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A Deep Learning Model of Traffic Signs in Panoramic Images Detection

Intelligent Automation & Soft Computing 2023年第7期37卷 401-418页

作者： Kha Tu Huynh Thi Phuong Linh Le Muhammad Arif thien khai tran International University Ho Chi Minh CityVietnam-Vietnam National UniversityHo Chi Minh City700000Vietnam Department of Computer Science The Superior UniversityLahorePakistan Ho Chi Minh City University of Foreign Languages and Information Technology Ho Chi Minh City700000Vietnam

To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the *** ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic *** accomplish this goal,the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for com-prehensive traffic sign classification and Mask Region-based Convolutional Neural Networks(R-CNN)implementation for identifying and extracting signs in panoramic *** augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded,and considerably minimize the rates of discovering the extra *** proposed model is tested on both the testing dataset and the actual images and gets 94.5%of the correct signs recognition rate,the classification rate of those signs discovered was 99.41%and the rate of false signs was only around 0.11.

关键词： Deep learning convolutional neural network Mask R-CNN traffic signs detection

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A Deep Trash Classification Model on Raspberry Pi 4

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Intelligent Automation & Soft Computing 2023年第2期35卷 2479-2491页

作者： thien khai tran Kha Tu Huynh Dac-Nhuong Le Muhammad Arif Hoa Minh Dinh Ho Chi Minh City University of Foreign Languages and Information Technology Ho Chi Minh City700000Vietnam International University Ho Chi Minh CityVietnam-Vietnam National UniversityHo Chi Minh City70000Vietnam Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam Department of Computer Science and Information Technology University of LahoreLahorePakistan

Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most *** classi-ﬁcation from a collection of trash images can limit the overloading of garbage dis-posal systems and efﬁciently promote recycling activities;thus,development of such a classiﬁcation system is topical and *** paper proposed an effective trash classiﬁcation system that relies on a classiﬁcation module embedded in a hard-ware setup to classify trash in real *** image dataset isﬁrst augmented to enhance the images before classifying them as either inorganic or organic *** deep learning–based ResNet-50 model,an improved version of the ResNet model,is used to classify trash from the dataset of trash *** experimental results,which are tested both on the dataset and in real time,show that ResNet-50 had an average accuracy of 96%,higher than that of related ***,integrating the classiﬁcation module into a Raspberry Pi computer,which con-trolled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste,created a complete trash classiﬁcation *** proves the efﬁciency and high applicability of the proposed system.

关键词： Trash classiﬁcation ResNet raspberry pi internet of things(IoT) deep learning

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Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

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Computers, Materials & Continua 2022年第7期72卷 1357-1372页

作者： Nguyen A.Tuan D.Akila Souvik Pal Bikramjit Sarkar thien khai tran G.Mothilal Nehru Dac-Nhuong Le Faculty of Information Technology Ho Chi Minh City University of Foreign Languages-Information TechnologyHo Chi Minh City70000Vietnam Department of Information Technology Vels Institute of ScienceTechnology&Advanced StudiesChennai600117India Department of Computer Science and Engineering Sister Nivedita UniversityKolkata700156India Department of Computer Science and Engineering JIS College of EngineeringNadia741235India Institute of Research and Development Duy Tan UniversityDanang550000Vietnam School of Computer Science Duy Tan UniversityDanang550000Vietnam

This article presents a new scheme for dynamic data optimization in IoT(Internet of Things)-assisted sensor *** various components of IoT assisted cloud platform are *** addition,a new architecture for IoT assisted sensor networks is ***,a model for data optimization in IoT assisted sensor networks is proposed.A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization(MIDDO)algorithm for IoT assisted sensor network is proposed in this *** proposed algorithm considers every node data and utilized membership function for the optimized data *** proposed framework is compared with two stage optimization,dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio,coverage ratio and sensing *** optimization was performed based on the availability of cloud resource,sensor energy,data flow volume and the centroid of each *** was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%,reliability ratio of 94.74%,coverage ratio of 85.75%and sensing error of 0.154.

关键词： Data optimization IoT sensor network reliability membership sensor energy

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