Mushroom Edibility Identification Applying CBR and Ant Lion Techniques in Multi-sensor Environment

Abstract

Wireless sensor networks (WSN) are part of our daily life as they play a vital role in applications of various domains. Energy optimization is a major challenge of WSN as they are operated through the battery. Data exchange is a significant and often performed operation in any WSN which has to be operated with less energy consumption. Bio-inspired clustering protocols are proving a success in minimizing energy consumption and are of current research interest by many researchers. This paper briefs on work carried on the classification of mushroom into edible or non-edible. Since most mushrooms are dangerous to health and may lead to death, henceforth is it is essential to identify the edibility of mushroom. Mushroom features identified by sensor such as ring, odur, spore_print_color, stalk_color_above, stalk_surface_above, and gill size are forwarded to base station (BS). Ant Lion optimization clustering algorithm (ALOC) is adopted in the routing of information to BS. ALOC avoids improper clustering reducing multiple messages at BS. The work is divided into two phases in the initial phase sensing of mushroom features and forwarding to BS is performed through WSN. In the second phase, the decision is taken whether mushroom is edible or not applying class-based association rules (CBA). The simulation results show ALOC is better than LEACH, ABC PSO and MLEACH through evaluation results in terms of network lifetime, energy consumption and retained alive nodes. correlation-based feature selection (CFS) with three filter search techniques: genetic algorithm (GA), evolutionary algorithm (EA), and particle swarm optimization (PSO) at BS as an evaluation method for selection of significant feature selection. CBA rules were generated using these subsets of significant features; hence resulted in a limited number of strong rules which are reliable and sufficient enough to classify the mushroom as edible or not. The patterns and rules generated using the proposed approach avoid the generation of duplicate and irrelevant rules and henceforth simplifies the analysis process using a reliable and interesting set of rules.