Autonomous vehicles mitigate road accidents and provide safe transportation with a smooth traffic flow. They are expected to greatly improve the quality of the elderly or people with impairments by improving their mobility due to the ease of access to transportation. Autonomous vehicles sense the driving environment and navigate through it without human intervention. And, Deep Reinforcement Learning (DRL) is witnessed as a powerful machine learning solution to address a sequential decision problem in autonomous vehicles. The detailed state-of-the-art work in autonomous vehicles using DRL algorithms along with future research directions is discussed. Due to the high dimensional action space, two continuous action space DRL algorithms: Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) are chosen to address the complex autonomous driving problem. The proposed DDPG and PPO based decision-making models are trained and tested using the TORC simulator. Both the algorithms are trained for the same number of episodes for lane keeping as well as multi-agent collision avoidance scenarios. To the best of our knowledge, this is the first paper to present the comparative performance analysis of these two algorithms, and DDPG is found to perform better in terms of higher reward and faster convergence than PPO. Hence, DDPG is a suitable option in the design of a decision model for autonomous driving.

Numerous accidents and fatalities occur every year across the world as a result of the reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to these factors, autonomous cars have attracted enormous attention as a potentially game-changing technology to address a number of persistent problems in the transportation industry. Autonomous vehicles need to be modeled as intelligent agents with the capacity to observe, and perceive the complex and dynamic environment on the road, and decide an action with the highest priority to the lives of people in every scenarios. The proposed deep deterministic policy gradient-based sequential decision algorithm models the autonomous vehicle as a learning agent and trains it to drive on a lane, overtake a static and a moving vehicle, and avoid collisions with obstacles on the front and right side. The proposed work is simulated using a TORC simulator and has shown the expected performance under the above-said scenarios.

Sepsis is a lethal infection-related illness that has an extremely high fatality rate, especially among intensive care unit patients. Early and precise recognition of sepsis is critical as delayed treatment increases the mortality rate dramatically. System inflammatory response syndrome, quick sequential organ failure assessment, and modified early warning score are the traditional clinical score systems in practice to detect sepsis. But the scoring systems fail in the early prediction of sepsis, a stage in which if a patient is treated immediately, the mortality rate will reduce significantly. The proposed classifier can accurately predict sepsis up to six hours before the disease is clinically diagnosed. The patient’s electronic medical records, demographics, and vital signs are used to achieve this. The study uses data set adaptive data preprocessing strategies. The proposed method adds value to existing literature by introducing a novel outlier-based mean-median data imputation technique that enhances the prediction’s overall accuracy. The primary factors that influence the classifier’s predictions have been outlined, making the model easier to understand for medical professionals. For the classification of patients as sepsis positive or negative, four algorithms were investigated: Random Forest, Logistic Regression, Gradient Boosting, and Decision Tree. Of all the prediction algorithms, Random Forest gives the best results with an accuracy of 99.01%, F1-score of 99%, and an area under the receiver operator characteristic curve of 99.99%. Even for a 24-hour early prediction of sepsis, the random forest method is proven to provide greater prediction accuracy while logistic regression provides the least prediction accuracy. We attribute this to the fact that, unlike regression models, random forests do not require that the model have a linear relationship between the dependent and independent variables. The evaluation measures produced are useful and can be tremendously valuable in predicting sepsis in a timely and accurate manner.

Network Simulator(NS-2) is an open source discrete event simulator, widely used by the networking research community. NS-2 provides flexibility for the researcher to extend the simulator functionality for any new area of research in networks. The primary objective of the paper is to investigate the development and simulation of the interface assignment in multi-interface multi-channel wireless adhoc network in NS-2. Accordingly, existing ad hoc on-demand distance vector(AODV) routing protocol which was designed for single interface single channel (S-AODV) was studied and modified to support multi-interface multi-channel (M-AODV)configuration. Simulation results show better performance of M-AODV over S-AODV.