Muhammad Azeem Sarwar

This paper presents an ensemble machine-learning approach to monitor the blood volume decomposition state for early hypovolemia detection. Hypovolemia is one of the major causes of preventable deaths in trauma cases. The proposed algorithm discriminates hypovolemia from normovolemia and further classifies hypovolemia into relative and absolute hypovolemia. The algorithms for blood volume classification are analyzed by extracting 13 distinct features from multi-modal physiological signals including Photoplethysmogram, Electrocardiogram, and Seismocardiogram. We compared different Machine Learning classifiers for the multi-class classification problem. We have validated our algorithm on a publicly available dataset collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia conditions. The best-performing algorithm is the Artificial Neural Network- (ANN) for the normovolemia/hypovolemia classifier with an accuracy of 93.2% and an F1-score of 0.97. For the absolute/relative hypovolemia (AH-RH) classifier, Long Short-Term Memory offers an accuracy of 95.1% and an F1-score of 0.97. The proposed classifiers outperform the state-of-the-art algorithms and achieve the highest accuracy and F1-score, serving as a potential decision-support tool to observe blood volume decomposition state and help develop context-sensitive hypovolemia therapeutic strategies.

Intelligent traffic management systems have become one of the main applications of Intelligent Transportation Systems (ITS). There is a growing interest in Reinforcement Learning (RL) based control methods in ITS applications such as autonomous driving and traffic management solutions. Deep learning helps in approximating substantially complex nonlinear functions from complicated data sets and tackling complex control issues. In this paper, we propose an approach based on Multi-Agent Reinforcement Learning (MARL) and smart routing to improve the flow of autonomous vehicles on road networks. We evaluate Multi-Agent Advantage Actor-Critic (MA2C) and Independent Advantage Actor-Critical (IA2C), recently suggested Multi-Agent Reinforcement Learning techniques with smart routing for traffic signal optimization to determine its potential. We investigate the framework offered by non-Markov decision processes, enabling a more in-depth understanding of the algorithms. We conduct a critical analysis to observe the robustness and effectiveness of the method. The method’s efficacy and reliability are demonstrated by simulations using SUMO, a software modeling tool for traffic simulations. We used a road network that contains seven intersections. Our findings show that MA2C, when trained on pseudo-random vehicle flows, is a viable methodology that outperforms competing techniques.