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Enhancing Security in Smart Homes with IoT Using Logit-Boosted Techniques 6

Enhancing Security in Smart Homes with IoT Using Logit-Boost...

引用

6th International Conference on Computer Information Science and Application Technology, CISAT 2023

作者： Rahim, Asif Zhong, Yanru Ahmad, Tariq School of Computer and Information Security Guilin university of Electronic Technology Guilin54001 China Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin541001 China School of Information and Communication Engineering Guilin University of Electronic Technology Guilin541001 China

ISBN: (纸本)9781510668546

The utilization and advancement of Internet of Things (IoT) gadgets have noticeably increased in recent times. Various sectors, including intelligent homes, healthcare, sports analysis, and different industries, use IoT devices. Traffic is a crucial aspect of IoT devices, and it differs from conventional device traffic in several ways. This investigation employed 41 Internet of Things (IoT) devices. A machine learning model was developed that can classify IoT devices into multiple categories based on 13 different network traffic features. The raw data that was gathered was preprocessed using techniques like standardization and scaling techniques of the dataset. Algorithms for feature selecting can be utilized to extract characteristics from textual data. Following stratification, The dataset contains 106,234 sets of features that were utilized to enhance the predictive model. This research employed various evaluation metrics to demonstrate the effectiveness of the logistic-boosted algorithms. In this research, collaborative machine learning methods were utilized as a starting point to develop an intrusion detection system (IDS) that can identify abnormal network traffic. The principal aim of this investigation is to identify attacks and abnormalities within a Smart Home IoT setting. We have introduced a new technique for constructing Logit-Boosted algorithm, namely Logi-CatBoost Classifier. Once the logitboosted algorithm was employed to classify the dataset, Logi-CBC performed the best, with an accuracy of 88.70%. We suggested Logi-CBC algorithm achieved the top precision among previously used Logit-Boosted algorithms in past studies on the same dataset. © 2023 SPIE.

关键词： Internet of things

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Comparison of regressors on 3D visual discomfort prediction

Comparison of regressors on 3D visual discomfort prediction

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Runfeng Huang Jun Zhou Xiao Gu Ya Zhang Alan C. Bovik Shanghai Key Laboratory of Digital Media Processing and Transrnissions Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Laboratory for Image and Video Engineering University of Texas at Austin Austin USA

ISBN: (纸本)9781467390453

Most stereoscopic 3D (S3D) image visual discomfort predictors use the Support Vector Regressor (SVR) as the regression model. However, there are other good regression models such as the Random Forests (RF) and Gradient Boost Regression Tree (GBRT). Here we study the efficacy of these regression models for S3D image visual discomfort prediction. We deployed several regression models to predict the visual discomfort scores on S3D images using three kinds of features extracted from the images in the IEEE-SA and EPFL S3D image databases. So the prediction performance was evaluated to compare the performance of the different regression models. We also studied the issue of over-fitting which can affect model performance. The comparison results show that it is feasible and reliable to apply Random Forests or GBRT for S3D visual discomfort prediction with better performance than SVR.

关键词： Visualization Feature extraction Kernel Predictive models Vegetation Radio frequency Analytical models

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Design and Implementation of Embedded Multimedia Surveillance System

Design and Implementation of Embedded Multimedia Surveillanc...

引用

International Workshop on Knowledge Discovery and Data Mining (WKDD)

作者： Yan Liu Ren-Fa Li Cheng Xu Fei Yu School of Computer and Communication Hunan University Changsha China Guangdong Province Key Laboratory of Electronic Commerce Market Application Technology Guangdong University of Business Studies Guangzhou China Provincial Key Laboratory of Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing China

Due to the thrive of networking multimedia, artificial intelligence and embedded system, video surveillance system is evolved from computer-based to embedded-based. This paper presents an intelligent multimedia surveillance system based on embedded video server. Design and implementation issues of this surveillance system are discussed including system architecture, hardware design and software framework. Moreover, practical applications show that our framework can suitable for commercial applications.

关键词： Multimedia systems Artificial intelligence Application software Embedded system Video surveillance Computer networks Embedded computing Intelligent systems Network servers Computer architecture

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Study of Double SMO Algorithm Based on Attributes Reduction

Study of Double SMO Algorithm Based on Attributes Reduction

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6th International Symposium on Neural Networks

作者： Chen, Chen Hong, Liu Song, Haigang Chen, Xueguang Hou, TieMin Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Bas Res Serv Ministry Sci Beijing 100862 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9783642015090

To solve the classification problem in data mining, this paper proposes double SMO algorithm based on attributes reduction. Firstly attributes reduction deletes irrelevant attributes (or dimensions) to reduce data amount, consequently the total calculation is reduced, the training speed is fastened and Classification mode is easy to understand. Secondly applying SMO algorithm on the sampling dataset to get the approximate separating hyperplane, and then we obtain all the Support vectors of original dataset. Finally again use SMO algorithm on the support vectors to get the final separating hyperplane. It is shown in the experiments that the algorithm reduces the memory space, effectively avoids the noise points' effect on the final separating hyperplane and the precision of the algorithm is better than Decision Tree, Bayesian and Neural Network.

关键词： Data mining Support vector machine Attribute reduction Training algorithm

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Denoising Diffusion Tensor images with Shearlet

Denoising Diffusion Tensor Images with Shearlet

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： Xiangfen Zhang Bao-Liang Lu Yan Ma Xiaozhong Xu Fangfang Wei Wenjie Xu Institute of Intelligent Computing & Image Processing College of Mechanical and Electronic EngineeringShanghai Normal University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiaotong University MOE-Microsoft Key Lab for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781467321969

Diffusion tensor imaging （DTI） is known to be the best non-invasive imaging modality in providing anatomical information as white-matter fiber bundles. However, the Gaussian noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many denoising methods have been presented. In this paper, a shearlet based denosing strategy is introduced. To evaluate the efficiency of the proposed shearlet based denoising method in accounting for the Gaussian noise introduced into the images, the peak to peak signal-to-noise ratio （PSNR）, signal-to-mean squared error ratio （SMSE） and edge keeping index （Beta） metrics are adopted. The experiment results acquired from both the synthetic and real data indicate the good performance of our proposed filter.

关键词： diffusion tensor imaging shearlet transform wavelet transform PSNR SMSE denoising

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Deep feature guided image retargeting 34

Deep feature guided image retargeting

引用

34th IEEE International Conference on Visual communications and image processing, VCIP 2019

作者： Wu, Jinan Xie, Rong Song, Li Liu, Bo Shanghai Jiao Tong University Institute of Image Communication and Network Engineering China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China University of Technology School of Computer Science Sydney Australia

ISBN: (纸本)9781728137230

image retargeting is the technique to display images via devices with various aspect ratios and sizes. Traditional content-Aware retargeting methods rely on low-level features to predict pixel-wise importance and can hardly preserve both the structure lines and salient regions of the source image. To address this problem, we propose a novel adaptive image warping approach which integrates with deep convolutional neural network. In the proposed method, a visual importance map and a foreground mask map are generated by a pre-Trained network. The two maps and other constraints guide the warping process to yield retargeted results with less distortions. Extensive experiments in terms of visual quality and a user study are carried out on the widely used RetargetMe dataset. Experimental results show that our method outperforms current state-of-Art image retargeting methods. © 2019 IEEE.

关键词： Aspect ratio

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A new roadmap constitution method based on dividing grid and level-set

A new roadmap constitution method based on dividing grid and...

引用

International Symposium on Intelligence Information processing and Trusted Computing

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Information Engineering of Hubei University for Nationalities Enshi Hubei China Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies HUST Wuhan China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China HUST Wuhan China

ISBN: (纸本)9780769541969

Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of start and goal of route, so the roadmap can not be reused when anyone of start and goal changed. Absence of reusability reduces the efficiency of route planning by using roadmap. A new roadmap constitution method was proposed in this paper, which is build on dividing the 2D configuration space into multi-scale grids and setting nodes of roadmap on grid boundary using Level-Set method, same method was used to construct the map cells of roadmap between any two nodes lays on same grid . The roadmap constituted with new method can be reused when the start or goal was changed;it is efficient for replanning problem. © 2010 IEEE.

关键词： Reusability

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Skeleton-based fast path planning for UAV

Skeleton-based fast path planning for UAV

引用

MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Liu, Xin Zhou, Chengping Ding, Mingyue Cai, Chao Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478092

A new path planning method for UAV in static workspace is presented. The method can find a nearly optimal path in short time which satisfies the UAV kinematic constraints. The method makes use of the skeletons to construct the graph of the planning space considering the configuration of the obstacles and utilizes the graph to find a shortest collision-free path, and a novel technique is utilized to convert the free path into a feasible path. The method can be applied to different applications and easy to be implemented. Experimental results showed that the path planning can be done in a fraction of second on a contemporary workstation (2-3 seconds) under the condition of satisfying the kinematic constraints. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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Attention Electroencephalogram Analysis based on Jensen-Shannon Divergence Analysis

Attention Electroencephalogram Analysis based on Jensen-Shan...

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2013 International Conference on Bio-Medical Materials and Engineering(ICBME 2013)

作者： Zhengzhong Zheng Junchang Zhao Xiaolin Huang Jun Wang Image Processing and Image Communications Key Lab College of Geo & Bio InformationNanjing Univ of Posts & Telecomm Institute of Biomedical Electronic Engineering School of Electronic Science and EngineeringNanjing University

In this paper, the complexity analysis method based on Jensen-Shannon Divergence was used to calculate the complexity of the Idle states, the close eyes states and the count numbers states electroencephalogram The study found that the JSD value of close eyes states EEG was highest, followed by that of the Idle states EEG, and that of the count numbers states EEG was minimum The result can be used to assisted clinical diagnosis

关键词： Complexity electroencephalogram Jensen-Shannon Divergence

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Coupling analysis of epileptic EEG signals based on the multiscale mutual model entropy

Coupling analysis of epileptic EEG signals based on the mult...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Ning Ji Jia-Fei Dai Jun Wang Feng-Zhen Hou Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. Nanjing General Hospital of Nanjing Military Command School of Science China Pharmaceutical University

ISBN: (纸本)9781510823808

The Multiscale Mutual Model Entropy algorithm is presented to quantify the coupling degree between two EEG time series collected at the same time on different *** extracted the characteristics of EEG signals from the healthy and epileptics based on the *** results show that the entropy value of healthy people is higher than that of *** with the increase of scale,the difference in entropy value between them is more *** indicates that Multiscale Mutual Entropy can distinguish the coupling difference between normal samples and case samples,which is significant for the clinical pathological assessment and brain disease diagnosis.

关键词： multiscale epileptic coupling mutual model entropy

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