Abdulsalam Ahmed Alqarni

The decline in battery voltage during drone (UAV) flights poses a critical challenge, impacting flight safety, operational range, and efficiency. As battery voltage drops, motor performance diminishes, increasing the risk of crashes or failure to return to the launch point. This issue requires innovative solutions due to its implications for safety, performance, cost reduction, and sustainability. This research proposed the development of an intelligent system using live data and the machine learning algorithms to predict the state of drone’s battery. The model uses historical flight data, incorporating key variables such as battery voltage, wind speed, flight time, and environmental conditions to predict battery discharge during the operation. The proposed research is consisted of multiple phases, starting with problem definition and exploratory data analysis (EDA), followed by correlation analysis to understand the relationships between variables. The final phase will involve developing an interactive intelligent system that integrates the predictive model for real-time monitoring, proactive maintenance, and improved power management strategies. The current study will reduce operational costs, extend battery lifespan, and enhance safety across various drone applications.

Efficient selection of trucks represents a significant challenge within logistics operations, directly influencing both cost efficiency and delivery effectiveness. This study introduces a comprehensive four-stage data modeling framework that employs machine learning techniques, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), to forecast load capacities and appropriately assign trucks for deliveries. The analysis utilized historical delivery data, which was meticulously processed to ensure accurate predictions regarding truck selection based on load capacity. Findings indicate that the Random Forest model excels in precisely estimating pallet requirements, while the Decision Tree method demonstrates superior performance in optimizing truck assignments. This research provides logistics managers with a viable strategy to enhance operational efficiency, reduce expenses, and improve resource allocation.