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检索条件"任意字段=7th International Conference on Computer and Information Processing Technology, ISCIPT 2022"

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Design and Implementation of Image Copyright Protection System Based on Chinese Cryptographic Algorithms 7

Design and Implementation of Image Copyright Protection Syst...

引用

7th international conference on Signal and Image processing, ICSIP 2022

作者： Cai, Zengyu Si, Yajie Zhang, Jianwei Wang, Jingchao Zhengzhou University of Light Industry School of Computer and Communication Engineering Zhengzhou China Zhengzhou University of Light Industry School of Software Engineering Zhengzhou China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

With the sharp increase in the number of netizens, people's online life is becoming richer and richer, the behavior of stealing pictures on the Internet is more rampant, and online image infringements continue to occur, so the protection of image copyright is particularly important. this paper first introduces the relevant background knowledge of Chinese cryptographic algorithms, image copyright protection and image steganography technology;secondly, the overall design scheme of the system is proposed, including functional design, hierarchical pattern design and system flow design;then, the key technologies of the system implementation are introduced;finally, test and analyze the system. the test results show that the system can secretly write the image copyright information encrypted by the SM2 algorithm into the image through the LSB algorithm, so as to achieve the purpose of image copyright protection without affecting the beauty of the image. © 2022 IEEE.

关键词： Copyrights

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Phishing Website Detection Using Machine Learning 7

Phishing Website Detection Using Machine Learning

引用

7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Mandadi, Adarsh Boppana, Saikiran Ravella, Vishnu Kavitha, R. Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665421683

Phishing is an internet scam in which an attacker sends out fake messages that look to come from a trusted source. A URL or file will be included in the mail, which when clicked will steal personal information or infect a computer with a virus. Traditionally, phishing attempts were carried out through wide-scale spam campaigns that targeted broad groups of people indiscriminately. the goal was to get as many people to click on a link or open an infected file as possible. there are various approaches to detect this type of attack. One of the approaches is machine learning. the URL's received by the user will be given input to the machine learning model then the algorithm will process the input and display the output whether it is phishing or legitimate. there are various ML algorithms like SVM, Neural Networks, Random Forest, Decision Tree, XG boost etc. that can be used to classify these URLs. the proposed approach deals with the Random Forest, Decision Tree classifiers. the proposed approach effectively classified the Phishing and Legitimate URLs with an accuracy of 87.0% and 82.4% for Random Forest and decision tree classifiers respectively. © 2022 IEEE.

关键词： Decision trees

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A Hybrid Model Combined with SVM and CNN for Community Content Classification 23

A Hybrid Model Combined with SVM and CNN for Community Conte...

引用

23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ye, Yukui Xie, Xia Jin, Hai Wang, Duoqiang National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

Community websites bring many conveniences to people, and the classification of community content is playing an important role in website management and information searching. As the carrier of community content, posts are difficult to classify manually. According to the characteristics of community content, a hybrid classification model for machine learning is proposed. this model consists of three steps. Firstly, aiming at the problem of fewer features of posts, a weighted word vector is proposed to enrich the features of posts. Secondly, since the single kernel function of SVM can not completely match all data distributions, a mixed kernel function is employed to improve the model. Finally, in order to fully utilize the powerful feature extraction ability of Convolutional Neural Network as well as the classification ability of SVM, a hybrid model is designed and implemented by replacing softmax layer with SVM classifier. the corresponding experiment results indicate that compared with traditional Convolutional Neural Network, the proposed hybrid model has better performance and stability with classification accuracy improved from 0.9% to 1.4% in general. © 2021 IEEE.

关键词： Support vector machines Smart cities Support vector machine classification Machine learning Feature extraction Stability analysis Data models

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Multi-step stereo matching algorithm based on image fragment and confidence propagation 7

Multi-step stereo matching algorithm based on image fragment...

引用

7th international conference on Communication, Image and Signal processing, CCISP 2022

作者： Li, Yingjiang Deng, Yaping Zhong, Yuzhong Chongqing University of Technology Department of Internet of Things Engineering Chongqing China Computer College Sichuan University Chengdu China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

this paper presents a multi-step stereo matching algorithm that can be applied to multiple scenes. To adapt to different application scenarios, the algorithm divides the stereo matching process into three steps: point, fragment, and plane. First, the texture points of an image are extracted and the stereo matching (point disparity) of these points is performed using the improved self-aware matching measure (SAMM) algorithm. then, according to the edge information of the image, a smooth region is divided into different fragments in the horizontal direction. the disparity estimation of the smooth region (segment disparity) is obtained through the confidence propagation of disparity values of texture points in the fragments. Finally, based on the similarity of plane disparity, a disparity map is generated using the disparity refining algorithm (plane disparity), and a final high-precision disparity value is obtained. the experimental results show that the proposed algorithm has high operational efficiency and accurate disparity estimation. Moreover, the algorithm may be adapted for more application scenarios. © 2022 IEEE.

关键词： Image enhancement

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Global Warming: Temperature Prediction Based on ARIMA 23

Global Warming: Temperature Prediction Based on ARIMA

引用

7th international conference on Innovation in Artificial Intelligence, ICIAI 2023

作者： Li, Qianquan Zhu, Lingyun School of Chemistry Chemical Engineering and Life Sciences Wuhan University of Technology China School of Computer Science and Information Engineering Hubei University China

ISBN: (纸本)9781450398398

In recent years, global warming and the frequent occurrence of extreme weather have raised concerns about global climate change. With the advent of big data and information technology era, it has improved more scientific technical support for temperature prediction. Since temperature data are closely related to time, the time series method can be used to analyze and evaluate temperature data. In this paper, we describe the global temperature trends in the past century and the global temperature trends in the next century. the data are from the official records on BERKELEY EARth from 1900 to 2022. the ARIMA (12,1,5) model is used to predict the global average temperature for the next century (2023-2100). the findings indicate that global temperatures have demonstrated an upward tendency in the past and in the future. Global warming will further intensify. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Global warming

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Image segmentation of persimmon leaf diseases based on UNet 7

Image segmentation of persimmon leaf diseases based on UNet

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Jia, Zhida Shi, Aiju Xie, Guangkuo Mu, Shaomin Shandong Agricultural University College of Information Science and Engineering Taian China Shandong Agricultural University College of Chemistry and Materials Science Taian China ShiKeFeng Ecological Agriculture Engineering Co. Ltd Linyi China

ISBN: (纸本)9781665478571

the size and shape of lesion area of persimmon diseases will vary with the occurrence period and degree. CNN has fixed receptive field when extracting persimmon disease features, which can not adapt to the geometric changes of disease spots, resulting in incomplete disease feature extraction and reducing the segmentation accuracy of persimmon disease images. Deformable convolution can dynamically adjust the size of the receptive field according to the input features, and automatically adapt to the geometric deformation of the lesion. this paper proposes a UNet based on self-attention mechanism and deformable convolution for image segmentation of persimmon leaf disease. With UNet as the basic network, the standard convolution in the down-sampling stage of UNet is replaced by deformable convolution to extract more abundant features, and the self-attention mechanism is used to learn the relationship between the various features to obtain more spatial information and context information. the experimental results show that the mPA and the mIoU of the proposed algorithm are 89.18 % and 83.58 %, its segmentation effect is better than UNet. © 2022 IEEE.

关键词： Convolution

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Detection and Estimation of Diameter of Retinal Vessels 7th

Detection and Estimation of Diameter of Retinal Vessels

引用

7th international conference on computer, Communication and Signal processing, ICCCSP 2023

作者： Jamwal, Abhinav Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9783031398100

the change in retinal vessel diameter is a crucial measure of disorders, including hypertension, high blood pressure, and diabetes. Abnormalities in the retinal vasculatures can signify a spectrum of diseases. In retinal fundus images, providing an automatically estimated diameter of blood vessels may aid clinicians in diagnosing diseases like hypertensive retinopathy, which can even lead to blindness. therefore, automated diagnosis of these diseases requires accurate measurements of vascular diameters. In this research work, this paper have investigated three methods for the estimation of the diameter of the retinal blood vessel. Two publicly available datasets such as Central Light Reflex Image Set (CLRIS) and Vascular Disease Image Set (VDIS) of the Retinal Vessel Image Set for Estimation of Width (REVIEW) database have been used for the comparison of estimated diameter of the retinal vessel. the performance of these three algorithms is compared against the available ground truth average diameter for both CLRIS and VDIS. It was found that the estimated diameter is closer to the available ground truth using morphological-based and center line-based, respectively. However, the estimated diameter using the line integral-based method is significantly different from the ground truth for both datasets. © IFIP international Federation for information processing 2023.

关键词： Image segmentation

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Research of EEG Signal Analysis Method Based on Fusion of Riemannian Space and Convolutional Neural Network 7

Research of EEG Signal Analysis Method Based on Fusion of Ri...

引用

7th international conference on Signal and Image processing, ICSIP 2022

作者： Nuo, Gao Kai, Zhao Shandong Jianzhu University Department of Information & Electrical Engineering Jinan China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Brain computer interface (BCI) system based on motor imagery EEG (MI-EEG), as a deeply concerned human-computer interaction mode, can realize the information exchange between human brain and the outside world. the key to the performance of BCI system lies in electroencephalogram (EEG) processing and feature extraction. In order to improve the performance of BCI, an algorithm based on Riemann space processing EEG signal and convolutional neural network is proposed in this paper. Firstly, the covariance matrix of the symmetric positive definite matrix is selected as the descriptor in the pre-processed raw EEG signal, and the EEG signal described in the spatio-temporal domain in Euclidean space is reasonably transformed to Riemannian space, then the infinite-dimensional symmetric positive definite matrix is obtained by kernel mapping, and the low-dimensional vector estimation of the infinite-dimensional symmetric positive definite matrix is used to obtain a low-dimensional efficient EEG signal feature set using a specific feature mapping, and finally a convolutional neural network (CNN) classifier is constructed according to the feature set Finally, a CNN classifier is constructed based on the feature set for classification. Experimental tests of the method using publicly available datasets show that the algorithm can effectively identify EEG signals with high classification accuracy and short running time. © 2022 IEEE.

关键词： Electroencephalography

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Fake News Detection Model for Regional Language 7

Fake News Detection Model for Regional Language

引用

7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Nair, Jayashree Akhil, S.S. Harisankar, V. Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9781665421683

Social media has expanded far and wide in the past few years, which has led to the ease of circulating any kind of news and posts all over the network. the work that we are pursuing currently focuses on the prevention of the circulation of large, misinformed news over the network. the different machine and deep learning techniques incorporated with certain Natural Language processing techniques can result in achieving such good models to filter the fake news and real news apart from themselves. the social media spectrum is active in other low-resource language domains like Malayalam exactly how it prevails in other languages which currently aids in the inflation of rumors and false accusations among the masses. the main objective is to classify the incoming Malayalam contextual news and other rumors to aid humans in fast-tracking the manual verification or detection process of the news. the introduction of the classifier with an additional front-end interface to assist the people in the time of a crisis would help the crowd to a great degree with dynamic regular feeding of real-time data to the language model. © 2022 IEEE.

关键词： Data mining

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Multi-Sensor Data Fusion for Detection of Sensor Drifts in a Landslide Monitoring System 7

Multi-Sensor Data Fusion for Detection of Sensor Drifts in a...

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7th international conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, NCON 2022

作者： Smunyahirun, Nathnaree Karnjana, Jessada Laitrakun, Seksan Nishio, Takayuki Deepaisarn, Somrudee Sirindhorn International Institute of Technology School of ICT Thailand NSTDA National Electronics and Computer Technology Center Thailand Tokyo Institute of Technology Department of Information and Communications Engineering Japan

ISBN: (纸本)9781665495103

this paper aims to extend the lifetime of polymer dielectric-based humidity sensors used in landslide monitoring systems by mathematically adjusting the sensor's drifting value as time passes. therefore, a strategy based on nested Kalman filters, cosine similarity, and Euclidean distance was proposed in this paper. An outer Kalman filter is used top redict humidity values, with the prediction based on an inner Kalman filter that fuses data from surrounding sensors. To adjust the process noise covariance of the nested Kalman filter, cosine similarity and Euclidean distance are used. When the sensor is in normal conditions, the process noise covariance remains constant;however, when the sensor is drifting, the process noise covariance changes depending on the angle and distance between the value from surrounding sensors and the drift. the proposed method could reduce the drifting value error by 14.85 percent, according to the experimental results. © 2022 IEEE.

关键词： Kalman filters

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