Smile detection in real time video is an interesting problem with many potential applications. This paper is intended to implement a real time smile detection from video using Haar Classifiers through Raspberry Pi BCM2835 processor which is a combination of SoC with GPU based Architecture. For capturing video we used Raspberry Pi Camera Board of 5MP and which plugs directly into the CSI connector on the Raspberry Pi. Computer vision OpenCV libraries with python IDE is used for face detection and smile detection through linux based raspbian operating system. Frame rates of various video resolutions during smile detection in Raspberry Pi are also observed. The present method can be used in low cost robotic computer vision applications, human computer interaction (HCI) and in education as well because cheap and small hardware used.
At present, ensuring the security of MANET is a highly challenging chore due to the dynamic topology of the network. Hence, most of the existing Frameworks for Intrusion Detection Systems (IDS) seek to predict the attacks by utilising the clustering and classification mechanisms. Still, they face the major problems of reduced convergence speed, high error rate and increased complexity in the algorithm design. Therefore, this paper intends to utilise integrated optimisation and classification methods for accurately predicting the classified label. This framework comprises the working modules of preprocessing, feature extraction, optimisation and classification. Initially, the input datasets are preprocessed by filling the missing values, and normalising the redundant contents. After that, the Principal Component Analysis (PCA) technique is employed for selecting the set of features used for improving the classification performance. Consequently, the Grey Wolf Optimisation (GWO) technique is utilised for selecting the most optimal features based on the best fitness value, which reduces the overall complexity of IDS. Finally, the Deterministic Convolutional Neural Network (DCNN) technique is utilised for predicting whether the classified outcomes are normal or attacks. For validating the results, various performance metrics have been assessed during the analysis, and the obtained results are compared with the recent state-of-the-art models.
The Editor-in-Chief and the publisher have retracted this article. This article was submitted to be part of a guestedited issue. An investigation concluded that the editorial process of this guest-edited issue was compromised by a third party and that the peer review process has been manipulated. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.