Memetic Algorithms: A Contemporary Introduction

Cotta, Carlos; Gallardo, José E.; Mathieson, Luke; Moscato, Pablo

doi:10.1002/047134608x.w8330

Cited by 3 publications

(1 citation statement)

References 172 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional Gabor filter is slower when processing pictures. To provide the quick response, WiVi uses the Memetic algorithm [16] to design an improved Gabor filter, M-Gabor. Memetic algorithm is a hybrid computing intelligent optimization method framework.…”

Section: ) Automatic Feature Extractionmentioning

confidence: 99%

WiVi: A Ubiquitous Violence Detection System With Commercial WiFi Devices

Zhang

Ruan

2020

IEEE Access

View full text Add to dashboard Cite

School violent behaviors is a serious problem that greatly affects the healthy growth of the youth and the children. Current prevention measures mainly depend on propaganda and self-report. So far, there is still no effective solution that can automatically detect the violent behaviors. In this paper, we take the first attempt to build a ubiquitous passive violence detection system, WiVi, based on the commercial WiFi infrastructure. To capture the patterns of the complicated violent behaviors actions, besides the timeseries features used in existing activity recognition works, WiVi also leverages the correlated features extracted from combined subcarriers, to take fully advantages of Channel State Information. WiVi integrates a feature fusion method to select the appropriate features for the classification model in different scenarios. We implement and evaluate WiVi in various real-world environments. The experimental results show that the recall and specificity that WiVi can achieve 93.46% and 93.57% respectively. INDEX TERMS Violent behaviors, ubiquitous detection, channel state information (CSI). I. INTRODUCTIONSchool violent behaviors has becoming a major problem that seriously affects the physical and mental health of the youth all over the world. According to the National Center for Education Statistics of USA, 28% of the students in graders 6-12 experienced bullying [1]. Violent behaviors usually involves violence that hurts the youth's physical health. Research statistics [2] show that 32.4% of the middle school students experienced bullying with pushing or shoving, and 29.2% of them experienced hitting, slapping, or kicking. Violent behaviors can also cause problems such as emotional loss and insomnia, and even depression and suicide. The study [3] shows that the probabilities of depression and suicide for the victims being bullied are 4.8 times and 18.5 times larger than the ordinary people. Given the serious impacts of bullying, many countries have enacted the law of antibullying. However, few of the victims being bullied report to the school due to the fear of retaliation [4]. Hence, a timely and automatic violent behaviors detection method is urgently needed to prevent the youth from suffering from bullying.The associate editor coordinating the review of this manuscript and approving it for publication was Alessandro Pozzebon.

show abstract

Section: ) Automatic Feature Extractionmentioning

confidence: 99%