دانلود مقاله ISI انگلیسی شماره 157381
کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
157381 2018 13 صفحه PDF سفارش دهید 10728 کلمه
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عنوان انگلیسی
Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 107, 1 October 2018, Pages 32-44

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چکیده انگلیسی

Mobile crowdsensing is a recent model in which a group of mobile users uses their smart devices such as smartphones or wearable devices to cooperatively perform a large-scale sensing task. In this paper, a novel model will be introduced for recognizing/classifying human activities that were collected from sensor units on the chest, legs, and arms. The proposed model employed the k-Nearest Neighbor (k-NN) classifier which is one of the most common classifiers. k-NN has only one parameter, k, to determine the number of selected nearest neighbors to the test or unknown samples for predicting the class labels of the unknown samples. Searching for the value of k which has a great impact on the classification performance is difficult especially with high dimensional data. This paper employs the Particle Swarm Optimization (PSO) algorithm to search for the optimal value of the k parameter in the k-NN Classifier. This paper shows first experimentally how the PSO in the proposed algorithm searches for the optimal value of k parameter to reduce the misclassification rate of the k-NN classifier. Then, in the second experiment, ten standard datasets are utilized to benchmark the performance of the proposed algorithm. For verification, the results of the PSO-kNN algorithm are compared with two well-known algorithms: Genetic Algorithm (GA) and Ant Bee Colony Optimization (ABCO). In the third experiment, the proposed PSO-kNN algorithm was employed for recognizing human activities. The experimental results proved that the PSO-kNN algorithm is able to find the optimal or near optimal value(s) of the k parameter which enhances the accuracy of k-NN classifier. The results also demonstrated lower error rates compared when GA and ABCO algorithms.

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