Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC)

Classification of Factors Used to Predict Vehicle Break down in Commercial Vehicles

Harshitha K — 2024-01-18

Prediction of vehicle breakdowns in commercial vehicles is of utmost importance to ensure the safety, reliability, and cost-effectiveness of road transport. In today's fast-paced and competitive business landscape, the smooth operation of commercial vehicles is crucial for ensuring efficient logistics and supply chain management. Unplanned breakdowns of commercial vehicles can lead to significant financial losses, delays in delivery schedules, and potential damage to the reputation of the transportation companies. Commercial vehicle breakdowns pose significant challenges to both fleet operators and the broader transportation industry. The unexpected failure of a commercial vehicle can result in substantial costs, operational disruptions, and safety hazards. In light of these considerations, the development of effective breakdown prediction models has become an essential endeavor. Predicting when and why a breakdown might occur not only allows for proactive maintenance but also minimizes the economic and safety risks associated with breakdowns.

Literature Review on Early PCOS Detection on Girl Child Using Artificial Intelligence or Machine Learning

Pallavi C S — 2024-06-30

Metabolic syndrome and polycystic ovarian syndrome (PCOS) are prevalent hormonal disorders affecting many women, often leading to long-term health complications. Timely and accurate diagnosis is crucial for effective treatment and prevention of further issues. However, traditional diagnostic methods can be inconsistent and may delay proper diagnosis. This study investigates the transformative potential of artificial intelligence (AI) in the detection, classification, and segmentation of PCOS and its correlation with metabolic syndrome. By leveraging AI's vast clinical data learning capabilities, we explore how AI can notify the main feature related with both conditions. The paper emphasizes AI's self-correcting ability, which facilitates continuous improvements in diagnostic accuracy. Through AI, enhance risk assessments for PCOS and related conditions like metabolic syndrome, enable earlier and more precise diagnoses, and ultimately increase individualized treatment plans tailored to each patient's unique needs. This research explores AI's potential in PCOS and metabolic syndrome, with the potential to revolutionize patient care and health outcomes.

Harnessing Reinforcement Learning for Agile Portfolio Management in Nifty 50 Stock Analysis

S.Satyanarayana — 2024-06-30

This ground-breaking research paper introduces a novel application of Reinforcement Learning (RL) for portfolio management in the context of Nifty 50 stock analysis. Traditional portfolio management methods often suffer from static allocation models that lack adaptability to market dynamics, resulting in suboptimal performance and increased risk. In response to this limitation, we propose a pioneering approach that harnesses the power of RL to construct an agile and adaptive portfolio management system capable of dynamically adjusting stock allocations based on real-time market trends and risk assessments. By leveraging the learning capabilities of RL, we demonstrate the system's efficacy in creating resilient and flexible investment strategies, ultimately leading to optimized portfolio performance within the Nifty 50 index.