Basically, a cooler word depicts the meaning of cool or relief from heat. Same as above a phone cooler cool down the phone or gives relief from heat which is radiated due to continuous use of phone. The working principle of the cooler is so easy to understand. Now- a-days power dissipation levels in mobile phones are continue to increase due to gaming, higher power apps and increased functionality associated with internet. With this power dissipation level, products such as mobile phones will require active cooling to ensure that the comfort and reliability perspectives. The current cooling methodologies of natural convection and radiation limit the power dissipation within a mobile phone to between 1-2 w depending on size. In this paper, the external portable mobile phone cooling system has proposed. This system does not need any changes in designing of mobile phone. It can be used for any types of mobile phone. The proposed cooling system is portable and reliable in cooling phones. The result of this project shows up to phone heating can be controlled in any condition without any external higher power source.It has very less limitations. If the model is connected to phone to provide input power to it the fan and cool it, it will result in drainage of battery of phone. It will not so comfortable to use it for long time less than two hours.
Intrusion Detection System is very important tool for network security. However, Intrusion Detection System suffers from the problem of handling large volume of data and produces high false positive rate. In this paper, a novel Grading method of ensemble has proposed to overcome limitation of intrusion detection system. Partial decision tree (PART), RIpple DOwn Rule (RIDOR) learner and J48 decision tree have used as base classifiers of Grading classifier. Optimzed Genetic Search algorithm have used for selection of features.These three base classifiers have graded using RandomForest decision tree as a Meta classifier. Experimental results show that the proposed Grading method of classification offers accuracies of 81.3742%, 99.9159% and 99.8023% on testing, training datasets and cross validation respectively. It is found that the proposed graded classifier outperform its base classifiers and existing hybrid intrusion detection system in term of accuracy, false positive rate and model building time.
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