Jul 2026· Journal of Research in Engineering and Computer Sciences· 0 citations
TL;DR
An algorithm is developed that automates and streamlines the analysis of equipment field-failure reports and other unstructured maintenance records and reduces the resource-intensive manual work required to prepare, interpret, and process FRACAS reports, thus enabling timelier, data-driven equipment reliability analysis.
Abstract
This study presents a novel, AI-assisted approach to industrial reliability analysis that integrates Failure Modes, Effects and Criticality Analysis (FMECA) and the Failure Reporting, Analysis, and Corrective Action System (FRACAS) with Natural Language Processing (NLP). We developed an algorithm that automates and streamlines the analysis of equipment field-failure reports and other unstructured maintenance records. The proposed framework combines unsupervised clustering to identify recurring equipment failure modes with a supervised Support Vector Machine (SVM) classifier with a Radial Basis Function (RBF) kernel to categorize equipment field reliability reports by failure mode and underlying mechanism at scale. Using an train–test split, the proposed model achieved accuracy on the test dataset, indicating effective generalization to unseen maintenance reports. The Confusion Matrix metrics across all classes showed true positive rate (TPR) (or sensitivity) of 0.91, indicating the model’s strong ability to correctly identify positive samples. The false positive rate (FPR) averaged 0.03 across all classes, demonstrating excellent specificity (true negative rate, TNR of 0.97). Operationally, the methodology reduces the resource-intensive manual work required to prepare, interpret, and process FRACAS reports, thus enabling timelier, data-driven equipment reliability analysis. Overall, the study demonstrates the feasibility and benefits of using AI-assisted reliability tools that balance automation with human expertise through a human-in-the-loop approach.
This study proposes a lightweight Natural Language Processing (NLP) pipeline to automatically classify the primary cause of major accidents using the European eMARS database and shows that Word2Vec+SVM provides the strongest and most stable baseline on the full labelled set, while SBERT performance improves markedly under higher label fidelity.
Valerio Cozzani, B. Fabiano, G. Reniers et al.· 0 citations
This paper offers an automated solution to the problem of categorizing the threat descriptions based on OSINT into the MITRE ATT&CK techniques with a sophisticated model based on transformer and natural language processing, leading to more effective CTI automation and decision support to the security operations of a practitioner.
Ramesh Kumar Sharma, Dharmendra Kumar Singh, Rajesh P. Barnwal· International journal of com...· 0 citations
Recruiting teams are increasingly confronted with the volume-versus-quality problem: the number of applications has grown faster than the ability of manual resume screening, and keyword-driven applicant tracking systems (ATS) routinely miss qualified candidates whose resumes don’t surface the exact terms a filter is looking for. In this paper, we propose a comprehensive end-to-end intelligent recruitment system, “Intelli360”, which exploits Natural Language Processing (NLP), supervised Machine Learning (ML) and predictive analytics to automate the process of resume parsing, skill extraction, ATS score prediction and candidate ranking. The system extracts structured information from unstructured resumes, computes a weighted resume-matching score against job descriptions and predicts an ATS suitability score. Recruiter and candidate dashboards, an AI chatbot and automated interview scheduling support the surrounding workflow. Intelli360 was benchmarked against a traditional keyword-based ATS and manual screening on a multi-domain resume set covering Artificial Intelligence, Data Science, Software Engineering, Web Development and Cybersecurity roles. Intelli360 achieved a matching accuracy of 91.8% compared to 82.3% for the keyword-based ATS and 72.5% for manual screening, a precision of 90.6%, recall of 92.9% and F1-score of 91.7%. Average resume screening time reduced from 18.4 seconds to 4.6 seconds per resume Average shortlisting time reduced from 6.5 days to 1.8 days Our results suggest that the combination of semantic, NLP-based skill extraction and ML-based ranking provides a significant improvement overrule-based and keyword-based recruitment pipelines. Further work is needed to statistically validate these gains (crossvalidation, confusion-matrix analysis, and significance testing).
Index Terms—recruitment automation, natural language processing, machine learning, resume parsing, ATS score prediction, candidate ranking, predictive hiring analytics.
Namandeep Namandeep, A. Amandeep, Dharmender Dharmender et al.· International Scientific Jou...· 0 citations
This research provides a highly accurate, scalable, and reliable framework for automated bridge defect analysis, offering a practical methodology to enhance data utilization in bridge management.
Lu-yang Zhang, Xuzhao Lu, Fengzong Gong et al.· Advances in Structural Engin...· 0 citations
This study presents an automated knowledge extraction and prediction system using the advancements in Artificial Intelligence (AI) tools, referred to as APEX-LLM, which is a scalable, domain-independent system which can be customized and applied to health, financial and business sectors, and education.
The study aimed to develop and implement a machine learning system for predicting the likelihood of incidents involving medical devices, using operating documentation (instructions for use) as a key data source. To this end, the authors designed a dedicated database architecture and curated open data sources on registered medical devices and related incidents, ensuring their effective parsing and preprocessing. A novel aspect of the research lies in the analysis of operating documentation contents: the study identifies textual patterns — potentially evolving over time — that correlate with past incidents linked to similar devices. This approach shifts the focus from modelling the physical interaction between the device and the human body to leveraging linguistic and procedural markers in technical documentation as risk indicators. The proposed machine learning framework is designed to be scalable and continuously retrainable as new data becomes available. An experimental model based on a recurrent neural network (RNN) was trained and validated, achieving an 89 % prediction accuracy on the validation set. These results confirm the hypothesis that specific features in medical device documentation are indicative of elevated risks to safety, quality, or effectiveness. The practical value of the developed solution lies in its potential for broad and cost effective deployment. It can support the identification of higher risk medical devices both at the stage of state registration and among those already circulating on the market. Economically, the approach offers benefits by streamlining the registration process and enabling manufacturers to proactively detect and address design or usage related deficiencies, thereby reducing long term costs.
D. O. Roshchin, S. Otstavnov· Mezhdunarodnaja jekonomika (...· 0 citations
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