In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares the effectiveness of various machine learning models to improve recruitment accuracy and efficiency. Using the recruitment data from a major Yemeni organization (2019–2022), we evaluated models including K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Trees, Random Forest, Gradient Boosting Classifier, AdaBoost Classifier, and Neural Networks. Hyperparameter tuning and cross-validation were used for optimization. The Random Forest model achieved the highest accuracy (92.8%), followed by Neural Networks (92.6%) and Gradient Boosting Classifier (92.5%). These results suggest that advanced machine learning models, particularly Random Forest and Neural Networks, can significantly enhance the recruitment processes in business intelligence systems. This study provides valuable insights for recruiters, advocating for the integration of sophisticated machine learning techniques in talent acquisition strategies.

Traffic signal control aims to minimize congestion and maximize efficiency in urban traffic systems. While recent reinforcement learning algorithms have shown promising performances, they are solely based on structured sensor inputs and cannot cope with unexpected disruptions described in natural language, such as accidents, construction, or extreme weather. Current RL methods experience 3% to 5% performance degradation under these severe disruptions. In this paper, we propose TARL-TSC, a framework to integrate natural language event description with traffic observations using learned fusion mechanisms. TARL-TSC uses a frozen pre-trained text encoder for semantic understanding and presents two designed fusion architectures for cross-model integration: attention-based fusion and adaptive gating fusion, and intersections are spatially coordinated by a Graph Attention Network. Experimental results on real traffic networks show that TARL-TSC performs better than baselines in several aspects. TARL-TSC reaches a throughput of 869.1 vehicles per episode on the Hangzhou 4×4 network, improving over state-of-the-art MPLight by 2.9% (p = 0.023). Cross-city comparisons also show strong generalization over unseen urban networks, without fine-tuning specifically for certain cities. TARL-TSC achieves stable gains of 1.2% to 2.9% on the Jinan and New York networks. TARL-TSC maintains a stable performance under event distribution with minimal degradation of only 1.3%, while the baselines decline about 5%. Besides, the framework achieves real-time inference within 8.3 ms, which demonstrates its practical deployment ability. TARL-TSC sets a new benchmark for event-aware traffic signal control, proving the feasibility of using text-augmented RL in achieving better robustness without sacrificing operational efficiency.

Traffic signal control aims to minimize congestion and maximize efficiency in urban traffic systems. While recent reinforcement learning methods have shown promising performance, they primarily rely on structured sensor data and have limited ability to incorporate semantic data about traffic disruptions described in natural language, such as accidents, construction, or extreme weather. This paper introduces Text-Augmented Reinforcement Learning for Traffic Signal Control (TARL-TSC), a framework that integrates textual event descriptions with traffic observations through learned fusion mechanisms. TARL-TSC employs two fusion architectures, attention-based and adaptive gating, combined with graph attention networks for spatial coordination. Experimental evaluation on real traffic networks shows consistent improvements. TARL-TSC achieves 869.1 throughput on Hangzhou, exceeding the top-performing baseline by 2.9\% ($p = 0.023$). Under event disruptions, TARL-TSC maintains degradation between 0.3\% and 0.9\%, while the top-performing baseline experiences degradation between 0.8\% and 2.4\%. Cross-city evaluation indicates improvements of 1.2\% to 2.9\% across networks with 12 to 48 intersections without fine-tuning. The framework achieves real-time inference at 8.3 milliseconds per decision inference latency within the control loop, meeting deployment constraints. Architectural analysis suggests that semantic augmentation, spatial coordination, and learned fusion each contribute to performance under both traffic disruptions and variations in semantic input. The code will be released upon publication.