| _version_ | 1866902137418547200 |
|---|---|
| author | Chirag Devendrakumar Parikh |
| author_facet | Chirag Devendrakumar Parikh |
| contents | <p><span>Before entering the international markets, electronic products have to comply with a significant number of safety, electromagnetic compatibility, environmental, and performance standards. The increasing number of standards and the variation in regional requirements tend to create long certification cycles, disjointed interpretations, and late design revisions, which result in a higher cost and slow commercialization. The paper presents an example of an AI-based framework that automates and simplifies the global compliance lifecycle of consumer and industrial electronics. The framework includes the combination of natural-language processing to interpret regulatory documents, supervised learning to predict high-risk requirements, and rule-based reasoning to produce clause-specific compliance actions. An anomaly-detection layer points out the possible design flaws at the early stages of development, and an automated documentation layer generates test plans, compliance matrices, and certification checklists. An example of case-study data based on publicly accessible certification documents and safety outcomes is used to demonstrate that AI may be used to minimize the risk exposure, balance requirements in the various markets, and enhance the availability of formal testing. The findings show that AI-powered compliance applications can enhance efficiency, consistency, and predictability in international product certification to a considerable extent</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18182708 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Based Global Compliance Automation Framework for Consumer and Industrial Electronics Chirag Devendrakumar Parikh Artificial Intelligence (AI), Global Compliance Automation, Regulatory Standards Interpretation, Natural Language Processing (NLP), Predictive Compliance Risk, Anomaly Detection, Automated Compliance Documentation, Consumer and Industrial Electronics <p><span>Before entering the international markets, electronic products have to comply with a significant number of safety, electromagnetic compatibility, environmental, and performance standards. The increasing number of standards and the variation in regional requirements tend to create long certification cycles, disjointed interpretations, and late design revisions, which result in a higher cost and slow commercialization. The paper presents an example of an AI-based framework that automates and simplifies the global compliance lifecycle of consumer and industrial electronics. The framework includes the combination of natural-language processing to interpret regulatory documents, supervised learning to predict high-risk requirements, and rule-based reasoning to produce clause-specific compliance actions. An anomaly-detection layer points out the possible design flaws at the early stages of development, and an automated documentation layer generates test plans, compliance matrices, and certification checklists. An example of case-study data based on publicly accessible certification documents and safety outcomes is used to demonstrate that AI may be used to minimize the risk exposure, balance requirements in the various markets, and enhance the availability of formal testing. The findings show that AI-powered compliance applications can enhance efficiency, consistency, and predictability in international product certification to a considerable extent</span></p> |
| title | AI-Based Global Compliance Automation Framework for Consumer and Industrial Electronics |
| topic | Artificial Intelligence (AI), Global Compliance Automation, Regulatory Standards Interpretation, Natural Language Processing (NLP), Predictive Compliance Risk, Anomaly Detection, Automated Compliance Documentation, Consumer and Industrial Electronics |
| url | https://doi.org/10.5281/zenodo.18182708 |