https://jaim.sciforce.org/JAIM <p>Advancing Intelligence in the Digital Age: Journal of Artificial Intelligence and Machine Learning (JAIM) by Sciforce Publications</p> <p>Step into the world of artificial intelligence (AI) and machine learning (ML) with the Journal of Artificial Intelligence and Machine Learning (JAIM), a distinguished publication by Sciforce Publications. JAIM serves as a beacon for the latest research and innovations in the fields of artificial intelligence, machine learning, and data-driven decision-making. In this web content, we will explore the significance of JAIM, its contributions to the scientific community, and the dynamic realm of AI and machine learning.</p> www.sciforce.org en-US Journal of Artificial intelligence and Machine Learning 2995-2336 https://jaim.sciforce.org/JAIM/article/view/257 <p>Efficient resource allocation is a critical component of Enterprise Resource Planning (ERP) systems. Existing approaches often rely on static allocation methods that fail to adapt to dynamic business environments, leading to inefficiencies. This paper proposes an intelligent, Machine Learning (ML)-based solution leveraging reinforcement learning to dynamically optimize resource allocation in ERP systems. We review resource allocation challenges, present our dynamic ML-based framework, and validate its effectiveness through simulated scenarios. Results demonstrate significant improvements in resource utilization, adaptability, and overall system performance.This study evaluates eight ML-based resource allocation methods for ERP systems across six metrics: efficiency, cost reduction, scalability, implementation time, integration complexity, and energy consumption. Using normalized data and weighted analysis, the research identifies Automated Resource Allocation System as the optimal solution, with Machine Learning-based Scheduling as a strong alternative.</p> Veeresh Dachepalli Copyright (c) 2025 Veeresh Dachepalli https://creativecommons.org/licenses/by-nc/4.0 2025-01-29 2025-01-29 3 2 1 16 10.55124/jaim.v3i2.257 https://jaim.sciforce.org/JAIM/article/view/262 <p>One of the most important aspects of risk management for financial institutions is assessing credit risk. Credit scoring models are important tools for evaluating loan applications because they provide a systematic way to assess credit worthiness. While traditional statistical models have been widely used, artificial intelligence (AI) has emerged as a more efficient alternative due to its ability to process large datasets and improve predictive accuracy. The growing reliance on AI-powered models has transformed lending practices, improving decision-making, reducing default risks, and enhancing financial stability. The focus of this research is on exploring AI-based credit scoring models and their impact on financial institutions. Traditional credit scoring methods often lack accuracy and efficiency, leading to increased risks and losses. AI methods like machine learning and deep learning offer a more reliable method, analyzing huge amounts of data and spot patterns that people are not aware of. Gaining insight into how AI affects credit scoring helps with risk management, loan selection, and financial inclusion. Other options for A1, A2, A3, A4, and A5. Income level, credit score, existing debt, and recent credit inquiries are all part of the assessment. The results showed that A3 ranked lowest and A4 ranked best. A1 has the highest value for The Role of AI in Enhancing Credit Scoring Models for Better Lending according to the TOPSIS Method approach</p> VINAY KUMAR CHUNDURU Copyright (c) 2025 VINAY KUMAR CHUNDURU https://creativecommons.org/licenses/by-nc/4.0 2025-03-05 2025-03-05 3 2 1 11 10.55124/jaim.v3i2.262