This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.

The Mobile Adhoc Networks (MANETs) are often visible to the exposed terminal problem and hidden terminal problem, which exist due to non-transitivity in Media Access Control schemes. This affects the utilization of channel and throughput in Media Access Control protocols e.g. Dual Busy Tone Multiple Access protocol (DBTMA). Hence, to improve the fairness and throughput performance of Dual Busy Tone Multiple Access protocol it is very necessary to address the problems associated with hidden and exposed terminals. Hence, in this paper, the Quality of Service is improved by enhancing the capability of Dual Busy Tone Multiple Access protocol for better network service in MANETs. The proposed method uses an improved Dual Busy Tone Multiple Access protocol called Retransmission Dual Busy Tone Multiple Access (RDBTMA) protocol. This is based on two elements namely: busy tones and Ready To Send/Clear to Send (RTS/CTS) dialogues. In addition to this fast retransmission, a strategy is used further to improve its effectiveness. The retransmission strategy is adopted using negative acknowledgement after the collision occurred by the hidden nodes. A hidden node, where the collision occurs at access point listens to the NACK signal and uses the signal to determine the requirement fast retransmission scheme. The proposed method is simulated and compared against existing methods in terms of various network parameters. The result shows that the proposed RDBTMA protocol is effective in terms of improved QoS in terms of network throughput (21.9%), packet delivery ratio (17.8%), 14.9% less packet loss and 38% less route discovery delay than the existing methods.

Everything around us is made up of atoms and molecules. The properties of quantum atoms are sought to understand the behavior of a particular object. But with the advent of research, it was discovered that there is a quantity smaller than the molecular size. The nanoscale measures a fraction of a billionth of a meter. The atom of an object measures 0.1 nm. Since atoms are the building blocks of matter, at the nanoscale one can combine these atoms to create new materials. The proposed model displays the properties of these nano-scale elements in modern medical applications. The nano-scale research of matter is fascinating because it is the basic phase in which atoms are held together. Therefore, by manipulating material at this level, one can create many different types of objects. This proposed model calculates the operation requirements and expects the results. Based on the operational requirements, the proposed model provides the suggestions. This will be helpful for the medical researchers to identify the proper medical treatments based on the microbiological requirements.