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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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Computational Approaches to Detect Human in Multifaceted Environmental Conditions Using Computer Vision and machine intelligence - A review 3

Computational Approaches to Detect Human in Multifaceted Env...

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Bakshi, Gaytri Aggarwal, Alok University of Petroleum & Energy Studies School of Computer Science UK Dehradun India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Human detection is a very significant research problem in different arenas depending upon the adopted application. The main motivation that dominated the area are related to security and natural disasters. Considering the varying wide geographical area of the earth comprising of different countries with mountains, plains, deserts, plateaus and strong river system supporting the human civilization, there are problems dealing with the security system as well as ruining the normal day-to-day life with upcoming natural calamities. To tackle both the major issues with the growing technology and to serve the humanity, specific scientific, automatic, mechanically robust systems have been developed which intelligently provide solutions by detecting the upcoming problems. This paper depicts a study on various aspects on environmental and climatic conditions that could affect human detection and researchers' endeavors to achieve results with utmost accuracy. © 2022 IEEE.

关键词： Object detection

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Automatic 3D Teeth Semantic Segmentation with Mesh Augmentation Network 3

Automatic 3D Teeth Semantic Segmentation with Mesh Augmentat...

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3rd international conference on pattern recognition and machine Learning, PRML 2022

作者： Zhao, Qijun Wu, Xiao Zhu, Feiyu Liu, Jun Wei, Mingying Peng, Jiahan Lu, Yin Sichuan University College of Computer Science Chengdu China Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu China Sichuan University State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases Department of Orthodontics West China Hospital of Stomatology Chengdu China The People's Hospital of Neijiang Department of Stomatology Neijiang China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

In this paper, we propose an approach that improves segmentation networks with automatic augmentation networks for dental mesh data. Since conventional data augmentation is to augment all samples uniformly with predefined parameters, it ignores the unique characteristics of a single tooth sample and cannot make good use of the data set. And the traditional method separates data augmentation and segmentation network training, so the augmented data cannot be well adapted to the network to make it play a good role. We adopt a joint optimization strategy to integrate the augmentation network and the segmentation network, so that the augmented tooth data is the most suitable for the segmentation network. In addition, we design new improved loss functions suitable for augmentation and segmentation networks. Experiments have shown that the automatic augmentation network in our proposed method, named MeshAugNet, can effectively improve the segmentation accuracy after it is used for tooth segmentation. In general, this work achieves a combination of 3D dental data auto- augmentation network and segmentation network, which improves the accuracy of tooth segmentation, and can be used to solve the problem of too few samples in tooth datasets. © 2022 IEEE.

关键词： Semantic Segmentation

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Named Entity recognition for Fusion Bridge Generation Strategies 3

Named Entity Recognition for Fusion Bridge Generation Strate...

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3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： He, Li Jin, Yuling Liu, Jie Duan, Jianyong Wang, Hao School of Information Science and Technology North China University of Technology Beijing100144 China CNONIX National Standard Application and Promotion Lab Beijing100144 China China Language Intelligence Research Center Capital Normal University Beijing100089 China School of Information Science and Technology North China University of Technology 100144 China

ISBN: (纸本)9798350331387

para>Domain adaptation aims to alleviate the problem of insufficient labeled data in the target domain. Current domain adaptation methods for named entity recognition often use BERT models to generate entity type representations. However, there is an issue of vague entity type representations in the entity type embedding space generated by the BERT model. Based on this, a named entity recognition model with a fusion bridge generation strategy is proposed. First, a dictionary is constructed using entities and entity types from the entire training set. Then, keys are generated at the entity level using a key generator, and entity replacements are performed to obtain positive and negative examples of the original sentences. By actively focusing on fine-grained entity type information through contrastive learning, the model narrows the distance between entity types of the same kind and pushes apart entity types of different kinds, thereby clearly distinguishing entity types and improving the model's generalization ability. Experimental results on the CONLL and CrossNER datasets demonstrate that the improved model proposed in this paper outperforms the baseline model, confirming the effectiveness of this model approach. © 2023 IEEE.

关键词： BERT Contrastive Learning Domain Adaptation Named Entityrecognition TransferLearning

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Advanced Detection of Copy-Move Forgery Using Deep Convolutional Neural Network Features Integrated with machine Learning-Based Classification Techniques 3rd

Advanced Detection of Copy-Move Forgery Using Deep Convoluti...

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3rd international conference on machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Al-khuzaie, Mohammed Y. Chohan, Jasgurpreet Singh Sabah, Hawraa Ali Dey, Protyay Verma, Deeksha Hussein, Laith College of Technical Engineering National University of Science and Technology Dhi Qar Nasiriyah64001 Iraq School of Mechanical Engineering Rayat Bahra University Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Rajasthan Jaipur India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri- Punjab Mohali140307 India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9789819624676

Technological advancements have significantly contributed to the creation and processing of digital images through various image manipulation software available today. Consequently, the need for effective image forgery detection techniques has become paramount to distinguish between authentic and manipulated images. Image forensics is utilized in various fields, including national intelligence agencies, scientific publications, and social networks. One specific type of image manipulation, known as copy-move forgery, involves copying and pasting a portion of an image within the same image, resulting in an altered version of the original. In this study, we introduce a lightweight convolutional neural network (CNN) architecture combined with Support Vector machine (SVM) classification for copy-move forgery detection (CMFD). This approach uses a similarity measure across feature patches to identify regions containing forgeries. We evaluated the effectiveness of our model using the challenging MICCF600 dataset commonly employed in CMFD research. The proposed model effectively reduces false positives, thereby enhancing pixel-level accuracy and reliability in CMFD applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Fusion of LBP and Median LBP for Dominant Region Based Multimodal recognition Using Imperfect Face and Gait Cues 3rd

Fusion of LBP and Median LBP for Dominant Region Based Multi...

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3rd international conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Annbuselvi, K. Santhi, N. Sivakumar, S. V.V.Vanniaperumal College for Women Madurai Kamaraj University Virudhunagar Tamilnadu Madurai India Cardamom Planters’ Association College Madurai Kamaraj University Bodinayakanur Tamilnadu Madurai India

ISBN: (纸本)9789819966899

The paper provides a novel approach to recognize imperfect face and gait cues by using only the dominant region of such cues which contain more information than that possessed by the other regions. To enhance features in such dominant region, a fusion of Local Binary pattern (LBP) and Median Local Binary pattern (Median LBP) procedure is proposed. Initially, the given imperfect face and gait probe images are divided into six overlapped half regions. After partition, the dominant overlapped half regions of face and gait are selected by using content based image retrieval process. Subsequently, the features of dominant overlapped imperfect face and gait regions are enhanced by fusing the feature vectors obtained by using fusion of LBP and Median LBP methods. Next the Eigen feature vectors followed by Fisher’s vectors are constructed using Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) dimensionality reduction algorithms. The decisions from face and gait biometric systems are obtained separately by using Euclidean distance measure. Finally, the decisions of face and gait classifiers are fused at decision level for recognition using AND/OR rule. The method is verified on freely available ORL face and CASIA B gait datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Local binary pattern

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A Novel Namib Beetle Optimization Approach for Minimizing CO2 Emissions in Electric Vehicle Charging Integrated with Renewable Energy Sources 3

A Novel Namib Beetle Optimization Approach for Minimizing CO...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Adline priya, G. Kavithamani, K. Anupriya, K. Saranya, S. Kaliappan, S. Siva Ramkumar, M. Department of Electrical and Electronics Engineering Karpagam College of Engineering Tamilnadu Coimbatore641032 India Department of Artificial Intelligence and Machine learning Hindusthan College of Engineering and Technology Tamilnadu Coimbatore641050 India Department of Information and Technology Sri Krishna College of Technology Tamilnadu Coimbatore641042 India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Dept of ECE SNS College of Technology India

ISBN: (纸本)9798331532420

Integrating electric vehicles (EVs) with renewable energy sources (RESs) can lead to increased costs due to the need for advanced infrastructure and control systems, which may deter investment in clean technologies. Additionally, if the grid still relies on fossil fuels during periods of low renewable generation, the overall CO2 emissions may not decrease significantly, undermining the environmental benefits of EV adoption. In an effort to eliminate these drawbacks, this manuscript develops an approach to implementing charging of EVs using RESs. Namib Beetle Optimization (NBO) is the proposed method of this research. The major aim of the proposed method is to minimize the cost of charging of EV and at the same time minimize CO2 emission. The proposed NBO is employed so as to control and maximize the charging and discharging rate of the EV. At that time the proposed technique is implemented in MATLAB and performance is compared with several other techniques. The proposed technique is better than all the previous techniques like IALO, GA, MWOA, JMVO, and BESA for countering the threats effectively. The results demonstrate that the proposed method significantly outperforms traditional methods, effectively reducing costs of EV charging as $7,500 and CO2 emissions as 60.5ppm. © 2024 IEEE.

关键词： Windmill

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IoT's Role in Personalized Dietary Support for Mental Health using RNN Model 3

IoT's Role in Personalized Dietary Support for Mental Health...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Subha, S. Mohankumar, N. Rao, Allanki Sanyasi Ayub Khan, A. Ishwarya, M.V. Murugan, S. M Kumarasamy College of Engineering Department of English Tamil Nadu Karur India Maharashtra Nagpur India Christu Jyothi Institute of Technology & Science Department of Electronics and Communication Engineering Telangana Jangaon India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Agni College of Technology Artificial Intelligence and Data Science Department Old Mahabalipuram Road Tamil Nadu Thazhambur603103 India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

Rapid developments in Internet of Things (IoT) technology have spawned new ways of thinking about the very important field of mental health and wellbeing. This abstract explores "Personalized Dietary Support,"a new paradigm powered by the Internet of Things that aims to improve mental health outcomes by providing individualized dietary counseling. Individuals may get real-time insights into the dynamic interaction between their food choices, lifestyle, and mental well-being using data from IoT devices, such as wearables, nutritional trackers, and physiological sensors. Incorporating nutrient intake, meal timing, and dietary behaviors favorable to enhanced mental health, machine learning algorithms evaluate this data to provide tailored dietary recommendations. The IoT ecosystem also encourages user engagement through gamification and constant progress monitoring, which helps to keep users committed to their new eating habits over time. Working with medical experts, patients may care for their mental health from every angle. Recognizing the important importance of diet on mental health, this abstract investigates the possibility of IoT-driven Personalized Dietary Support to equip people with implementable methods. Data privacy and security are emphasized, as are patient autonomy and systemic approaches to healthcare. © 2024 IEEE.

关键词： Electronic health record

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Proceedings of 3rd international conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022

Proceedings of 3rd International Conference on Latest Trends...

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3rd international conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022

ISBN: (纸本)9781665487061

The proceedings contain 23 papers. The topics discussed include: an intelligent QR code scanning system for visually-impaired users;a computer aided technique for classification of patients with diabetes;an applied artificial intelligence technique for early prediction of diabetes disease;polarization-insensitive metalens with high efficiency for optical fiber communication;electrocardiograph based obstructive sleep apnea diagnoses;an application of artificial intelligence for an early and effective prediction of heart failure;an integrated machine learning framework for classification of cirrhosis, fibrosis, and hepatitis;heart attack prediction using machine learning approach;design of unit cells for groove gap waveguide at millimeter wave spectrum;future position estimation in case of GPS outages;design of customized high-performance on-chip and off-chip inductor to transmit power in millimeter distance compared with contrasting electromagnetic simulators;and application of digital twin technology in the field of autonomous driving test.

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3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation 26

3D Point Cloud Completion with Geometric-Aware Adversarial A...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Wu, Mengxi Huang, Hao Fang, Yi New York Univ NYU Multimedia & Visual Comp Lab NYU Tandon Sch Engn New York NY USA New York Univ NYUAD Ctr Artificial Intelligence & Robot NYU Multimedia & Visual Comp Lab Abu Dhabi U Arab Emirates

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

With the popularity of 3D sensors in self-driving and other robotics applications, extensive research has focused on designing novel neural network architectures for accurate 3D point cloud completion. However, unlike point cloud classification and reconstruction, the role of adversarial samples in 3D point cloud completion has seldom been explored. In this work, we demonstrate that adversarial samples can benefit neural networks on 3D point cloud completion tasks. We propose a novel approach to craft adversarial samples that improve the performance of models on both clean and adversarial inputs. In contrast to the Projected Gradient Descent (PGD) attack, our method generates adversarial samples that keep the geometric features in clean samples and contain few outliers. In particular, we use minimum absolute curvature directions to constrain the adversarial perturbations for each input point. The gradient components in the minimum absolute curvature directions are taken as adversarial perturbations. In addition, we adopt attack strength accumulation and auxiliary Batch Normalization layers to speed up the training process and alleviate the distribution mismatch between clean and adversarial samples. Experimental results demonstrate that training with the adversarial samples crafted by our method under the geometric-aware constraint effectively enhances the performance of the Point Completion Network (PCN) on the ShapeNet dataset.

关键词： Adversarial machine Learning 3D Point Cloud Completion

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Network information data processing method based on artificial intelligence 3

Network information data processing method based on artifici...

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3rd international conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Yang, Hong Meng, Xiaokai Zhang, Guangwei Wang, Dawei Liu, Xiaofei Institute of Electric Power Science Shanxi Electric Power Company National Power Network Taiyuan030001 China Anhui Jiyuan Software Co. Ltd. Hefei230000 China

Because of the complexity and development of network information data processing, many conventional technical means can not process information data, and the artificial itself is powerless, so it needs to use artificial intelligence technology for processing. In view of this, this paper will introduce the basic concept of artificial intelligence technology and its advantages in information data processing, and then combine the technology to put forward the network information data processing method. Through the research, the method in this paper can give full play to the role of artificial intelligence technology, fast and in-depth processing of network information data, and can give artificial decision support. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision support systems

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