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共 50189 条记录，以下是181-190 订阅

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Deep Geometric Moments Promote Shape Consistency in Text-to-3D Generation

Deep Geometric Moments Promote Shape Consistency in Text-to-...

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2025 ieee/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Nath, Utkarsh Goel, Rajeev Jeon, Eun Som Kim, Changhoon Min, Kyle Yang, Yezhou Yang, Yingzhen Turaga, Pavan Arizona State University United States Seoul National University of Science and Technology Korea Republic of Intel Labs

ISBN: (纸本)9798331510831

To address the data scarcity associated with 3D assets, 2D-lifting techniques such as Score Distillation Sampling (SDS) have become a widely adopted practice in text-to-3D generation pipelines. However, the diffusion models used in these techniques are prone to viewpoint bias and thus lead to geometric inconsistencies such as the Janus problem. To counter this, we introduce MT3D, a text-to-3D generative model that leverages a high-fidelity 3D object to overcome viewpoint bias and explicitly infuse geometric understanding into the generation pipeline. Firstly, we employ depth maps derived from a high-quality 3D model as control signals to guarantee that the generated 2D images preserve the funda-mental shape and structure, thereby reducing the inherent viewpoint bias. Next, we utilize deep geometric moments to ensure geometric consistency in the 3D representation explicitly. By incorporating geometric details from a 3D asset, MT3D enables the creation of diverse and geometri-cally consistent objects, thereby improving the quality and usability of our 3D representations. Project page and code: https://***/ © 2025 ieee.

关键词： 3D modeling

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TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations

TimberVision: A Multi-Task Dataset and Framework for Log-Com...

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2025 ieee/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Steininger, Daniel Simon, Julia Trondl, Andreas Murschitz, Markus Ait Austrian Institute of Technology Center for Vision Automation & Control Austria

ISBN: (纸本)9798331510831

Timber represents an increasingly valuable and versatile resource. However, forestry operations such as harvesting, handling and measuring logs still require substantial human labor in remote environments posing significant safety risks. Progressively automating these tasks has the potential of increasing their efficiency as well as safety, but requires an accurate detection of individual logs as well as live trees and their context. Although initial approaches have been proposed for this challenging application domain, specialized data and algorithms are still too scarce to develop robust solutions. To mitigate this gap, we introduce the Timbervision dataset, consisting of more than 2k annotated RGB images containing a total of 51k trunk components including cut and lateral surfaces, thereby surpassing any existing dataset in this domain in terms of both quantity and detail by a large margin. Based on this data, we conduct a series of ablation experiments for oriented object detection and instance segmentation and evaluate the influence of multiple scene parameters on model performance. We introduce a generic framework to fuse the components detected by our models for both tasks into unified trunk representations. Furthermore, we automatically derive geometric properties and apply multi-object tracking to further enhance robustness. Our detection and tracking approach provides highly descriptive and accurate trunk representations solely from RGB image data, even under challenging environmental conditions. Our solution is suitable for a wide range of application scenarios and can be readily combined with other sensor modalities. © 2025 ieee.

关键词： Logging (forestry)

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 ieee.

关键词： Data integrity

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Simplification Process in Supply Chain Management Through Artificial Intelligence 4

Simplification Process in Supply Chain Management Through Ar...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sharma, Ayushi Kumar, Rakesh Kumar, Deepak Pandey, Purnendu Shekhar Awasthi, Shashank Uttaranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Management Studies Dehradun India Army Institute of Technology Pune India G.L. Bajaj Institute of Technology and Management India

ISBN: (纸本)9798331523923

The aim of this study is to examine Artificial Intelligence(AI) applications in the supply Chain process. According to recent research on Supply Chain Management (SCM) in AI technology and various other technologies (machine learning, deep learning, neural network, expert vision, artificial neural network). Supply Chain Management (SCM) is transforming to artificial intelligence (AI), which improves sustainability, accuracy, and efficiency. This study explores AI in inventory management, warehouse management, customer satisfaction etc. AI Transportation, inventory control, and demand forecasting are all made easier by technologies like computer vision, robotics, and machine learning. Global sustainability goals are in line with AI, but adoption is hampered by issues like labour skill gaps, algorithmic biases, cybersecurity threats, and data quality. Training for the workforce, effective data management, and sustainable procedures are among the recommendations. AI is a key component of SCM's future since it can promote innovation, operational excellence, and sustainable development. The study contributes the objectives to analysis about AI reduced overall cost in supply chain management and also to study and analysis about AI enhanced quality and quantity of product in supply chain management. The study also discussed the SCM through the demand and supply concept economically. There are many challenges like biasness in the algorithm, skill person, and compromise with accuracy which need to be addressed. Finally, the study recommends suggestions and discusses the limitations of AI in supply chain management including sustainable future scope. © 2025 ieee.

关键词： Customer satisfaction

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Does Federated Learning Improve the Resiliency of Machine Learning Models Against Data Fault Attacks? 13

Does Federated Learning Improve the Resiliency of Machine Le...

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13th International conference on Intelligent control and Information Processing, ICICIP 2025

作者： Tavasoli, Mahsa Sarrafzadeh, Abdolhossein Patooghy, Ahmad Applied Science and Technology North Carolina A&T State University GreensboroNC United States Center of Excellence in Cybersecurity Research Education and Outreach North Carolina A&T State University GreensboroNC United States

ISBN: (纸本)9798331516147

Machine learning (ML) is increasingly used in various fields, making it vulnerable to cyberattacks like label-flipping attacks, which alter training data labels to mislead models. This paper evaluates the impact of such attacks on popular ML models, including Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), using two datasets. Results show that while RF is most resistant, MLP performs best under normal conditions but suffers greatly under attack. To mitigate this, we propose a federated learning (FL) approach that distributes training across multiple servers, enhancing robustness. Our results on the Breast Cancer Wisconsin dataset demonstrate FL’s effectiveness, achieving no accuracy drop with 20 servers, a training time of 99.79 seconds, and 3.82 MB bandwidth usage. FL proves highly resilient, making it suitable for critical applications like medical diagnosis in adversarial environments. © 2025 ieee.

关键词： Federated learning

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Enhancing the Security of Healthcare Data in Cloud Computing Environments 2

Enhancing the Security of Healthcare Data in Cloud Computing...

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2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sajwan, Himani Kumari, Anjali Chauhan, Garima Kumar, Raj School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (纸本)9798331530389

Cloud computing has completely evolved the way health care data is managed, but it also introduces critical security vulnerabilities that can compromise the privacy of patient information and threaten regulatory compliance. Herein, we present one of the most severe challenges: securing EHRs in a cloud environment with access to data while maintaining HIPAA compliance. We introduce a novel hybrid framework combining homomorphic encryption with blockchain-based access control and dynamic audit logging. The framework was used with a three-phase experimental approach and was deployed over five major healthcare institutions while being analyzed for 2.3 million patient records over 18 months. Results showed a high success rate of 99.97% against denial-of-service attacks while showing reductions in latency time about data retrieval up to 3 4 % compared to traditional methods of encryption. Security-incident-based variation was reduced up to 78%, and 99.99% data integrity was achieved during concurrent access scenarios. Cost analysis shows that overheads in security management amount to a reduction of 4 5 % against conventional systems. Our results show that the proposed framework is far better for healthcare data security, along with improved operational efficiency and regulatory compliance. This work contributes to the literature by providing a scalable and cost-efficient solution addressing the most complex security requirements of modern health care systems in cloud environments. The fact that the framework is implemented successfully in real life seems to reflect its effectiveness for mass-scale implementation in health organizations. © 2025 ieee.

关键词： Cloud security

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A CNN and HBA Based Approach for Grape Disease Identification 4

A CNN and HBA Based Approach for Grape Disease Identificatio...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vishnuvarthan, K. Renugadevi, R. Santhi, S. Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Grapes are among the most extensively cultivated fruit crops globally, with over 10,000 varieties. They serve as a significant economic resource for many nations due to their diverse applications, including wine production, fresh juice consumption, raisin production, and juice manufacturing. However, grape diseases pose a severe threat to the viticulture industry, leading to substantial financial losses and a decline in wine quality. Manual evaluation is the foundation of traditional disease detection techniques, which are error-prone in addition to saving time. This highlights the need for an automated system capable of analyzing plant health, identifying diseases, and addressing undernourishment efficiently and reliably. Accurate disease diagnosis and timely control measures are crucial for mitigating these challenges. Many scholars have studied various algorithms such as CNN, SVM, Neural Networks, Random Forests, etc., yet each has disadvantages. So we have proposed an automated deep learning-based approach that integrates the Honey Badger Algorithm (HBA) with Convolutional Neural Networks (CNN) for the detection of grape diseases. A phenomenal 95% accuracy rate was attained by the prototype after it was trained on a dataset of 10,000 photos. Its performance was evaluated across various grape varieties and environmental conditions, demonstrating its robustness and adaptability. © 2025 ieee.

关键词： Wine

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Isolated Soft Switched Resonant Led Driver with Constant Output Current and Wide Input Voltage Range 16

Isolated Soft Switched Resonant Led Driver with Constant Out...

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16th Annual Power Electronics, Drive Systems, and Technologies conference, PEDSTC 2025

作者： Askari, Shirin Farzanehfard, Hosein Maghsoudi, Mohammad Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798331533946

This paper proposes an isolated LCC resonant converter (LCC-RC) designed to deliver a constant output current for LED drivers across a wide range of input voltages by integrating a boost front-end stage with an LCC resonant converter. This topology leverages an LCC network to deliver numerous advantages. It integrates the boost filter inductor and transformer magnetizing inductance, ensuring that the primary switches achieve full Zero Voltage Switching (ZVS) and the secondary rectifier diodes experience Zero Current Switching (ZCS) across a wide range of duty cycles. Notably, the output current remains load-independent, making this configuration well-suited for LED applications. In addition, efficiency is improved by functioning near the resonant frequency. The hybrid structure of the proposed converter facilitates the use of a straightforward constant-frequency asymmetrical pulsewidth modulation (APWM) scheme to control the output current. To confirm the theoretical analysis and validate the LED driver functionality, a 50 W experimental prototype of the proposed driver was successfully created in the laboratory. This prototype supports an extensive input voltage range spanning from 18 V to 40 V, enabling it to supply a 500 mA output current to a series of 1 to 30 LEDs. This corresponds to an extensive output voltage capability spanning 3.3-100 V. © 2025 ieee.

关键词： Zero voltage switching

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IrriSmart-An IoT Testbed for a Resilient Smart Indoor Irrigation System 17

IrriSmart-An IoT Testbed for a Resilient Smart Indoor Irriga...

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17th International conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Maharana, Shakti Prasad Maharana, Siba Prasad Behera, Sadananda Palit, Basabdatta National Institute of Technology Department of Electronics and Communication Engineering Rourkela India

ISBN: (纸本)9798331531195

With the rise of smart technologies, numerous tasks can now be automated without manual intervention, optimizing resource use in various applications. This work introduces-IrriSmart-a testbed for a smart, automatic irrigation system designed for efficient indoor plant care, particularly suited for indoor gardens, nurseries, and greenhouses. IrriSmart leverages advanced capacitive soil moisture sensors, which offer increased durability and accuracy by measuring soil dielectric permittivity, avoiding the corrosion and sensitivity issues associated with traditional resistive sensors. It integrates the ESP8266 Node MCU microcontroller, a DHT11 sensor for temperature and humidity monitoring, and a DC pumping motor, all coordinated via a relay module. Additionally, it also has a water flow sensor for precise water regulation. IrriSmart uses the Blynk IoT platform to effectuate real-time remote monitoring and control from any internet-connected device. The sensor data is also logged, providing users with insights into plant conditions and system performance. The key advantages of the proposed IrriSmart system include reduced maintenance requirements, comprehensive environmental data collection, and remote operability, making it a robust, cost-effective solution for automated irrigation in controlled environments. © 2025 ieee.

关键词： Soil moisture

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Multi-Modal Biometric Authentication System using Hybrid Convolutional Neural Networks (HCNN) based on Face, Finger Vein and Iris Fusion 6

Multi-Modal Biometric Authentication System using Hybrid Con...

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6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Yuvasri, I. Soundarya Devi, K. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai India Loyola ICAM College of Engineeirng and Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

The increasing demand for accurate and secure biometric authentication has spurred the creation of complex multimodal biometric systems. A Hybrid Convolutional Neural Network (HCNN) is suggested in this study as a multi-modal biometric system that integrates face, vein, and iris recognition. The technique uses deep learning techniques to enhance feature extraction and fusion, increasing identification accuracy and resistance to spoofing attacks. The proposed system employs federated learning to ensure privacy - preserving model training and encryption for data transfer and storage, therefore addressing significant privacy and security concerns. Additionally, liveness detection techniques are offered to ensure the accuracy of the biometric data and prevent spoofing efforts. To ensure data-security, AES-265 is used for encryption. Experimental results show considerable improvements in recognition accuracy of 96.5% and precision of 97.1%, reduced error rates, and a significant reduction in processing time, indicating that the system is suitable for large-scale deployment. With applications in a number of industries, such as financial services and access control, this technique offersa complete, reliable solution for safe and effective biometric authentication. © 2025 ieee.

关键词： Convolutional neural networks

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