Impact of Statistical Intelligent Analysis on Corona Virus Disease

Statistical intelligence formulates the analysis model and reveals the system that can be easily visible and understandable to mankind. On one hand, it will benefit the society to predict the nature or man-created virus environment, and on the other hand, it will solve the problems of intelligent agents' formation with their functionality. It's a well-known fact that the agents are visible and noticeable, and they perform their own assigned task, but their recognition process is delayed. The chapter will focus on the statistical intelligence analysis that includes the properties of the error tolerance, forecasting, and high reliability. The information is always the part of the memory, but the processing methodology that may lead to knowledge is lacking. This may include the logical induction, Bayesian statistics, functional decision theory, value learning, forecasting, etc. Statistics will assist in path selection to formulate the highly adaptive intelligent system with the said functionalities with reduction in the overall cost factors.

Trilateral Location based Maximum Weighted Directive Spanning Tree for Optimal Routing in IoV

Internet of Vehicles (IoV) is one of the developing models in the Vehicular adhoc networks (VANETs) with the vast improvement of communication technologies. In order to improve data transmission among the multiple communities without link breakage, a novel Trilateral Location Identified Maximum Weighted Directive Spanning Tree (TLIMWDST) technique is introduced. The proposed TLIMWDST technique consists of two major phases, namely location identification and optimal path identification to improve the reliability of data transmission from source vehicle to destination vehicle. In the first phase, the location of the neighboring vehicles is identified by applying a trilateration technique. After the location identification, an optimal route path between the source and destination is identified using Maximum Weighted Directive Spanning Tree (MWDST) through the intermediate nodes. The performance of the TLIMWDST technique is assessed through simulation as compared to the previous path selection techniques in terms of different routing metrics such as packet delivery ratio, packet loss rate, end-to-end delay and throughput with respect to the number of data packets.

Path Selection for the Inspection Robot by m-Generalized q-Neutrosophic PROMETHEE Approach

Path planning can be considered the most vital task of the autonomous robot. In this task, selecting an optimal route from the starting to the target position becomes an important problem that must be addressed when multiple competing optimization priorities are considered. Thus, a novel route assessment strategy based on a multi-criteria decision-making approach is proposed. The m-generalized q-neutrosophic PROMETHEE (PROMETHEE-mGqNS) method is applied to aggregate the competing route assessment requirements and choose an optimal route. A case study is investigated to explain the proposed strategy for path planning in a typical environment and indicates the method stability when incomplete input data characteristics are present.

Passenger management strategy and evacuation in subway station under Covid-19

Under the background of Covid-19 sweeping the world, safe and reasonable passenger flow management strategy in subway stations is an effective means to prevent the spread of virus. Based on the social force model and the minimum cost model, the movement and path selection behavior of passengers in the subway station are modeled, and a strategy for passenger flow management to maintain a safe social distance is put forward. Take Qingdao Jinggangshan Road subway station of China as the simulation scene, the validity of the simulation model is verified by comparing the measured value and simulation value of the time required for passengers from getting off the train to the ticket gate. Simulation results indicate that controlling the time interval between incoming passengers at the entrance can effectively control the social distance between passengers and reduce the risk of epidemic infection. By comparing the evacuation process of passengers under different initial densities, it is found that the greater the initial density of passengers, the longer the passengers are at risk social distance. In the process of passenger emergency evacuation, the stairs/escalators and ticket gates are bottleneck areas with high concentration of passenger density, which should be strictly disinfected many times on the basis of strictly checking the health code of incoming passengers and controlling the arrival time interval. The simulation results of this paper verify the harmfulness of passenger emergency evacuation without protective measures, and provide theoretical support for the operation and management of subway station under the epidemic situation.