Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.

Sustainable cities are envisioned to have economic and industrial steps toward reducing pollution. Many real-world applications such as autonomous vehicles, transportation, traffic signals, and industrial automation can now be trained using deep reinforcement learning (DRL) techniques. These applications are designed to take benefit of DRL in order to improve the monitoring as well as measurements in industrial internet of things for automation identification system. The complexity of these environments means that it is more appropriate to use multi-agent systems rather than a single-agent. However, in non-stationary environments multi-agent systems can suffer from increased number of observations, limiting the scalability of algorithms. This study proposes a model to tackle the problem of scalability in DRL algorithms in transportation domain. A partition-based approach is used in the proposed model to reduce the complexity of the environment. This partition-based approach helps agents to stay in their working area. This reduces the complexity of the learning environment and the number of observations for each agent. The proposed model uses generative adversarial imitation learning and behavior cloning, combined with a proximal policy optimization algorithm, for training multiple agents in a dynamic environment. We present a comparison of PPO, soft actor-critic, and our model in reward gathering. Our simulation results show that our model outperforms SAC and PPO in cumulative reward gathering and dramatically improved training multiple agents.