Avleen Kaur Malhi

Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring

Air pollutants such as benzene (

Genetic-based framework for prevention of masquerade and DDoS attacks in vehicular ad-hocnetworks

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Fuzzy-based trust prediction for effective coordination in vehicular ad hoc networks

C6H6) have accelerated the rate of cancer among human beings. Currently, atmospheric contamination is measured using spatially separated networks with limited sensors. However, the expenses involving multiple sensors with varying sizes limit the operational efficiency. Therefore, in this paper, a novel multi-objective regression model is proposed to predict benzene concentration in the ambient air pollution data, without need to deploy actual sensors for benzene detection. It is possible because there is a relation among various atmospheric gasses and thus regression can be performed to measure

Decision inference system for misbehavior detection in VANETs

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Improved Particle Swarm Optimization Based Adaptive Neuro‐Fuzzy Inference System for Benzene Detection

C6H6 if the concentration level of other gasses is known. Proposed technique utilizes adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict