A Novel Approach to Extract High Utility Itemsets from Distributed Databases

Traditional approaches in data mining focus on support and confidence measures which are just statistics based. Support and confidence measures which are based on the frequency count of the items enable us to derive the frequent itemsets. The frequency of the items as a single factor does not represent the interestingness of the items. To enhance the process of data mining tasks based on the value of the product, several researches were conducted. It resulted in utility mining which is an emerging field of research in data mining. In the recent years various data mining approaches have been implemented in order to find the high utility itemsets. The main objective of utility mining is to identify the itemsets with highest utilities, by considering the subjectively defined utility values, as set by the user. Existing methods based on utility mining concept focus on centralized systems where the data and associated processing is pertained to a particular location. As a further step ahead we try to implement the utility mining concept in a distributed environment. In this approach we use a sophisticated way of mining high utility itemsets using a Fast Utility Mining (FUM) algorithm

An Enhanced Localization Approach for Energy Conservation in Wireless Sensor Network with Q Deep Learning Algorithm

Wireless Sensor Networks (WSN) have distributed a collection of tiny sensor nodes deployed randomly in the given symmetry environment to sense natural phenomena. The sensed data are disseminated symmetrically to the control station using multi-hop communication. In WSN, the energy conservation during node coverage plays a major role in detecting node failure and providing efficient and symmetrical data transmission to the nodes of WSN. Using the cluster method and efficient localization techniques, the nodes are grouped and the precise location of the nodes is identified to establish the connection with the nearby nodes in the case of node failure. The location accuracy is achieved using the localization estimation of the anchor nodes and the nearest hop node distance estimation using the received signal strength measurement. The node optimization can be performed efficiently by the accurate estimation of the localization of the node. To optimize the node coverage and provide energy efficient and symmetrical localization among the nodes, in this paper, a cluster-based routing protocol and a novel bio-inspired algorithm, namely, Modified Bat for Node Optimization (MB−NO), to localize and optimize the unknown nodes along with the reinforcement-based Q learning algorithm is proposed with the motive of increasing the accuracy estimation between anchor nodes and the other neighbor nodes, with the objective function to optimize and improve the nodes’ coverage among the network’s nodes in order to increase the nodes’ localization accuracy. The distance metrics between the anchor nodes and other neighbor nodes have an estimated symmetry with three node positions, namely C-shape, S-shape and H-shape, using the Q learning algorithm. The proposed algorithm is implemented using the NS3 simulator. The simulation results show that the accuracy and precision of the proposed algorithm are achieved at 98% in the node coverage optimization with reduced Mean Localization Error (MLE) and computational process time compared with other bio-inspired algorithms, such as Artificial Bee Colony optimization and Genetic Algorithms

Self-Assembled Molecular Hybrids of CoS-DNA for Enhanced Water Oxidation with Low Cobalt Content

Water oxidation in alkaline medium was efficiently catalyzed by the self-assembled molecular hybrids of CoS-DNA that had 20 times lower Co loading than the commonly used loading. The morphological outcome was directed by varying the molar ratio of metal precursor Co­(Ac)₂ and DNA and three different sets of CoS-DNA molecular hybrids, viz. CoS-DNA(0.036), CoS-DNA(0.06), and CoS-DNA(0.084) were prepared. These morphologically distinct hybrids had shown similar electrocatalytic behavior, because of the fact that they all contained the same cobalt content. The CoS-DNA(0.036), CoS-DNA(0.06), and CoS-DNA(0.084) required very low overpotentials of 350, 364, and 373 mV at a current density of 10 mA cm^–2 (1 M KOH), respectively. The advantages of lower overpotential, lower Tafel slope (42.7 mV dec^–1), high Faradaic efficiency (90.28%), high stability and reproducibility after all, with a lower cobalt loading, have certainly shown the worth of these molecular hybrids in large-scale water oxidation. Moreover, since DNA itself a good binder, CoS-DNA molecular hybrids were directly casted on substrate electrodes and used after drying. It also showed minimum intrinsic resistance as DNA is a good ionic and electronic conductor. Besides, the present method may also be extended for the preparation of other active electrocatalysts for water splitting