The Multi Factor-Naive Bayes classifier based recommendation system is analyzed with respect to the traditional KNN classifier based recommendation system. The classification of the web usage data is done on the basis of the keyword name, keyword count, inbound links and age group of the users. Whereas, in traditional KNN the URL was the only factor that was considered for the purpose of classification. The performance evaluation is done in the terms of RMSE, Error Rate, Accuracy Rate and Precision. The MF-NB is observed to be outperforming the KNN classifier in all respective terms.

Vibration induced in the vehicle affects the performance of the driver or rider because of reduction in comfort & safety level. In case of motorcycle, poor suspensions system & uneven road condition make driving difficult, these also introduces vibration. The vibrations are directly transferred to the body through the seat & handlebar. It has been seen that handlebar vibrations are more serious & creates physical problem to the rider. Particle damping technology is a derivative of impact damping with several advantages. Particle damping is the use of particles moving freely in a cavity to produce a damping effect. In this paper a passive damper using particle damping technique is designed and developed to reduce the hand arm vibrations (HAV). The experiments are planned and conducted using DOE. Optimum configuration of particle damper has been derived through this research work. Experimental tests shows by employing a particle damper the vibration amplitude is minimized significantly.