The increasing demand for secure and efficient voting systems has led to the exploration of online voting solutions. Traditional voting methods are often vulnerable to fraud, inefficiencies, and logistical challenges. This paper presents an online voting system that leverages machine learning techniques to enhance security, accuracy, and accessibility. The system employs facial recognition for voter authentication, anomaly detection to prevent fraudulent activities, and natural language processing (NLP) for user interaction. Experimental results indicate that the proposed model provides a reliable, tamper-resistant, (...) and user-friendly voting experience. (shrink)

Multi-agent reinforcement learning (MARL) has gained significant attention due to its applications in complex, interactive environments. Traditional MARL approaches often struggle with scalability and non-stationarity as the number of agents increases. Mean Field Reinforcement Learning (MFRL) provides a scalable alternative by approximating interactions using aggregated statistics. However, existing MFRL models fail to capture causal relationships between agent interactions, leading to suboptimal decision-making. In this work, we introduce Causal Mean Field Multi-Agent Reinforcement Learning (Causal-MFRL), which integrates causal inference techniques into the (...) mean field framework. By leveraging causal graphs and counterfactual reasoning, Causal-MFRL improves policy learning and enhances the interpretability of agent behaviors. We evaluate our approach on standard MARL benchmarks, demonstrating superior performance in efficiency, robustness, and generalization. (shrink)