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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Kumar, Vivek Prabhu, A. Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

The potential to comfortable systems and networks from unauthorized get right of entry is essential for any virtual community. Authentication protocols are a cornerstone to protecting such structures from malicious actors and imposing facts security. Despite the myriad of authentication protocols, however, there are boundaries to their efficacy in nicely securing digital networks. Authentication protocols, even those strongly encouraged through protection professionals, have recognized flaws and weaknesses which can be exploited by means attackers-resulting in violations of confidentiality, integrity, and availability of machine and application facts. Further, authentication protocols can be too hard to apply and cumbersome to maintain, main customers to bypass them-setting structures at risk and exposing them to capability breaches. This paper intends to research the strengths and weaknesses of authentication protocols and endorse techniques to maximize their effectiveness while deliberating applicability, usability, practicality, and cost. We can inspect adequate controls that can be used to bolster authentication protocols hired in existing facts protection models, as well as talk about the need for advanced authentication protocols and verification techniques. Authentication protocols are used in records protection fashions to offer cozy get right of entry to networked applications. Those protocols have to offer sturdy identification assurance and securely authenticate each gets the right of entry to try. This summary discusses the restrictions of the authentication protocols presently employed by current statistics protection fashions. Authentication protocols rely on cryptography and get admission to manipulate mechanisms to ensure that the most straightforward authenticated users gain access to sources. A few of the authentication protocols currently in use, including fundamental username and password authentication, are often a problem to assault due to thei

关键词： authentication community exploited implementation protocols

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Leveraging Self-Supervised Transfer Learning for Robust Medical Image Classification

Leveraging Self-Supervised Transfer Learning for Robust Medi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Surendra Dwivedi, Rakesh Kumar Gobi, N. Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

this study appears to use self-supervised transfer mastering for sturdy scientific photo classes. Switch getting to know is a powerful approach for enhancing the accuracy of deep mastering fashions in scientific imaging. This paper investigates using self-supervised getting-to-know techniques for scientific picture classes within characteristic-based procedures. By leveraging self-supervised schooling strategies, consisting of contrastive mastering, distributed representations, clustering, pseudo-venture gaining knowledge of, and self-supervised multi-undertaking gaining knowledge of, the proposed technique can learn representations that are extra sturdy to the area shift of various clinical imaging datasets. Experiments performed on an extensive x-ray and ultrasound snapshots dataset reveal that the proposed approach affords extra improvement in type accuracy compared to traditional feature-primarily based techniques. © 2024 IEEE.

关键词： classification clustering greater representations training

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Enhancing Automated Industrial Guiding Through computer Vision-Based Detection of Centerlines and Patterns

Enhancing Automated Industrial Guiding Through Computer Visi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumari, Neeraj Ojha, Ananta Goyal, Manish Kumar Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This paper gives a method for enhancing automatic industrial guiding via pc vision-based detection of centerlines and patterns. The principal intention of this painting is to utilize vision algorithms to offer a reliable steering system for commercial robots. The technique combines numerous vision-primarily based detection techniques that allow you to come across the favored center lines and patterns. The consequent steerage device is then examined with actual robot experiments for validation. Moreover, a novel metric for measuring the accuracy of the detection device is also proposed. The effects show that the proposed technique gives a dependable answer for automatic commercial guiding. This paper discusses an automated business steerage technique, which leverages laptop imagination and is prescient to become aware of centerlines and patterns in business additives. This method uses a Convolutional Neural community (CNN) to discover those strains and patterns. To carry out detection, a CNN is educated on a fixed of photographs containing centerlines or styles and corresponding ground reality that is manually annotated. The effects of the CNN are used to guide robots to their favored function and become aware of element edges. © 2024 IEEE.

关键词： algorithms commercial experiments misalignments robots

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Accurate Diagnosis by Magnetic Resonance Imaging Using Deep Learning

Accurate Diagnosis by Magnetic Resonance Imaging Using Deep ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Rengarajan, A. Ahmed, Zahid Pandey, Rajendra P. Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

deep mastering has revolutionized the field of scientific imaging, providing practical affected person prognosis competencies. A current example is deep studying for Magnetic Resonance Imaging (MRI) acquisition, reconstruction, segmentation, and interpretation. Deep mastering-primarily based strategies leverage convolutional neural networks (CNNs) to automatically classify and section not unusual disease areas in MRI scans, allowing particular and correct prognosis. Those computerized strategies can potentially conquer the bottleneck of the complex work-intensive guide segmentation and visualization procedure. Moreover, they can provide an extra complete assessment of complex sicknesses. Through incorporating both the semantic and spatial information of image information, the overall performance of deep-gaining knowledge of-based totally structures for MRI evaluation has dramatically progressed. Moreover, CNN-based total MRI segmentation has proven promise in targeted and effective remedies for numerous diseases, including mind tumors, stroke, and dementia. The demonstration of superior effects in segmentation and classification tasks, in terms of accuracy and efficiency, shows the potential of deep learning-based techniques as an effective device within the automation of MRI analysis. © 2024 IEEE.

关键词： classification demonstration performance prognosis visualization

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FSA-Net:A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization

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Intelligent Automation & Soft Computing 2023年第7期37卷 971-983页

作者： Zhipeng Bin Huihuang Zhao Xiaoman Liang Wenli Chen College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two *** this study,the Face Swapping Attention Network(FSA-Net)is proposed to generate photoreal-istic face *** existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint(cheek,mouth,eye,nose,etc.),which causes artifacts and makes the generated face silhouette *** address this problem,a novel reinforced multi-aware attention module,referred to as RMAA,is proposed for handling facial fusion and expression occlusion *** framework includes two *** the first stage,a novel attribute encoder is proposed to extract multiple levels of target face attributes and integrate identities and attributes when synthesizing swapped *** the second stage,a novel Stochastic Error Refinement(SRE)module is designed to solve the problem of facial occlusion,which is used to repair occlusion regions in a semi-supervised way without any *** proposed method is then compared with the current state-of-the-art *** obtained results demonstrate the qualitative and quantitative outperformance of the proposed *** details are provided at the footnote link and at https://***/view/fsa-net-official.

关键词： Attention face-swapping neural network face manipulation identity swap image translation

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Prediction of Epileptic Seizures based on EEG Signal using CNN Model 8

Prediction of Epileptic Seizures based on EEG Signal using C...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kumar, Suraj Prakash, Shiva Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9798350396638

Millions of patients suffer from epilepsy each year, a chronic nervous illness with a growing global prevalence. In a lot of situations, it might cause critical injuries or patient deaths. So, the automatic prediction of the epileptic seizure saves the patients from injury as well as from death. In this age of technological advancements, the internet of things (IoT) has started to offer a variety of solutions in the healthcare sector through the use of machine learning, deep learning, and cloud-based services. Internet of thing, Edge computing, and Cloud computing helps in the detection of such nervous disorders using deep learning and machine learning. Most of the models were developed preciously but there are many issues related to the prediction and security of the data of patients. This research study proposed an autonomous cloud-edge integrated seizure prediction model based on a convolutional neural network. The proposed model detects and classifying the epileptic seizure using an EEG dataset. The proposed model shows average training accuracy and average validation accuracy are 99.27% and 96.37%, respectively and the average losses for training and validation are 2.10 % and 14.23%, respectively. The result shows that overall accuracies and losses are better than the wellknown existing works. All implementation for seizure prediction is done in Python language. © 2023 IEEE.

关键词： Internet of things

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Partial Attention-Based Direction-Aware Vehicle Re-identification 18th

Partial Attention-Based Direction-Aware Vehicle Re-identific...

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18th International Conference on computer Science and Education, ICCSE 2023

作者： Zhou, Yujie Yuan, Caihong Su, Chenshuang Zou, Mingdong Zhu, Xiaoke Liang, Wenjuan School of Computer and Information Engineering Henan University Henan Engineering Research Center of Intelligent Technology and Application Henan Province Spatial Information Processing Engineering Technology Research Center Kaifeng China

ISBN: (纸本)9789819707294

With the rapid development of urban transportation, vehicle re-identification has become a focal point in traffic management and vehicle tracking problems. In order to address the problem of small inter-class similarity among vehicles, previous studies utilize vehicle parsing models to extract local features. Therefore, we introduce the Squeeze-and-Excitation attention mechanism to extract important discriminative information from these local features. Furthermore, we propose a local co-occurrence attention mechanism to represent the proportion of common parts feature matching. To address the issue of large intra-class differences caused by vehicle direction change, we propose a lightweight and effective direction weighted fusion strategy. Experiments on two large datasets show that the proposed algorithm performs competitively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urban transportation

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of computer Science & technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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Cross-Guided Attention and Combined Loss for Optimizing Gastrointestinal Visual Question Answering

Cross-Guided Attention and Combined Loss for Optimizing Gast...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Qian Liu, Lijun Yang, Xiaobing Liu, Li Peng, Wei Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming650500 China

ISBN: (纸本)9798350386226

Medical Visual Question Answering (Med-VQA) aims at answering clinical questions related to medical imaging, providing important reference for medical imaging diagnosis. Gastrointestinal visual question answering can deeply understand and analyze gastrointestinal medical images, provides supporting information to answer questions about the diagnostic process, and improves diagnostic accuracy and efficiency. However, most existing Med-VQA can only handle simple and general questions, struggling to tackle visual question answering related to complex diseases. Therefore, this paper proposes a Med-VQA model called CACL, which uses cross-guidance and improved combined loss. The model focuses on gastrointestinal image analysis and achieves efficient and accurate feature extraction for gastrointestinal images. A cross-guided attention module is also designed to enhance the model's reasoning ability when dealing with complex cross-modal tasks. In addition, a multi-task composite loss function is designed to balance the loss of segmentation task and classification task, and improve the overall performance. Experimental results show that the proposed method can effectively improve the accuracy of gastrointestinal visual question answering. © 2024 IEEE.

关键词： Multi-task learning

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Design and Implementation of Embedded Qinghai Tourism Customer Service Question-and-Answer Robot Based on ChatGPT 24

Design and Implementation of Embedded Qinghai Tourism Custom...

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9th International Conference on Cyber Security and information Engineering, ICCSIE 2024

作者： Wu, Jing Guo, Zhenxin Wang, Zhicheng Zhang, Haotian Kang, Xaolin Ma, Xiaoguang Department of Computer and Information Science Qinghai Institute of Technology China Foshan Graduate School Northeastern University China School of Computer Science and Application Qinghai University China

ISBN: (纸本)9798400718137

The application of big data technology has brought new impetus and possibilities to the development of tourism. The design of an embedded Q&A robot system for Qinghai tourism customer service based on ChatGPT system aims to provide an intelligent customer service solution to help users obtain information about Qinghai tourism and answer questions. The system uses ChatGPT as the core dialogue model, combines the knowledge and data in the field of Qinghai tourism, and realizes the intelligent answer to user questions. This study application in the field of natural language processing, and analyzes the design requirements and implementation scheme of embedded travel question answering system in detail. The function module includes user login and registration, travel tickets, hotel information query, scenic spot ticket price query and other essential basic function modules of the tourism website. Whether the design is reasonable or not will also directly affect the user experience. The results show that ChatGPT embedded in Qinghai tourism Q&A system can not only provide more intelligent and accurate answers, but also enhance the interactivity and practicability of the system, and provide users with more convenient and personalized tourism information services. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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