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Light bottle transformer based large scale point cloud classification

Optoelectronics Letters 2023年第6期19卷 377-384页

作者： XIE En ZHANG Zhiyong ZHANG Guodao CHEN Pingkuo GE Yisu College of Computer Information and Engineering Nanchang Institute of TechnologyNanchang 330044China School of Information Engineering East China University of TechnologyNanchang 330000China Department of Digital Media Technology Hangzhou Dianzi UniversityHangzhou 310023China Great Bay University Dongguan 523000China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325100China

With the rapid development of computer vision,point clouds technique was widely used in practical applications,such as obstacle detection,roadside detection,smart city construction,***,how to efficiently identify the large scale point clouds is still an open *** relieving the large computation consumption and low accuracy problem in point cloud classification,a large scale point cloud classification framework based on light bottle transformer(light-Bot Net)is ***,the two-dimensional(2D)and three-dimensional(3D)feature values of large scale point cloud were extracted for constructing point cloud feature images,which employed the prior knowledge to normalize the point cloud ***,the feature images are input to the classification network,and the light-Bot Net network is applied for point cloud *** is an interesting attempt to combine the traditional image features with the transformer *** proving the performance of the proposed method,the large scale point cloud benchmark Oakland 3D is *** the experiments,the proposed method achieved 98.1%accuracy on the Oakland 3D *** with the other methods,it can both reduce the memory consumption and improve the classification accuracy in large scale point cloud classification.

关键词： computer cloud point

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A Scheme for Authentication and Traceability of 5G Mobile Terminal Based on IPv6 and Blockchain 16

A Scheme for Authentication and Traceability of 5G Mobile Te...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Ji, Xinsheng Zhao, Ying Liao, Xingxing Yang, Jie Wang, Chunxiao Tsinghua University Department of Computer Science and Technology Beijing100084 China National Digital Switching System Engineering & Technological R&d Center Zhengzhou450002 China Purple Mountain Laboratories Nanjing211111 China School of Cyber Science and Engineering Southeast University Nanjing211102 China

ISBN: (纸本)9798350390643

To satisfy the service-side requirements for network layer authentication and traceability of users, this paper proposes a method for mobile terminal authentication and identity tracing based on IPv6 and blockchain. Our method introduces a new verification function (VEF) to verify the interface identification between the terminal and the 5G core network (5GC), thereby effectively enhancing the security of terminal access. Furthermore, considering the privacy protection of user identity information to prevent attackers from deducing encrypted content, it is necessary to periodically update encryption keys. Focusing on the practical deployment of VEFs on a network-wide scale and server-proximal for authentication and traceability, a time-varying key sharing scheme based on blockchain is proposed. The experimental section analyzes the communication performance and security of the proposed scheme, showing that our scheme does not significantly degrade the original network performance while providing substantial security gains. © 2024 IEEE.

关键词： Authentication Protocol

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Sentiment Analysis on the Elevated Expressway in Bangladesh: A Machine Learning and Deep Learning Approach 2

Sentiment Analysis on the Elevated Expressway in Bangladesh:...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Jeba, Samiha Maisha Khan, Rajib Ahmed, Md Shoaib Aurpa, Tanjim Taharat Dash, Satya Ranjan International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Boise State University Department of Computer Science Idaho Boise United States Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Educational Technology Bangladesh School of Computer Applications Kiit University Odisha India

ISBN: (纸本)9798331517984

User comments on social media or any video streaming platforms show how people feel about something, including different opinions, feelings, and problems of people interacting with it. It can assist people and organizations in making wellinformed choices by offering insightful information on how people feel about a topic or issue. The Elevated Expressway is a crucial infrastructure in Bangladesh and a controversial project that has sparked public debate and criticism. The 46.73 km road project connecting Hazrat Shahjalal International Airport to Kutubkhali on the Dhaka-Chittagong Highway has eased the commute time from the Airport to Farmgate from over an hour to 10 minutes. Regarding this project, People's comments on YouTube as a digital platform for voicing their opinions and contributing to a shared narrative that goes beyond traditional media. In this work, we examine users' sentiments about Elevated Expressway from YouTube comments, employing various Machine Learning (ML) algorithms and Deep Learning (ML) architecture. Based on these analyses, the BERT architecture arises as the most satisfying model, with an accuracy of 84.34% and a macro f1 score of 80.14% for classifying public sentiment as positive, negative, or neutral. © 2024 IEEE.

关键词： Video streaming

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Privacy Data Management Mechanism Based on Blockchain and Federated Learning

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computers, Materials & Continua 2023年第1期74卷 37-53页

作者： Mingsen Mo Shan Ji Xiaowan Wang Ghulam Mohiuddin Yongjun Ren Engineering Research Center of Digital Forensics of Ministry of Education School of ComputerNanjing University of Information Science&TechnologyNanjing210044China Zhengde Polytechnic Nanjing211106China Xi’an University of Posts&Telecommunications Xi’an710061China Department of Cyber Security at VaporVM Abu Dhabi999041United Arab Emirates

Due to the extensive use of various intelligent terminals and the popularity of network social tools,a large amount of data in the field of medical *** to manage these massive data safely and reliably has become an important challenge for the medical network *** paper proposes a data management framework of medical network community based on Consortium Blockchain(CB)and Federated learning(FL),which realizes the data security sharing between medical institutions and research *** this framework,the data security sharing mechanism of medical network community based on smart contract and the data privacy protection mechanism based on FL and alliance chain are designed to ensure the security of data and the privacy of important data in medical network community,*** intelligent contract system based on Keyed-Homomorphic Public Key(KH-PKE)Encryption scheme is designed,so that medical data can be saved in the CB in the form of ciphertext,and the automatic sharing of data is *** knowledge mechanism is used to ensure the correctness of shared ***,the zero-knowledge mechanism introduces the dynamic group signature mechanism of chosen ciphertext attack(CCA)anonymity,which makes the scheme more efficient in computing and communication *** the end of this paper,the performance of the scheme is analyzed fromboth asymptotic and practical *** experimental comparative analysis,the scheme proposed in this paper is more effective and feasible.

关键词： Data management blockchain federated learning

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Location-guided Head Pose Estimation for Fisheye Image

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 682-697页

作者： Li, Bing Zhang, Dong Huang, Cheng Xian, Yun Li, Ming Lee, Dah-Jye Sun Yat-sen University School of Electronics and Information Technology China Duke Kunshan University Data Science Research Center Kunshan215316 China Brigham Young University Department of Electric and Computer Engineering United States

Camera with a fisheye or ultra-wide lens covers a wide field of view that cannot be modeled by the perspective projection. Serious fisheye lens distortion in the peripheral region of the image leads to degraded performance of the existing head pose estimation models trained on undistorted images. This paper presents a new approach for head pose estimation that uses the knowledge of head location in the image to reduce the negative effect of fisheye distortion. We develop an end-to-end convolutional neural network to estimate the head pose with the multi-task learning of head pose and head location. Our proposed network estimates the head pose directly from the fisheye image without the operation of rectification or calibration. We also created a fisheye-distorted version of the three popular head pose estimation datasets, BIWI, 300W-LP, and AFLW2000 for our experiments. Experiments results show that our network remarkably improves the accuracy of head pose estimation compared with other state-of-the-art one-stage and two-stage methods. © 2016 IEEE.

关键词： Convolutional neural networks

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Identification of Diabetic Retinopathy by Transfer Learning Based Retinal Images 9

Identification of Diabetic Retinopathy by Transfer Learning ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sagili, Sreenivas Reddy Goswami, Chandrashekhar Bharathi, V.C. Ananthi, S. Rani, K. Sathya, R. Rochester Institute Of Technology Department of computer science Lomb Memorial Drive RochesterNY14623 United States Mit School of Computing Mit Art Design and Technology University Department of Computer Science & Engineering Maharashtra Pune India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Tamil Nadu Coimbatore India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798350377972

The human eye is frequently considered to deliver a space into a person's strength because it is only with diabetic retinopathy (DR), a devastating eye illness caused by diabetes mellitus, that one may understand the subject's unprotected surface without invasive operations. There are certain disorders, notably vascular illness, that authority telltale indicators in the eye retina. Microaneurysms (MAs) are primary indicators of diabetic retinopathy (DR), making their detection crucial for an effective screening program that adheres to clinical procedures. Retinal examinations can reveal pathological deviations caused by limited ocular diseases such as arteriosclerosis, diabetes, cardiovascular disease hypertension and stroke. The proposed work introduces image processing algorithms, such as shady object identification, to predict the disorder or improve the contribution frame, thereby improving the perceptibility of vessels in color fundus frames. Following this enhancement, an automated classification algorithm, specifically a Convolutional Neural Network (CNN), is executed. The CNN architecture is designed to efficiently extract features from retinal frames, capturing complex patterns connected with diabetic retinopathy. The trained CNN is then assessed on a separate test condition, with its performance system of measurement-accuracy, specificity, recall, and Fl measure-being reported. © 2024 IEEE.

关键词： Convolutional neural networks

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering 7

nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zheng, Yan Wu, Song Ren, Yazhou Pu, Xiaorong Xu, Zenglin He, Lifang Yi, Zhang University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Harbin Institute of Technology Shenzhen School of Computer Science And Technology Shenzhen China Lehigh University Department of Computer Science And Engineering Bethlehem United States Sichuan University Intelligent Interdisciplinary Research Center College of Computer Science Chengdu China

ISBN: (纸本)9798350359145

Deep multi-view clustering (MVC) has recently gained significant interest for its capability to harness complementary information across multiple views through deep neural networks, enhancing clustering performance. However, existing deep MVC methods still have two issues: (1) They separate the feature learning objective and the clustering objective, potentially leading to suboptimal embedding features due to the neglect of the global clustering structure. (2) Most works utilize the target distribution obtained by the clustering objective to supervise feature learning. However, they simply sharpen the soft clustering distribution to obtain the target distribution, lacking the dynamics of the clustering process. To address these issues, in this paper we propose a novel end-to-end self-supervised multi-view clustering approach based on NeuralODEs, namely nmODE-MVC. Our approach operates in two stages: In the first stage, all view-embedded features are extracted, and then unified them into a global feature, facilitating global clustering assignments by exploiting multi-view data's complementarity. The second stage leverages the dynamic attributes of NeuralODEs to dynamically refine the data features and generate a target distribution that actively drives the clustering decisions. We formulate these learning processes as a unified optimization problem, facilitating iterative training and refinement. The effectiveness of nmODE-MVC is validated through extensive experiments on multiple real-world multi-view datasets. © 2023 IEEE.

关键词： Ordinary differential equations

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Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

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computer Systems science & engineering 2023年第1期44卷 595-612页

作者： D.Banumathy Osamah Ibrahim Khalaf Carlos Andrés Tavera Romero P.Vishnu Raja Dilip Kumar Sharma Department of Computer Science and Engineering Paavai Engineering CollegePachalNamakkal637018Tamil NaduIndia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdadIraq COMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de Cali76001CaliColombia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer *** computer-Aided Detection(CADs)/computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less *** paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the *** we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological *** test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications ***50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.

关键词： computer-Aided Detection breast cancer detection convolutional neural network class activation map computer-aided diagnosis

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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

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Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

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