No abstract

No abstract

Among various data mining concepts like prediction, clustering, classification, association and outlier discovery, association is a useful technique to extract the interesting relations among data items effectively. Association technique is applied in a number of applications like marketing, education, chemical, bioinformatics, computational linguistics and etc. The important purpose of association is to provide useful information of buying preferences of customers in supermarket in order to increase the sales opportunity, which is called as market- basket analysis. Till now there are many algorithms were developed, but the usage of formal grammars in association rule mining (ARM) is a latest technique to mine required data by means of grammars. In this paper ARM is performed using Context –free Grammar (CFG) – (ARM – Grammar) and the experiments are conducted on MATLAB 2017 software using network dataset, KDDCUP’99. Experimental outcomes prove that the proposed ARM – Grammar is effective than the traditional ARM approach.

Road traffic accidents are a major social concern as well as a crucial issue for the public in recent days due to the risk factors involved. Analysing and identifying the major risk factors of road accident is still a challenging task. In this paper, a fuzzy Context-free Grammar (FCFG)-based association rule mining (ARM) technique is proposed to categorize a heterogeneous road accident dataset into two categories based on the critical factors such as total number of accidents (TA), persons killed (PK) and persons injured (PI). The role of the fuzzy grammar in this paper is to govern the entire algorithm using the prescribed grammar rules to proceed further. The considered road accident dataset does not have class labels; hence there is a need to assign class labels for the available data instance. The accident data with assigned class labels are given as input to K-nearest neighbour (KNN) machine learning algorithm in order to train the classifier for testing purpose. Further, the collected test data from the user are utilized by the KNN classifier for carrying out the performance analysis of the proposed algorithm. The case study is conducted on the National Highway roads, India, to examine the proposed approach. The experimentations are executed for road accident records using MATLAB software and the analysis is made using the following performance measures: accuracy, recall or sensitivity, precision or specificity and F1 score. A comparative study is accomplished with existing algorithms in order to show that the proposed algorithm works with improved accuracy of more than 83%. The results suggested that the road users are responsible for the acceptance or rejection of safe or unsafe roads, respectively.

No abstract

The wireless mesh network (WMN) is a challenging technology that offers high quality services to the end users. With growing demand for real-time services in the wireless networks, quality-of-service-based routing offers vital challenges in WMNs. In this paper, a discrete multi-objective differential evolution (DMODE) approach for finding optimal route from a given source to a destination with multiple and competing objectives is proposed. The objective functions are maximization of packet delivery ratio and minimization of delay. For maintaining good diversity, the concepts of weight mapping crossover (WMX)-based recombination and dynamic crowding distances are implemented in the DMODE algorithm. The simulation is carried out in NS-2 and it is observed that DMODE substantially improves the packet delivery ratio and significantly minimizes the delay for various scenarios. The performance of DMODE, DEPT and NSGA-II is compared with respect to multi-objective performance measures namely as 'spread'. The results demonstrate that DMODE generates true and well-distributed Pareto-optimal solutions for the multi-objective routing problem in a single run.

No abstract

No abstract