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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

共 90984 条记录，以下是361-370 订阅

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PoseSmart: AI-Driven Dress Recommendations with Pose Estimation 3

PoseSmart: AI-Driven Dress Recommendations with Pose Estimat...

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3rd international conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Bindu, Dubba Hima Siri, M. Shanvitha, Muvvala Sri Venkata Satya Aiswariya Milan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798331523893

Persistent challenges in the fashion e-commerce industry include inaccurate sizing and lack of personalized experiences, which often lead to dissatisfaction and high return rates. The innovations in technology, including pose estimation and machine learning, offer promising solutions to these issues. This project offers a real-time body measurement system to increase the probability of fit accuracy and the self-confidence of the users while selecting clothes online. Utilizing MediaPipe's pose estimation framework and OpenCV, the application captures accurate measurements of body length, chest, waist, and sleeve length using an interface webcam. User-friendly web application developed using Flask allows users to choose from categories of clothes, and receive size recommendations according to their measurements. It's designed to deal with the challenges of fit accuracy and engagement. This reduces size related returns and allows for scalability and further development along the lines of more sophisticated recommendation algorithms. It integrates traditional shopping experiences with the demands of modern e-commerce. Evaluation metrics used to examine how well recommendation systems work are success rate (the proportion of matched items to the dataset), matched items (successful suggestions based on user measures), dataset size (total items processed), and matching time (evaluating computational efficiency for real-time application) are some of the metrics used in this implementation © 2025 IEEE.

关键词： Body measurements Clothing Size Classification Flask Web Application OpenCV (computer Vision) Personalized Clothing Pose Estimation

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Effect of Industrial Robot Load on its Pose Repeatability and Working Cycle Time

Effect of Industrial Robot Load on its Pose Repeatability an...

引用

international conference on computer and Automation Engineering, ICCAE

作者： Ivan Zajačko Peter Forgáč Vladimír Tlach Ivan Kuric Dariusz Wiecek Department of Automation and Production Systems University of Žilina Žilina Slovakia Department of Industrial Engineering University of Bielsko Biala Bielsko-Biała Poland

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The experiments presented in this paper aim to assess the effect of proper loading of the end of the robot's arm on its pose repeatability and working cycle time. The measurement is carried out on a FANUC LR Mate 200iC industrial robot using a digital indicator. The experiment was carried out at five different robot loads. For each single load, a measurement in three axes was performed. The experiment also includes an assessment of the influence of the detailed definition of the end of the robot's arm load in its control system on the measurement results. The paper provides insight into the possibility of investigating the performance criteria of industrial robots and highlights the essence of the correct setting of the robot load including the moments of inertia and the position of the center of gravity.

关键词： Weight measurement Service robots Current measurement Loading Industrial robots Manipulators Control systems Time measurement Spreadsheet programs Gravity

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A Hybrid GRU-Transformer Model for Financial Forecasting and Risk Management 3

A Hybrid GRU-Transformer Model for Financial Forecasting and...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Pradhan, Rahul Alazzam, Malik Bader Keswani, Sarika Bhasin, Narinder Kumar Jaff, Nadhema Ahmed Muthuperumal, S. Gla University Department of Computer Engineering & Applications Mathura India Jadara University Faculty of Information Technology Irbid Jordan Pune India Indian Institute of Banking and Finance Mumbai India College of Administrative and Financial Sciences Knowledge University Department of Business Administration Erbil Iraq K.Ramakrishnan College of Engineering Department of Mathematics Trichy India

ISBN: (纸本)9798331508456

Financial sector demands precise forecasting models to mitigate risk and optimize asset management. This study presents a novel Hybrid GRU-Transformer Model that integrates Gated Recurrent Units (GRUs) for capturing local temporal patterns and the Transformer T5 architecture for modeling global dependencies. Financial datasets sourced from Kaggle, including stock prices and trading volumes, were preprocessed through normalization and categorical encoding, followed by an 80:20 dataset split for training and testing. The proposed model outperformed existing methods such as ARFIMA-LSTM and EMD-CNN-LSTM, achieving superior metrics, including a Mean Squared Error of 0.08 and an R2 of 0.95. Results underline the model's accuracy and robustness in predicting financial risks and trends. By enhancing predictive performance and risk management strategies, this model addresses the dynamic complexities of financial markets. Future research will explore its adaptability across diverse instruments and markets to further its application in real-time forecasting scenarios. The system is implemented using Python integrated with MATLAB. © 2025 IEEE.

关键词： Risk management

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601387

The proceedings contain 49 papers. The special focus in this conference is on Frontiers of Intelligent Computing: Theory and Applications. The topics include: Nepali Word Spelling Correction Using Ensemble Learning Technique;the Impact of Information Security Policies and Innovations in Digital Technology on the Transformation of Healthcare in Developing Nations: A Literature Review;integrating Multi-omics and Clinical Narratives for Predictive modeling: Genomics, Transcriptomics, Proteomics, and Medical Texts in Disease Analysis;a Comprehensive Survey of Fake Review Detection Technology;a Hybrid Intelligent Decision Support System for Automating Financial Credit Evaluation;Advancing Taxation in the New Era: Enhancing Tax Ratios with the Core Tax Administration System (CTAS);Enabling Grid Stability: Harnessing μPMU Data for Data-Driven Analysis of Grid Frequency Events;machine Learning Techniques to Detect Fake News on Social Media: A Systematic Review;cutting-Edge Technology-Based Social Enterprises in India for Sustainable, Inclusive Healthcare;enhancing Telugu Sarcasm Classification Models with Word Embeddings in Imbalanced Datasets;capital Punishment: Analyzing Trends in the United States (1976–2016);exploring the Technostress Issue Among Indonesian Young Entrepreneurs;Teaching computer Science Using Cloud-Based IDE with Perspectives in Inclusivity;Comparison of Data Encryption Standard (DES) and Advanced Encryption Standard (AES) in Security Issues of Cloud Computing: A Literature Review;enhancing Corporate and Factory Training Through Game Development: A Comparative Review;usability of Intelligent System in Implementing Tutoring Lesson in Education: A Literature Review;performance Efficiency of Cloud Computing—A Literature Review;decentralized Finance (DeFi) Wallets: A Review of its Efficiency, Usability, and Effectiveness;Social Networks on WEB 3.0.

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17th international conference on the Simulation of Adaptive Behavior, SAB 2024

17th International Conference on the Simulation of Adaptive ...

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17th international conference on the Simulation of Adaptive Behavior, SAB 2024

ISBN: (纸本)9783031715327

The proceedings contain 26 papers. The special focus in this conference is on Simulation of Adaptive Behavior. The topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of Varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch Through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent systems: A Computational modeling Approach;transient Milling Dynamics in Collective Motion with Visual Occlusions;extended Swarming with Embodied Neural Computation for Human Control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for Data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg Vehicles;neural Chaotic Dynamics for Adaptive Exploration Control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback Controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of Exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"Value" Emerges from Imperfect Memory;the Role of Theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy Analysis in Education: A Multimodal Computational Model.

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Wellness AI: AI-Driven Personalized Diet and Fitness Recommendation

Wellness AI: AI-Driven Personalized Diet and Fitness Recomme...

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Aditya Kothari Utkarsh Khanna Neil Shah Sapna Shah Department of Artificial Intelligence NMIMS MPSTME Mumbai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Wellness AI is a health, fitness and lifestyle recommendation platform designed to process direct prompts that contain user information, such as age, gender, dietary preferences, and lifestyle choices. The system integrates real-time health metrics from Apple HealthKit, enabling more comprehensive insights by including user’s vitals. Using advanced fine-tuned large language models (LLMs) with Low-Rank Adaptation (LoRA) and quantization techniques, the platform achieves high efficiency and accuracy. This paper details the technical components, training methods, and model optimization approaches used to streamline personalized health insights.

关键词： measurement Training Technological innovation Quantization (signal) Accuracy Computational modeling Large language models Real-time systems Data models Optimization

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601424

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Persian Text-Image Retrieval: A Framework Based on Image Captioning and Scalable Vector Search

Persian Text-Image Retrieval: A Framework Based on Image Cap...

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international CSI computer conference, CSICC

作者： Rasoul Asadian Alireza Akhavanpour Shenasa AI Tehran Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Text-image retrieval systems have seen significant advancements with CLIP-based models and Vision-Language models, which rely on vast datasets and powerful computational resources. However, these advancements primarily focus on English and other high-resource languages, leaving a substantial gap for languages like Persian. This paper introduces a pioneering Persian Text-Image Retrieval (PTIR), the first comprehensive framework designed to address Persian's unique challenges. We present three key innovations: (1) a diverse corpus of 1.2 million Persian image-caption pairs from multiple sources; (2) training a vision-language model combining a vision-encoder and a pre-trained Persian text-decoder to optimize cross-modal alignment and generate detailed captions; and (3) a scalable retrieval pipeline using sentence embedding models and vector databases for efficient Persian query-image matching. Evaluations on the Persian-adapted COCO benchmark dataset demonstrate PTIR achieves a 30-40% improvement over existing Persian image captioning models in standard metrics and a 9% mean improvement in Hit@K compared to the CLIP baseline. The framework's modular design enables domain-specific adaptations, offering a blueprint for low-resource language retrieval systems. A subset of our implementation, including code and datasets, is available at: https://***/PTIR.

关键词： Training measurement Adaptation models Technological innovation Databases Computational modeling Pipelines Diversity reception Vectors Standards

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601462

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A Toolbox for Simulation and Analysis of Structured Light 3D Reconstruction systems 3rd

A Toolbox for Simulation and Analysis of Structured Light...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Li, Yiming Zhang, Dongxiang Wang, Hao Zhang, Chaobo Wang, Xiaohao Yang, Chunhua Gui, Weihua Liang, Xiaojun Li, Xinghui Pengcheng Laboratory Shenzhen 518000 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China School of Electronic and Information Engineering South China University of Technology Guangzhou 510640 China Tsinghua Berkeley Shenzhen Institute Tsinghua University Shenzhen 518055 China School of Automation Central South University Changsha 410083 China

ISBN: (纸本)9789819645022

Structured light 3D measurement technology (SL-3D) has achieved extensive industrial applications, owing to its non-contact, high-precision, and rapid measurement capabilities. Recently, deep learning-based SL-3D has advanced quickly, outperforming traditional methods in many aspects. However, the high cost of constructing real datasets has impeded this technology’s development. To address this challenge, we have developed the first user-friendly simulation toolbox for structured light 3D measurement systems. This system generates and saves a complete set of 46 ‘.mat ’ files based on multi-frequency and N-step phase shifts within 4 s. We first introduced discontinuous geometric shapes, making simulated objects more representative of real-world scenarios. Additionally, our simulation first includes realistic measurement environment features such as shadow regions and various types of noise, including higher harmonic noise, random noise, and Gaussian noise, with controllable types and levels, closely mimicking actual measurement conditions. This toolbox holds significant potential for accelerating advancements in structured light 3D reconstruction and is publicly available at: https://***/LiYiMingM/Structured-Light-3D-Toolbox. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： discontinuous geometric shapes simulation toolbox Structured light 3D measurement

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