensus scores above 4 were retained without changes, while others were refined or discarded.

    “Rules with consensus scores above 4 were retained without changes, while others were refined or discarded.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The fuzzy logic framework delivers high predictive accuracy across all priority levels and generalizes effectively across different types of service disruptions.

    “First, it delivers high predictive accuracy across all priority levels, maintaining strong performance even when faced with input uncertainty.The system consistently prioritized incidents correctly, demonstrating its capability to generalize effectively across different types of service disruptions.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Transformer-based models are typically trained to optimize overall accuracy and may neglect ambiguous or borderline cases where fuzzy logic systems would provide more nuanced prioritization.

    “Lastly, transformer-based models are typically trained to optimize overall accuracy and may neglect ambiguous or borderline cases where fuzzy logic systems would provide more nuanced prioritization [23].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    Clipping outliers prevented extreme values from dominating the fuzzy inference process and maintained system stability under unusual inputs.

    “This prevented extreme values from dominating the fuzzy inference process, maintaining system stability under unusual inputs [24,33].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [33]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    The Mamdani-type fuzzy inference structure ensures mathematical consistency, allowing for a formal analysis of system behavior under different input scenarios.

    “It ensures mathematical consistency, allowing for a formal analysis of system behavior under different input scenarios [23,24].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    The proposed fuzzy framework can be seamlessly integrated into real-time enterprise ticketing systems by exposing the inference engine as a lightweight REST API or microservice.

    “Moreover, the proposed fuzzy framework can be seamlessly integrated into real-time enterprise ticketing systems by exposing the inference engine as a lightweight REST API or microservice.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    The full rule base contained 16 combinations (4 severity terms × 4 urgency terms) and was constructed in collaboration with domain experts to ensure operational realism.

    “The full rule base contained 16 combinations (4 severity terms × 4 urgency terms) and was constructed in collaboration with domain experts to ensure operational realism [27,38].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [27]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    The triangular membership functions ensure that the system remains computationally efficient, supporting real-time prioritization in enterprise environments with high incident volumes.

    “Additionally, the triangular membership functions ensure that the system remains computationally efficient, supporting real-time prioritization in enterprise environments with high incident volumes [32,38].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • The quote only partly states this claim.

    Interpretationcitation

    The mapping of incidents containing words like 'security breach' or 'massive outage' to the highest severity and urgency categories ensured that critical incidents were promptly escalated.

    “ensuring that critical incidents were promptly escalated [6,19,28].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [19]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [28]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationcitation

    More complex fuzzy models could offer marginal accuracy gains, but the additional complexity was deemed unnecessary for the intended enterprise deployment.

    “While more complex fuzzy models (e.g., trapezoidal functions and type-2 fuzzy systems) could offer marginal accuracy gains, the additional complexity was deemed unnecessary for the intended enterprise deployment [33].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [33]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Mamdani-type inference systems are widely adopted for decision-making tasks that require human-like reasoning under imprecision.

    “The fuzzy model was designed following a Mamdani-type inference system, widely adopted for decision-making tasks that require human-like reasoning under imprecision [23,24].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    Hyperparameters for all models, including the number and shape of membership functions in the fuzzy system, were tuned via cross-validation using grid search.

    “To ensure a fair comparison, hyperparameters for all models-including the number and shape of membership functions in the fuzzy system-were tuned via cross-validation using grid search.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The fuzzy inference engine maintains consistent behavior under parameter uncertainty, supporting its operational reliability.

    “These findings suggest that the fuzzy inference engine maintains consistent behavior under parameter uncertainty, supporting its operational reliability.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The fuzzy framework naturally handles linguistic ambiguity without forcing rigid categorizations by modeling partial memberships across overlapping severity levels.

    “Furthermore, the fuzzy framework naturally handles linguistic ambiguity without forcing rigid categorizations.By modeling partial memberships across overlapping severity levels, it reflects the inherent imprecision of human language more faithfully than traditional classifiers, which tend to impose hard decision boundaries.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The iterative and consensus-driven methodology ensured that the rule base captured both individual expertise and cross-departmental agreement, improving operational realism.

    “This iterative and consensus-driven methodology ensured that the rule base captured both individual expertise and cross-departmental agreement, improving operational realism.”
    Quote in the paperSubstantial claim

  • May combine two claims. The quote only partly states this claim.

    Interpretationreasoning

    The quantile-based calibration approach ensured that each fuzzy term had well-defined coverage over the observed data distribution and maintained consistency with operational thresholds.

    “This approach ensured that each fuzzy term had a well-defined coverage over the observed data distribution and maintained consistency with operational thresholds.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The fuzzy system operates with extremely low computational requirements, ensuring real-time incident processing even on standard CPU-based infrastructures.

    “In addition to its accuracy and interpretability, the fuzzy system operates with extremely low computational requirements, ensuring real-time incident processing even on standard CPU-based infrastructures.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    A quantitative validation of the final rule set was conducted by measuring the model's predictive performance on the labeled test set, confirming that the expert-derived rules supported accurate and consistent prioritization outcomes.

    “A quantitative validation of the final rule set was conducted by measuring the model's predictive performance on the labeled test set, confirming that the expert-derived rules supported accurate and consistent prioritization outcomes.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    Triangular membership functions were chosen for their simplicity, interpretability, and computational efficiency.

    “The degree to which an incident belonged to each linguistic category was determined using triangular membership functions, chosen for their simplicity, interpretability, and computational efficiency [24].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Fuzzy logic allows degrees of membership across multiple categories, unlike traditional classification systems that force a crisp assignment of an incident to a discrete severity level.

    “Unlike traditional classification systems, which force a crisp assignment of an incident to a discrete severity level, fuzzy logic allows degrees of membership across multiple categories.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [20]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    The Mamdani approach was preferred over Sugeno-type systems due to its greater interpretability and its ability to represent rules in linguistic terms, aligning better with transparent and auditable decision support in enterprise contexts.

    “Compared to Sugeno-type systems, the Mamdani approach was preferred due to its greater interpretability and its ability to represent rules in linguistic terms, which aligns better with the goal of providing transparent and auditable decision support in enterprise contexts.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The proposed fuzzy logic framework achieves strong predictive performance, high interpretability, and robust operational behavior in text-based incident prioritization.

    “The results presented in the previous sections confirm that the proposed fuzzy logic framework achieves strong predictive performance, high interpretability, and robust operational behavior in the task of text-based incident prioritization.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    The fuzzy model is designed to model partial memberships and overlapping categories, and thrives under conditions of vague, context-dependent, or conflicting descriptions.

    “The fuzzy model, by contrast, thrives under these conditions, as it is designed to model partial memberships and overlapping categories”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    A panel of eight incident management specialists, each with over five years of operational experience, participated in the qualitative assessment.

    “A panel of eight incident management specialists, each with over five years of operational experience, participated in the assessment.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: A panel of eight experts with over five years of operational experience is sufficient to provide reliable qualitative assessment.

  • May combine two claims.

    Interpretationreasoning

    The presence of multiple memberships enhanced the interpretability and allowed operators to exercise judgment when necessary.

    “The presence of multiple memberships further enhanced the interpretability and allowed operators to exercise judgment when necessary.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationown data

    The overall performance gains suggest that the integration of fuzzy inference layer and semantic embedding quality is synergistic.

    “While the overall performance gains suggest that the integration is synergistic”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The fuzzy system outperformed baselines in F1-score and provided richer outputs that human operators could interpret and act upon more confidently.

    “Not only did the fuzzy system outperform baselines in F1-score, but it also provided richer outputs that human operators could interpret and act upon more confidently.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    BERT and SVM models, lacking structured uncertainty modeling, defaulted to Medium Priority, underestimating the potential business impact in Case 2.

    “BERT and SVM models, lacking structured uncertainty modeling, defaulted to Medium Priority, underestimating the potential business impact.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Transparency and explainability are key drivers of trust in AI-based decision support systems, especially in critical operations like incident management.

    “As highlighted by the expert evaluation, transparency and explainability are key drivers of trust in AI-based decision support systems, especially in critical operations like incident management”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [29]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationown data

    The fuzzy system weighted the conflict appropriately in Case 2, assigning the highest membership to High Priority but still acknowledging Medium and Critical possibilities.

    “The fuzzy system weighted the conflict appropriately, assigning the highest membership to High Priority but still acknowledging Medium and Critical possibilities.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    BERT and SVM, unable to accommodate the uncertainty, defaulted to Low Priority-a potentially risky oversimplification in Case 3.

    “BERT and SVM, unable to accommodate the uncertainty, defaulted to Low Priority-a potentially risky oversimplification.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    The fuzzy model, leveraging simple mathematical structures like triangular membership functions and Mamdani-type inference, achieves rapid inference times with minimal infrastructure, making it attractive for large-scale deployments.

    “In contrast, the fuzzy model, leveraging simple mathematical structures like triangular membership functions and Mamdani-type inference, achieves rapid inference times with minimal infrastructure, making it an attractive solution for large-scale deployments”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    Experts were presented with a curated sample of 200 incident reports extracted from the test set, balanced across priority levels and linguistic ambiguity.

    “These experts were presented with a curated sample of 200 incident reports extracted from the test set, balanced across priority levels and linguistic ambiguity.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Deep learning models like BERT remain largely opaque without complex post hoc interpretability techniques that are often unreliable or cumbersome to apply in real time.

    “This contrasts sharply with deep learning models like BERT, which, despite their semantic power, remain largely opaque without complex post hoc interpretability techniques that are often unreliable or cumbersome to apply in real time”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [13]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [30]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [31]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationreasoning

    This works well for problems where boundaries between classes are clear, but it becomes a serious limitation when dealing with incident reports, where natural language descriptions are often vague, context-dependent, or conflicting.

    “While this works well for problems where boundaries between classes are clear, it becomes a serious limitation when dealing with incident reports, where natural language descriptions are often vague, contextdependent, or conflicting.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Incident reports in enterprise settings are often vague, context-dependent, or conflicting.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Explainability fosters trust in AI systems, particularly in safety-critical domains.

    “This aligns with contemporary theories suggesting that explainability fosters trust in AI systems, particularly in safety-critical domains”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [29]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The current set of fuzzy rules is static.

    “While designed in collaboration with domain experts to cover typical incident scenarios, the current set of fuzzy rules is static.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The low inference times of the fuzzy system ensured that users experienced no noticeable delay in receiving recommendations, supporting seamless workflow integration.

    “The low inference times of the fuzzy system (<10 milliseconds per incident) ensured that users experienced no noticeable delay in receiving recommendations, supporting seamless workflow integration.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The case studies validated the accuracy and interpretability of the fuzzy approach and reaffirmed its suitability for deployment in real-world, time-sensitive enterprise environments.

    “Thus, the case studies not only validated the accuracy and interpretability of the fuzzy approach but also reaffirmed its suitability for deployment in real-world, time-sensitive enterprise environments”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [5]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The current work lacks an explicit ablation study to isolate the individual contributions of the fuzzy inference layer versus the semantic embedding quality.

    “Another limitation of the current work is the lack of an explicit ablation study to isolate the individual contributions of the fuzzy inference layer versus the semantic embedding quality.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The qualitative insights complement the quantitative results, reinforcing the view that the fuzzy logic framework offers not only technical accuracy but also the human-centered qualities necessary for successful integration into enterprise incident management workflows.

    “These qualitative insights complement the quantitative results presented earlier, reinforcing the view that the fuzzy logic framework offers not only technical accuracy but also the human-centered qualities necessary for successful integration into enterprise incident management workflows.”
    Quote in the paperSubstantial claim0 of 5 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [27]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [29]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    BERT and SVM exhibited classic failure patterns: BERT made crisp, sometimes incorrect predictions when textual cues were weak or contradictory; SVM failed to recognize deeper semantic conflicts altogether.

    “BERT and SVM exhibited classic failure patterns: BERT, despite its semantic richness, still made crisp, sometimes incorrect predictions when textual cues were weak or contradictory; SVM, being surfacelevel and linear, failed to recognize deeper semantic conflicts altogether”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [8]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [16]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    The fuzzy system exposes degrees of belief across multiple priority levels, enabling better-informed decision-making, particularly in borderline or ambiguous cases.

    “Rather than presenting a binary label, the fuzzy system exposes degrees of belief across multiple priority levels, enabling better-informed decision-making, particularly in borderline or ambiguous cases”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [27]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    The fuzzy logic framework required minimal maintenance compared to deep learning models, facilitating long-term sustainability.

    “It required minimal maintenance compared to deep learning models, facilitating long-term sustainability.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Its modular structure allows it to be extended to multi-attribute prioritization schemes where not just textual incident descriptions but also metadata, system monitoring metrics, and historical resolution times can be integrated into the fuzzy inference engine.

    “Its modular structure allows it to be extended to multi-attribute prioritization schemes where not just textual incident descriptions but also metadata, system monitoring metrics, and historical resolution times can be integrated into the fuzzy inference engine.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Frameworks such as the European Union's proposed Artificial Intelligence Act and sector-specific guidelines in finance and critical infrastructure management explicitly call for systems that can explain their decisions, support human oversight, and handle uncertainty gracefully.

    “Frameworks such as the European Union's proposed Artificial Intelligence Act and sectorspecific guidelines in finance and critical infrastructure management explicitly call for systems that can explain their decisions, support human oversight, and handle uncertainty gracefully”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [13]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [31]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The current framework relies on a manually defined rule base.

    “While the current framework relies on a manually defined rule base”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    The cost of misprioritizing an incident could involve significant business impact or reputational damage.

    “This is particularly important when operational stakes are high, and the cost of misprioritizing an incident could involve significant business impact or reputational damage”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [27]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The fuzzy framework proposed here, by integrating semantic embeddings from NLP with structured fuzzy reasoning, represents a balanced approach that captures the strengths of both data-driven and expert systems.

    “The fuzzy framework proposed here, by integrating semantic embeddings from NLP with structured fuzzy reasoning, represents a balanced approach that captures the strengths of both paradigms.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation

    By designing multi-input, multi-output fuzzy systems, the framework could be expanded to support more complex operational workflows, such as automated ticket routing, resource allocation optimization, and incident escalation management.

    “By designing multi-input, multi-output fuzzy systems, the framework could be expanded to support more complex operational workflows, such as automated ticket routing, resource allocation optimization, and incident escalation management”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    The fuzzy logic framework naturally embodies many human-centered design principles.

    “The fuzzy logic framework naturally embodies many of these principles.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The adoption of fuzzy logic in incident management aligns well with regulatory trends emphasizing transparency, fairness, and accountability in AI systems.

    “Moreover, the adoption of fuzzy logic in incident management aligns well with regulatory trends emphasizing transparency, fairness, and accountability in AI systems.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The participatory model of AI deployment-where systems support, rather than replace, human expertise-has been identified as a critical success factor in enterprise AI initiatives.

    “This participatory model of AI deployment-where systems support, rather than replace, human expertise-has been identified as a critical success factor in enterprise AI initiatives”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [29]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Introducing a fuzzy inference layer could enhance interpretability, robustness, and trust in systems where powerful deep learning models are employed to extract semantic features.

    “Even in systems where powerful deep learning models are employed to extract semantic features, introducing a fuzzy inference layer could enhance interpretability, robustness, and trust.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    By presenting graded assessments rather than categorical judgments, the system invites human operators to participate actively in the decision-making process rather than passively accepting algorithmic outputs.

    “By presenting graded assessments rather than categorical judgments, the system invites human operators to participate actively in the decision-making process rather than passively accepting algorithmic outputs.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The fuzzy logic approach aligns much more naturally with the way human experts reason under uncertainty.

    “The fuzzy logic approach, by allowing degrees of membership across multiple categories, aligns much more naturally with the way human experts reason under uncertainty.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Incident reports are often hastily composed, context-dependent, and linguistically vague, requiring models that can accommodate shades of meaning rather than enforce binary decisions.

    “Incident reports are often hastily composed, context-dependent, and linguistically vague, requiring models that can accommodate shades of meaning rather than enforce binary decisions [24,27].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [27]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The results suggest that fuzzy models can serve as a bridge layer between raw machine learning outputs and final operational decisions.

    “Furthermore, the results suggest that fuzzy models can serve as a bridge layer between raw machine learning outputs and final operational decisions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Mathematical models that acknowledge and incorporate human dimensions-such as fuzzy logic systems-are practically necessary for building effective operational intelligence systems.

    “Mathematical models that acknowledge and incorporate these human dimensions-such as fuzzy logic systems-are therefore not just theoretically interesting but practically necessary for building effective operational intelligence systems”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Changes in enterprise terminology, reporting practices, or incident patterns over time may lead to semantic drift, potentially degrading model performance.

    “Additionally, changes in enterprise terminology, reporting practices, or incident patterns over time may lead to semantic drift, potentially degrading model performance.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    The fuzzy model aligns with emerging regulatory frameworks around AI transparency and accountability.

    “Moreover, the alignment of the fuzzy model with emerging regulatory frameworks around AI transparency and accountability positions it as a forward-looking solution.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [13]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [30]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [31]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    The static nature of the fuzzy rule base could become a limitation over time.

    “Although the manually designed fuzzy rule base employed in this study performed well, its static nature could become a limitation over time as enterprise environments evolve, new technologies emerge, and incident types shift.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Premisecitation

    Incident management is not purely a technical domain; it is deeply social, involving human perception, communication, and organizational decision-making processes.

    “Incident management is not purely a technical domain; it is deeply social, involving human perception, communication, and organizational decision-making processes.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    The fuzzy model is highly scalable and sustainable for enterprise operations.

    “Unlike transformer-based models, which require substantial computational overhead for real-time inference, the fuzzy model achieves sub-10 millisecond inference times on standard CPU infrastructure, making it highly scalable and sustainable for enterprise operations [5,24,32].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [5]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    The use of semantic embeddings and fuzzy reasoning helps generalize beyond rigid patterns but does not eliminate the risk of bias entirely.

    “While the use of semantic embeddings and fuzzy reasoning helps generalize beyond rigid patterns, it does not eliminate the risk entirely.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Past labeling practices may reflect organizational norms, human inconsistencies, or implicit biases.
    • Rests on an assumption: The potential for bias in severity prediction exists due to reliance on historically labeled incident data.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationcitation

    The fuzzy framework's capacity to quantify doubt, accommodate linguistic ambiguity, and support human oversight positions it as a model aligned with the future of responsible AI.

    “The fuzzy framework's capacity to quantify doubt, accommodate linguistic ambiguity, and support human oversight positions it as a model aligned with the future of responsible AI [13,30,31].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [13]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [30]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [31]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation

    Other membership function shapes (e.g., trapezoidal and Gaussian) might better capture certain nuances in semantic distributions.

    “other membership function shapes (e.g., trapezoidal and Gaussian) might better capture certain nuances in semantic distributions”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    The fuzzy logic framework demonstrates excellent potential for scalability.

    “In terms of scalability, the fuzzy logic framework demonstrates excellent potential.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation

    Fuzzy logic supports a philosophy of collaboration between human and machine, rather than competition or replacement, which is increasingly recognized as essential in designing sustainable AI systems.

    “Fuzzy logic, by design, supports a philosophy of collaboration between human and machine, rather than competition or replacement, which is increasingly recognized as essential in designing sustainable AI systems”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [38]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

129 of 129

  • The paper relies on this without support.

    The dataset of 10,000 incident descriptions from a mid-sized technology enterprise is representative of enterprise incident management contexts.

    “The proposed system is evaluated on a real-world dataset comprising 10,000 incident descriptions from a mid-sized technology enterprise.”
    “The framework is implemented and evaluated on a real-world dataset of 10,000 incident descriptions from a mid-sized technology enterprise”

  • The paper relies on this without support.

    Domain experts' qualitative assessments are valid and reliable indicators of interpretability and alignment with human judgment.

    “Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment.”
    “A qualitative analysis with domain experts is conducted to assess the interpretability and operational relevance of the fuzzy-based prioritization outputs.”

  • The paper relies on this without support.

    Linguistic ambiguity is most prevalent in medium-severity incidents.

    “for medium-severity incidents, where linguistic ambiguity is most prevalent.”
    “for medium-severity incidents, where ambiguity is most prevalent.”

  • The paper relies on this without support.

    Natural language incident reports contain inherent uncertainty.

    “handling the uncertainty inherent in natural language incident reports”

  • The paper relies on this without support.

    The internal incident management system of a mid-sized technology enterprise provides a realistic and operationally complex environment for evaluating incident prioritization.

    “While this setting offers a realistic and operationally complex environment for evaluation”

  • The authors state this as an assumption.

    The mapping between semantic space and operational concepts is linear.

    “The reliance on principal components assumes linearity in the mapping between semantic space and operational concepts, which may not always hold.”

  • The authors state this as an assumption.

    Incident reports are written in English.

    “One current limitation is the assumption that incident reports are written in English.”

  • The paper relies on this without support.

    Other operational factors such as incident duration, number of affected users, or scope of business impact can also influence prioritization.

    “we acknowledge that other operational factors-such as incident duration, the number of affected users, or the scope of business impact-can also influence prioritization.”

  • The paper relies on this without support.

    Incident reports in enterprise settings are often vague, context-dependent, or conflicting.

    “where natural language descriptions are often vague, contextdependent, or conflicting.”

  • The paper relies on this without support.

    Operators often submit incomplete, grammatically flawed, or inconsistent reports, particularly under pressure during major service disruptions.

    “Operators often submit incomplete, grammatically flawed, or inconsistent reports, particularly under pressure during major service disruptions.”

  • The paper relies on this without support.

    Real-time incident management systems must process thousands of inputs per day without latency.

    “Real-time incident management systems must process thousands of inputs per day without latency.”

  • The paper relies on this without support.

    A panel of eight experts with over five years of operational experience is sufficient to provide reliable qualitative assessment.

    “A panel of eight incident management specialists, each with over five years of operational experience, participated in the assessment.”

  • The paper relies on this without support.

    The selected case studies are representative of common ambiguity patterns in the dataset.

    “The selected cases reflect common ambiguity patterns observed in the dataset-vague language, conflicting signals, and incomplete information-and were chosen based on expert feedback during annotation as typical examples of challenging prioritization scenarios.”

  • The paper relies on this without support.

    Incident management in real-world enterprise settings rarely offers well-defined class boundaries.

    “However, incident management in real-world enterprise settings rarely offers such clarity.”

  • The paper relies on this without support.

    The success of the fuzzy framework depends on operator training, acceptance, and trust-building.

    “While the model offers substantial advantages, its success depends on operator training, acceptance, and trust-building.”

  • The paper relies on this without support.

    Past labeling practices may reflect organizational norms, human inconsistencies, or implicit biases.

    “Past labeling practices may reflect organizational norms, human inconsistencies, or implicit biases that could be learned and reproduced by the system.”

  • The paper relies on this without support.

    The potential for bias in severity prediction exists due to reliance on historically labeled incident data.

    “Another important consideration is the potential for bias in severity prediction due to reliance on historically labeled incident data.”
17 of 17

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Claimed fuzzy Q-learning conflicts with the static rule base

    Major

    The introduction says the proposed framework incorporates fuzzy Q-learning to dynamically adjust prioritization policies. The discussion instead describes the current rule base as static and online updating as a future extension.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 5

  1. Wooldridge, M. (2009). An Introduction to MultiAgent Systems, Wiley. [3rd ed.].
  2. Myerson, R.B. (1991). Game Theory: Analysis of Conflict, Harvard University Press.
  3. Jurafsky, D., and Martin, J.H. (2020). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Pearson. [3rd ed.].
  4. Smith, 2020, Ethical considerations in AI research: GDPR compliance and anonymization techniques, J. Ethical AI, 3, 45
  5. Valepucha, 2023, A survey on microservices-based applications, IEEE Access, 11, 88339

Cited by

Sorted by citations

Related

Authors
Peralta, A., Olivas, J. A., & Navarro-Illana, P.
Affiliations
  • Valencian International University (ES)
  • Departamento de Tecnología y Sistemas de Información, Universidad de Castilla-La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
  • Escuela de Doctorado, Tech Universidad Tecnológica, Av. Taco, 164, 38108 La Laguna, Spain
Corresponding author
Peralta, Arturo
ORCID
Venue
Mathematics
Publisher
MDPI AG
Published
2025-06-18
Volume
13
Issue
12
Pages
2014
ISSN
2227-7390 (ISSN-L)
DOI
10.3390/math13122014
OpenAlex
W4411457431
Semantic Scholar
29efaf8bcfc4dcbb50d25feec68a76c3408b5cc2
Cited by
3
References
24
Citations by year
2026: 1 · 2025: 2
FWCI
1.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Mathematics (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
Keywords
incident prioritization, fuzzy logic, Mamdani fuzzy inference, triangular membership function, incident reports, linguistic ambiguity, incident severity, interpretability, IT service management, natural language processing, BERT comparison, support vector machine comparison

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-29.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-29.

Statistics and arithmetic are recomputed, not judged.

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