Communications Medicine, Journal Year: 2025, Volume and Issue: 5(1)
Published: May 17, 2025
Language: Английский
Computers, Journal Year: 2025, Volume and Issue: 14(2), P. 59 - 59
Published: Feb. 10, 2025
Strategic cost optimization is a critical challenge for businesses aiming to maintain competitiveness in dynamic markets. This paper introduces Gen-Optimizer, Generative AI-based framework designed analyze and optimize business costs through intelligent decision support. The employs transformer-based model with over 140 million parameters, fine-tuned using diverse dataset of cost-related scenarios. By leveraging generative capabilities, Gen-Optimizer minimizes inefficiencies, automates analysis tasks, provides actionable insights decision-makers. proposed achieves exceptional performance metrics, including prediction accuracy 93.2%, precision 93.5%, recall 93.1%, an F1-score 93.3%. perplexity score 20.17 demonstrates the model’s superior language understanding abilities. was tested real-world scenarios, demonstrating its ability reduce operational by 4.11% across key functions. Furthermore, it aligns sustainability objectives, promoting resource efficiency reducing waste. highlights transformative potential AI management, paving way scalable, intelligent, cost-effective solutions.
Language: Английский
Citations
0
Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 37 - 64
Published: April 8, 2025
The rapid advancement of artificial intelligence (AI) has transformed contract security, offering innovative solutions for managing expiry, ensuring compliance, and mitigating risks. Traditional management systems often struggle with scalability, accuracy, adaptability, leading to inefficiencies potential legal vulnerabilities. This chapter explores how deep learning large language models (LLMs) enhance security by automating review, expiration tracking, regulatory compliance assessment. By leveraging natural processing (NLP) predictive analytics, AI-driven can proactively identify risks, flag anomalies, ensure adherence contractual obligations. Furthermore, this discusses key challenges such as bias in AI models, data privacy concerns, the need robust frameworks. Through case studies experimental results, we demonstrate AI-powered improves efficiency, reduces human errors, enhances organizational risk management.
Language: Английский
Citations
0
Journal of Enterprise Information Management, Journal Year: 2025, Volume and Issue: unknown
Published: April 16, 2025
Purpose Supply networks rarely operate in a stable, steady state. Thus, businesses must carefully plan for unpredictable events to mitigate risks. Consequently, this research investigates how artificial intelligence (AI), machine learning (ML) and big data analytics (BDA) improve supply chain resilience. Design/methodology/approach This study utilizes the integrated analytic hierarchy process (AHP) – decision-making trial evaluation laboratory (DEMATEL) as resolution technique achieve objective. By undertaking semi-structured interviews with experts from fast moving consumer goods (FMCG) industry, authors gathered useful information AHP-DEMATEL analysis. The results obtained are validated through qualitative survey approach. Findings Sub-factors used were extracted extensive literature review. AHP method was employed prioritize factors sub-factors wherein AI comes out be most prominent technology bring resilience by improving efficiency, followed demand forecasting. DEMATEL bifurcates into cause effect. Originality/value adds domain identifying that can better managed particular technologies, i.e. AI, ML BDA.
Language: Английский
Citations
0
Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105047 - 105047
Published: April 1, 2025
Language: Английский
Citations
0
Communications Medicine, Journal Year: 2025, Volume and Issue: 5(1)
Published: May 17, 2025
Language: Английский
Citations
0