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modeling and point cloud generation for street tree branches 3

Modeling and point cloud generation for street tree branches

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3rd international conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Zhang, Yongjun Chen, Baisong Qin, Xuzhou Liu, Zhongwei Yunji Intelligent Engineering Co. Ltd. Guangdong Province Shenzhen518049 China

ISBN: (纸本)9781510688179

The modeling of street trees is of great significance to the research of highway landscape design. However, it is very difficult to accurately model a complete and realistic street tree. This paper simplifies the tree model by only retaining the main branches and imagining the branches as cones that bent along a 3d spline curve with gradually decreasing radius. Based on this simplification strategy, the tree branches are modeled by the main trunk and sub-branches, and finally, a spline curve skeleton and its 3d model are obtained. After that, the point cloud of tree branches can be obtained by simulating laser scanning to sample points on the 3d model. This method of obtaining 3d tree models, point clouds, and skeletons is flexible, efficient, and low-cost, which can efficiently generate a large amount of training data for further AI model trainings. © The Authors.

关键词： digital elevation model

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3rd Workshop on Computational Mathematics modeling in Cancer Analysis, CMMCA 2024 was held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

3rd Workshop on Computational Mathematics Modeling in Cancer...

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3rd Workshop on Computational Mathematics modeling in Cancer Analysis, CMMCA 2024 was held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031733598

The proceedings contain 12 papers. The special focus in this conference is on Computational Mathematics modeling in Cancer Analysis. The topics include: Follicular Lymphoma Grading Based on 3d-ddcGAN and Bayesian CNN Using PET-CT Images;Multi-channel Multi-model Fusion Module (MMFM) Based Circulating Abnormal Cells (CACs) detection for Lung Cancer Early diagnosis with Fluorescence in Situ Hybridization (FISH) Images;domain Game: disentangle Anatomical Feature for Single domain Generalized Segmentation;Attention-Fusion Model for Multi-omics (AMMO) data Integration in Lung Adenocarcinoma;Pd-L1 Expression Prediction Using Scalable Multi Instance Transformer;improving Single-Source domain Generalization via Anatomy-Guided Texture Augmentation for Cervical Tumor Segmentation;PANdA: Pneumonitis Anomaly detection Using Attention U-Net;estimating the Average Treatment Effect Using Weighting Methods in Lung Cancer Immunotherapy;Beyond Conventional Parametric modeling: data-driven Framework for Estimation and Prediction of Time Activity Curves in dynamic PET imaging;assessment of Radiomics Feature Repeatability and Reproducibility and Their Generalizability Across Image Modalities by Perturbation in Nasopharyngeal Carcinoma Patients.

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3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

3rd International Workshop on Applications of Medical Artifi...

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3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031820069

The proceedings contain 24 papers. The special focus in this conference is on Applications of Medical Artificial Intelligence. The topics include: SP-NAS: Surgical Phase Recognition-Based Navigation Adjustment System for distal Gastrectomy;transforming Multimodal Models into Action Models for Radiotherapy;enhanced Interpretability in Histopathological Images via Combined Tissue and Cell-Level Graph Analysis;targeted Visual Prompting for Medical Visual Question Answering;deep Learning for Resolving 3d Microstructural Changes in the Fibrotic Liver;predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework;Optimizing ICU Readmission Prediction: A Comparative Evaluation of AI Tools;source Matters: Source dataset Impact on Model Robustness in Medical imaging;evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-automated Classification and detection of Community Acquired Pneumonia;incremental Augmentation Strategies for Personalised Continual Learning in digital Pathology Contexts;assessing Generalization Capabilities of Malaria diagnostic Models from Thin Blood Smears;automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery;quantifying Knee Cartilage Shape and Lesion: From Image to Metrics;RadImageGAN – A Multi-modal dataset-Scale Generative AI for Medical imaging;Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to discriminate Individuals with Psychiatric disorders from Controls;SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury;EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models;evaluating the Impact of Pulse Oximetry Bias in Machine Learning Under Counterfactual Thinking;normative modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s disease diagnosis and Biomarker Identification;one-Shot Medical

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AHMsys: An Automated HVAC modeling System for BIM Project 3rd

AHMsys: An Automated HVAC Modeling System for BIM Project

引用

3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： dang, Long Hoang Nguyen, duy-Hung Le, Thai Quang Nguyen, Thinh Truong Mei, Clark Hoang, Vu Akila Ho Chi Minh City Viet Nam Posts and Telecommunications Institute of Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031809422

This paper presents a novel system, named AHMsys, designed to automate the process of generating 3d Heating, Ventilation, and Air Conditioning (HVAC) models from 2d Computer-Aided design (CAd) drawings, a key component of Building Information modeling (BIM). By automatically preprocessing and extracting essential HVAC object information then creating detailed 3d models, our proposed AHMsys significantly reduced the 20% work schedule of the BIM process in Akila. This advancement highlights the essential impact of integrating AI technologies in managing the lifecycle of a building’s digital representation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Building Information Model

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Third international conference on Electrical, Electronics, and Information Engineering, EEIE 2024

Third International Conference on Electrical, Electronics, a...

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3rd international conference on Electrical, Electronics, and Information Engineering, EEIE 2024

ISBN: (纸本)9781510688179

The proceedings contain 67 papers. The topics discussed include: control method for longitudinal tearing of conveyor belts in coal mines;novel ultra-miniature bandpass filter using both capacitive- and inductive-loaded siw resonators;a modular multilevel converter based on bridge arm multiplexing with efficient converter tributary;optimization design of automotive relay noise based on electric - magnetic - thermal digital model and experiments;design and application of multi-channel focused ultrasound and electrical stimulation device for improving endometrial blood flow;optimized design of chip-breaking geometry of insert for the high-stability indexable insert drill;design and implementation method of automatic bird repellent based on infrared thermal imaging;analysis and experimental research on friction moment of high speed ball bearings for new energy vehicles;discussion on scientific research project management in colleges and universities based on PdCA cycle theory;hierarchical control strategy of interconnected AGC based on BAS-MPC and fractional-order PId;and modeling and simulation system for laser wireless power transmission.

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Big data for Enhancing Cybersecurity in Enterprise Environments Proactive Threat detection and Prevention 3

Big Data for Enhancing Cybersecurity in Enterprise Environme...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Chityala, Upender Reddy Shnain, Ammar H. Govindaraj, Manoj Johri, Prashant Kuppuraj, T. devi, N.Lavanya Equinox It Solutions Llc United States College of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Chennai India School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Psna College of Engineering and Technology Department of Ece India

ISBN: (纸本)9798331542375

This research explores the integration of big data technologies and machine learning to enhance cybersecurity in enterprise environments focusing on proactive threat detection and prevention. Traditional security systems often struggle to keep up with the growing complexity and volume of cyber threats, necessitating a more advanced, data-driven approach. The proposed methodology leverages real-time data processing, predictive modeling, and anomaly detection to improve threat detection accuracy, reduce response times, and enhance system scalability. By collecting and analyzing data from various network sources, including logs from security devices and endpoint protection software, the system detects potential threats more efficiently than conventional methods. Machine learning algorithms such as supervised and unsupervised learning, along with big data platforms like Apache Spark, enable real-time analysis of large volumes of data, ensuring timely identification of both known and unknown cyber threats. The results demonstrate significant improvements in detection accuracy, system scalability, and incident resolution time, with the proposed methodology outperforming traditional security systems in key performance metrics. This research highlights the potential of big data-driven cybersecurity solutions in addressing modern challenges and offers a framework for organizations to implement more effective, scalable, and proactive security measures to safeguard their digital assets. © 2025 IEEE.

关键词： Cyber attacks

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Proceedings of international conference on 3-d digital imaging and modeling, 3dIM

Proceedings of International Conference on 3-D Digital Imagi...

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3rd international conference on 3-d digital imaging and modeling, 3dIM 2001

ISBN: (纸本)0769509843

The proceedings contain 49 papers. The topics discussed include: a self-referenced hand-held range sensor;calibration-free approach to 3d reconstruction using light stripe projections on a cube frame;approaches to a color scannerless range imaging system;in process 3d-sensing for laser material processing;extract and display moving object in all direction by using stereo omnidirectional system(S0S);3d modeling of archaeological vessels using shape from silhouette;stroboscopic stereo rangefinder;compact 3d profilometer with grazing incidence diffraction optics;structure and motion from two uncalibrated views using points on planes;a laser range scanner designed for minimum calibration complexity;combining off- and on-line calibration of a digital camera;view planning with a registration constraint;computing camera positions from a multi-camera head;and a method for the registration of attributed range images.

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Evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-automated Classification and detection of Community Acquired Pneumonia 3rd

Evaluating Perceived Workload, Usability and Usefulness of...

引用

作者： Muzumala, Malaizyo G. Zulu, Ernest O. Chibuta, Peter Nyirenda, Mayumbo Phiri, Lighton Department of Computer Science University of Zambia Lusaka Zambia Department of Radiology University Teaching Hospitals Lusaka Zambia Department of Library and Information Science University of Zambia Lusaka Zambia

ISBN: (纸本)9783031820069

The use of Artificial Intelligence (AI) techniques in radiological workflows is increasingly becoming mainstream. However, the uptake of AI techniques is still low in low-resource settings such as the Global South. This paper presents a study conducted in a setting with low AI uptake, to determine the impact of AI on Radiologists’ workload when interpreting medical images. Two (2) AI models-a classification model and detection model indicating potential areas of interest-were implemented to facilitate the semi-automated interpretation of medical images for Pneumonia. In addition, a Web-based dICOM Viewer was implemented to interface the AI models. To determine the appropriate model configuration, two (2) experts-a Radiologist and Radiology Resident-participated in a focus group discussion aimed at determining how the AI models could facilitate interpretation processes. A comparative controlled experiment was subsequently conducted with 12 Radiology Residents at a large University Teaching Hospital, to assess the impact of AI on the workload and its perceived usefulness. NASA Task Load Index (TLX) and Technology Acceptance Model (TAM) 2 questionnaires were employed to measure the workload and usefulness. The results indicate that the perceived workload is significantly less when using the AI solution, with an overall NASA-TLX score of 1.86. Furthermore, the perceived usefulness of the AI solution is demonstrated through the positive responses for all the eight TAM 2 constructs. This study experimentally demonstrates the potential of utilising AI for the semi-automated interpretation of medical images in low-resource settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： digital imaging and Communications in Medicine (dICOM)

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Generation of Pseudo X-Ray and digitally Reconstructed Radiograph from CT images

Generation of Pseudo X-Ray and Digitally Reconstructed Radio...

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Intelligent data Communication Technologies and Internet of Things (IdCIoT), international conference on

作者： Arya R Sreeja Kochuvila Dept. of Artificial Intelligence Amrita School of Artificial Intelligence Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Computed Tomography (CT) scanning offers 3d insights into the human body but involves higher radiation exposure than traditional X-Rays, increasing the relevance of 3d reconstructions from 2d data. In this work, datasets of CT- pseudo-X-Ray and CT- digitally Reconstructed Radiograph (dRR) pairs are generated using ray-sum tracing and projection techniques. This can be helpful for implementing machine learning and deep learning architecture as 2-d data availability is limited. The described approach generates CT-pseudo-X-Ray and CT-digitally Reconstructed Radiograph (dRR) pairs using ray-sum tracing and projection methods. The pseudo-X-Ray is created by combining bone and soft tissue projections, while the dRR is derived via ray-sum tracing. By retaining the CT images in digital imaging and Communications in Medicine (dICOM) format, the created dataset enhances robustness and efficiency, supporting 3d reconstruction-a rapidly advancing field.

关键词： Radiography Three-dimensional displays Computed tomography X-rays Machine learning Robustness dICOM Internet of Things Image reconstruction X-ray imaging

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3rd international conference on Geometric modeling and imaging: Modern Techniques and Applications, GMAI

3rd International Conference on Geometric Modeling and Imagi...

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3rd international conference on Geometric modeling and imaging: Modern Techniques and Applications, GMAI

ISBN: (纸本)9780769532707

The proceedings contain 21 papers. The topics discussed include: three point stationary and non-stationary subdivision schemes;designing planar objects with an interactive spline;simulating a deformable object using a surface mass spring system;smooth surface reconstruction using doo-Sabin subdivision surfaces;a vector representation for polyhedra;shape analysis of left ventricle using invariant 3-d spherical harmonics shape descriptors;experimental evaluation of graphics algorithms;creative shape interpretation in paper scribbles;contour rectification and analysis using circular augmented rotational trajectory algorithm;a new contour reconstruction approach from dexel data in virtual sculpting;image restoration by non-standard diffusion;sub-tensor decomposition for expression variant 3d faces recognition;and a fingerprint local-matching algorithm using unit-circle parameterization.

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