Autonomous Resilience: Advancing Data Engineering Through Self-Healing Pipelines and Generative DOI

Lakshmi Srinivasarao Kothamasu

European Journal of Computer Science and Information Technology, Год журнала: 2025, Номер 13(28), С. 102 - 113

Опубликована: Апрель 15, 2025

This article explores the transformative potential of self-healing data pipelines enhanced by generative artificial intelligence in next-generation engineering environments. The integration machine learning models capable predicting, detecting, and autonomously resolving anomalies represents a paradigm shift how organizations manage their infrastructure. By examining both technical architecture organizational implications these systems, demonstrates can significantly reduce operational overhead while improving quality processing reliability. investigates implementation strategies across various industry contexts, addressing challenges governance considerations that emerge when deploying such systems. suggests adopting experience substantial improvements efficiency integrity, ultimately enabling more sophisticated data-driven decision making. contributes to evolving discourse on autonomous systems provides framework for future research field advanced engineering.

Язык: Английский

Apprehension toward generative artificial intelligence in healthcare: a multinational study among health sciences students DOI Creative Commons
Malik Sallam,

Kholoud Al-Mahzoum,

Haya Alaraji

и другие.

Frontiers in Education, Год журнала: 2025, Номер 10

Опубликована: Май 1, 2025

Background In the recent generative artificial intelligence (genAI) era, health sciences students (HSSs) are expected to face challenges regarding their future roles in healthcare. This multinational cross-sectional study aimed confirm validity of novel FAME scale examining themes Fear, Anxiety, Mistrust, and Ethical issues about genAI. The also explored extent apprehension among HSSs genAI integration into careers. Methods was based on a self-administered online questionnaire distributed using convenience sampling. survey instrument scale, while toward assessed through modified State-Trait Anxiety Inventory (STAI). Exploratory confirmatory factor analyses were used construct scale. Results final sample comprised 587 mostly from Jordan (31.3%), Egypt (17.9%), Iraq (17.2%), Kuwait (14.7%), Saudi Arabia (13.5%). Participants included studying medicine (35.8%), pharmacy (34.2%), nursing (10.7%), dentistry (9.5%), medical laboratory (6.3%), rehabilitation (3.4%). Factor analysis confirmed reliability Of constructs, Mistrust scored highest, followed by Ethics. participants showed generally neutral genAI, with mean score 9.23 ± 3.60. multivariate analysis, significant variations observed previous ChatGPT use, faculty, nationality, expressing highest level apprehension, Kuwaiti lowest. Previous use correlated lower levels. higher agreement Ethics constructs statistically associations apprehension. Conclusion revealed notable Arab HSSs, which highlights need for educational curricula that blend technological proficiency ethical awareness. Educational strategies tailored discipline culture needed ensure job security competitiveness an AI-driven future.

Язык: Английский

Процитировано

0

Autonomous Resilience: Advancing Data Engineering Through Self-Healing Pipelines and Generative DOI

Lakshmi Srinivasarao Kothamasu

European Journal of Computer Science and Information Technology, Год журнала: 2025, Номер 13(28), С. 102 - 113

Опубликована: Апрель 15, 2025

This article explores the transformative potential of self-healing data pipelines enhanced by generative artificial intelligence in next-generation engineering environments. The integration machine learning models capable predicting, detecting, and autonomously resolving anomalies represents a paradigm shift how organizations manage their infrastructure. By examining both technical architecture organizational implications these systems, demonstrates can significantly reduce operational overhead while improving quality processing reliability. investigates implementation strategies across various industry contexts, addressing challenges governance considerations that emerge when deploying such systems. suggests adopting experience substantial improvements efficiency integrity, ultimately enabling more sophisticated data-driven decision making. contributes to evolving discourse on autonomous systems provides framework for future research field advanced engineering.

Язык: Английский

Процитировано

0