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Anm- polar fuzzy set and soft sets are two different tools for representing uncertainty and vagueness. Anm- polar soft fuzzy set is a mapping from parameter set to them- polar fuzzy subsets of the universe. Anm- polar soft fuzzy set theory provides a parameterized point of view for uncertainty modeling and soft computing model. In this paper, we have introduced the notions ofm- polar soft fuzzy bipartite graph, size and degree ofm- polar soft fuzzy bi-partite graph as well as an investigation on buying of residence by considering various parameters. People, while buying residence, have many options. So, to choose the best one, they have to consider many parameters.m- polar soft fuzzy graph is one of the major areas of graph theory, which finds a solution to this problem

In a Wireless Sensor Network (WSN), one of the most important issue is to minimize the energy consumption without losing accuracy during faster data transmission. During information broadcast, message communication is to be sent in an optimized way to increase energy efficiency in the networks. By applying various techniques and methodology in cluster WSN the network lifetime is increased and delay is minimized with the load balanced network. To accomplish load balance, Adelson-Velskii and Landis (AVL) tree rotation clustering algorithm is simulated considering the cluster sensor node. A single large area network is divided into multiple clusters using modified K-means clustering algorithm. Computational complexity is reduced through the construction of Minimum Connected Dominating Set with Multi-hop Information (MCDS-MI) and Bi-Partite Graph (BG) technique. Cluster Head (CH) assortment mechanism is implemented to find maximum cover set count of the sensor nodes. In addition, the enactment of the anticipated design is established through simulations during scalable data transmission in a WSN. Hypothetical investigation and experimental simulations are studied by measuring various performance evaluation metrics namely Virtual Dominators, Size Reduction, Network Lifetime and Residual Energy. The results shows that the proposed MSDS-MI system has maximum reduction in network size of 50%, maximum increase in network lifetime of 60% and saved maximum residual energy consumption of 47.76%. The results are encouraging and our proposed method is found to be more efficient than Connected Dominating Sets (CDS), Pseudo Dominating Sets (PDS), Dynamic Cluster Head Genetic Algorithm (DCH-GA) and Distributed Self-Healing Approach (DSHA).

Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k -cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k -cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.

This paper presents a systematic fault analysis and diagnosis method of a PEM fuel cell control system using a model-based approach. With a model-based approach, it is possible to analyze the causal relationship and effect of probable faults in the system, and to diagnose them under the assumption that the model and the process are similar. With a model-based approach, it is possible to analyze the causal relationship and effect of probable faults in the system and diagnose them under the assumption that the model and the process are similar. In this work, a model-based approach was adopted for fault analysis and diagnosis, and its methods are suggested. A PEM fuel cell is mathematically modelled, analyzed, and verified for the analysis and simulations. Relationships among variables are shown using an incidence matrix and with a Dulmage–Mendelsohn decomposition of the matrix. When it is difficult to detect faults due to a deficient degree of redundancy, a bi-partite graph is used to analyze the effect of faults and to assess the possibility of fault detection through the appropriate redundant sensor placement. Thereafter, residuals are obtained based on analytical redundancies of the system, and a fault signature matrix is subsequently constructed. A fault detection and isolation (FDI) algorithm is developed based on a fault signature matrix that describes the connection between faults and residuals. The simulation results demonstrate the validity and effectiveness of the proposed FDI algorithm for diagnosing faults. With the proposed FDI algorithm, eight faults could be diagnosed by FDI algorithm with given sensors in the system.

In an underwater wireless sensor networks, it is very difficult to communicate data over the 3-D underwater acoustic signal. In acoustic communication, there is no proper transmission between a source node to sink node. In existing work, single-hop clustering protocols were used for direct communication from cluster head to base station. Since direct communication is involved, the transmission range is very large and hence the data communication becomes difficult. In proposed work, additional leverage is achieved by introducing multi-hop clustering for speedy data transmission by selecting Relay Autonomous Underwater Vehicles (Relay-AUV) without delay and maximum channel capacity in acoustic wireless communication. The proposed scheme presents Relay-AUV selection algorithm using Contextual Bandit Bi-partite Graph (CB-BG) formulated by multi-hop data transmission, which provides maximum leverage for energy saving in cluster networks. Theoretical analysis and experimental simulation results are evaluated based on performance metrics such as successive transmission rate, throughput, cost of execution time and packet delivery ratio. The results shows that the proposed CB-BG system has maximum increase in data transmission rate of 58.33%, maximum increase in throughput and network throughput of 54.95%, maximum increase in operational time cost of 27.77% and high packet delivery ratio of 66.66%. The results are encouraging and our proposed method is found to be more efficient than the weight matching algorithms and minimum distances relay policies. The proposed CB-BG mechanism performs faster and reliable communication.