AI-Driven Cloud Integration for Next-Generation Enterprise Systems: A Comprehensive Analysis DOI

Ramadevi Sannapureddy

European Journal of Computer Science and Information Technology, Год журнала: 2025, Номер 13(34), С. 13 - 24

Опубликована: Май 15, 2025

The convergence of artificial intelligence and cloud computing represents a transformative paradigm in enterprise architecture, creating unprecedented opportunities for operational excellence competitive differentiation. This comprehensive examination AI-driven integration explores the multifaceted impact across key domains computing. reinforcement learning into orchestration delivers substantial infrastructure cost reductions while simultaneously enhancing performance metrics environmental sustainability. In security frameworks, unsupervised federated approaches enable proactive threat detection with exceptional accuracy preserving data privacy organizational boundaries. Predictive analytics capabilities, particularly when combined edge architectures, fundamentally transform decision-making processes by providing actionable from heterogeneous sources remarkable speed precision. Self-healing systems powered sophisticated neural network architectures dramatically reduce downtime maintenance costs through automated anomaly remediation, cognitive APIs bridge legacy modern efficiency. technological evolution establishes new benchmarks excellence, enabling organizations to achieve significant agility efficiency increasingly complex digital environments. Future directions indicate quantum integration, advanced enhanced improved predictive analytics, robust ethical governance as critical areas continued advancement AI-cloud synergy.

Язык: Английский

AI-Driven Irrigation Systems for Sustainable Water Management: A Systematic Review and Meta-Analytical Insights DOI Creative Commons
Gülcay ERCAN OĞUZTÜRK

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100982 - 100982

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
AI vs. Human Programmers: Complexity and Performance in Code Generation DOI Open Access

Samina Azeem,

Muhammad Shumail Naveed, Muhammad Sajid

и другие.

VAWKUM Transactions on Computer Sciences, Год журнала: 2025, Номер 13(1), С. 201 - 216

Опубликована: Май 10, 2025

Large language models, such as ChatGPT, have demonstrated the capability to perform diverse tasks across various domains, significantly enhancing efficiency. However, their growing adoption raises concerns about potential job displacement, especially in technical fields. While numerous studies explored performance of large models a notable gap exists evaluating capabilities programming. This study addresses that by comparing ChatGPT (GPT-4) with human experts programming domain assess whether has reached level where it could replace programmers. To achieve this objective, generated 300 Python programs using and compared them functionally equivalent developed three experienced The evaluation encompassed both quantitative qualitative analyses, employing metrics Halstead Complexity, Cyclomatic expert judgment from two evaluators. findings revealed statistically significant differences between human-written code. Programs exhibited verbosity, complexity, resource demands, evidenced higher program volume, difficulty, cyclomatic complexity scores. In terms, ChatGPT’s code was more readable but lagged key areas, including documentation quality, function structuring, adherence coding standards. Conversely, excelled maintainability, error handling, addressing edge cases. Although remarkable efficiency generating functional code, its output required extensive review refinement meet concluded while serves valuable tool for generation, not yet expertise

Язык: Английский

Процитировано

0
A Comprehensive Analysis of Privacy-Preserving Solutions Developed for IoT-Based Systems and Applications DOI Open Access
Abdul Majeed, Sakshi Patni, Seong Oun Hwang

и другие.

Electronics, Год журнала: 2025, Номер 14(11), С. 2106 - 2106

Опубликована: Май 22, 2025

In recent years, a large number of Internet Things (IoT)-based products, solutions, and services have emerged from the industry to enter marketplace, improving quality service. With wide adoption IoT-based systems/applications in real scenarios, privacy preservation (PP) topic has garnered significant attention both academia industry; as result, many PP solutions been developed, tailored systems/applications. This paper provides an in-depth analysis state-of-the-art (SOTA) recently developed for systems applications. We delve into SOTA methods that preserve IoT data categorize them two scenarios: on-device cloud computing. existing privacy-by-design (PbD), such federated learning (FL) split (SL), engineering (PESs), differential (DP) anonymization, we map IoT-driven applications/systems. further summarize latest employ multiple techniques like ϵ-DP + anonymization or blockchain FL (rather than employing just one) PES PbD categories. Lastly, highlight quantum-based devised enhance security and/or real-world scenarios. discuss status current research within scope established this paper, along with opportunities development. To best our knowledge, is first work comprehensive knowledge about topics centered on IoT, which can provide solid foundation future research.

Язык: Английский

Процитировано

0
AI-Driven Cloud Integration for Next-Generation Enterprise Systems: A Comprehensive Analysis DOI

Ramadevi Sannapureddy

European Journal of Computer Science and Information Technology, Год журнала: 2025, Номер 13(34), С. 13 - 24

Опубликована: Май 15, 2025

The convergence of artificial intelligence and cloud computing represents a transformative paradigm in enterprise architecture, creating unprecedented opportunities for operational excellence competitive differentiation. This comprehensive examination AI-driven integration explores the multifaceted impact across key domains computing. reinforcement learning into orchestration delivers substantial infrastructure cost reductions while simultaneously enhancing performance metrics environmental sustainability. In security frameworks, unsupervised federated approaches enable proactive threat detection with exceptional accuracy preserving data privacy organizational boundaries. Predictive analytics capabilities, particularly when combined edge architectures, fundamentally transform decision-making processes by providing actionable from heterogeneous sources remarkable speed precision. Self-healing systems powered sophisticated neural network architectures dramatically reduce downtime maintenance costs through automated anomaly remediation, cognitive APIs bridge legacy modern efficiency. technological evolution establishes new benchmarks excellence, enabling organizations to achieve significant agility efficiency increasingly complex digital environments. Future directions indicate quantum integration, advanced enhanced improved predictive analytics, robust ethical governance as critical areas continued advancement AI-cloud synergy.

Язык: Английский

Процитировано

0