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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Hybrid Framework for Real-Time Traffic Flow Estimation Using Breadth-First Search 11

Hybrid Framework for Real-Time Traffic Flow Estimation Using...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Mahdaviabbasabad, Sajjad Vanderhoydonc, Ynte Vandenberghe, Roeland Mercelis, Siegfried University of Antwerp imec IDLab Faculty of Applied Engineering Sint-Pietersvliet 7 Antwerp2000 Belgium Transport & Mobility Leuven Diestsesteenweg 71 Leuven3000 Belgium

ISBN: (纸本)9789897587450

Traffic flow data is essential for urban planning, logistics, transport management, and similar applications. However, achieving full sensor coverage across a road network is often infeasible due to high installation and maintenance costs. Simulation data from traffic models can help in filling this gap. However, calibrating and validating these traffic models is time-consuming. this paper presents a framework that combines real-time traffic flow predictions from sensor-equipped road segments with 24-hour static simulation data across an entire network. By applying a method based on the Breadth-First Search algorithm, this framework updates network-wide traffic flow by utilizing the data-driven predictions at sensor-equipped road segments and simulation data. Evaluation on a network with over 27000 road segments shows that this approach improves prediction accuracy over static simulation and is viable for real-time deployment. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Urban planning

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Real-time Arm Motion Tracking and Hand Gesture Recognition Based on a Single Inertial Measurement Unit 11

Real-time Arm Motion Tracking and Hand Gesture Recognition B...

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11th international conference on Internet of things: Systems, Management and Security, IOTSMS 2024

作者： Chang, Tien-Chiao Wu, Yu-Chi Han, Chin-Chuan Chang, Chao-Shu National United University Dept. of Electrical Engineering Miao-Li Taiwan National United University Dept. of Computer Science and Information Engineering Miao-Li Taiwan National United University Dept. of Information Management Miao-Li Taiwan

ISBN: (纸本)9798350366501

With the development of virtual reality (VR) and augmented reality (AR) devices, handheld controllers and camera-based hand tracking are the most common methods for interacting with the virtual world. this paper proposes arm motion tracking and hand gesture recognition methods based on a single IMU (Inertial Measurement Unit) sensor embedded in a wristband. this approach is more convenient than handheld controllers and more energy-efficient than camera-based tracking. We utilize two deep learning models: one for arm motion tracking and the other for hand gesture recognition. When training the arm motion tracking model, we use two IMU sensors-one placed on the wrist to provide forearm pose data and the other placed above the elbow to provide upper arm pose data. We use the elbow's IMU data as the reference for training the model and calculate the real-time upper arm pose vector based on the wrist's pose data. We train the hand gesture recognition model using only the wrist-mounted IMU sensor. this model captures different vibrations in the IMU during various gestures (such as grab, release, snap, and tap) for training. We achieve real-time arm motion tracking and hand gesture recognition using only a single IMU sensor. the angle error for arm motion tracking is only 15 degrees, and the hand gesture recognition accuracy reaches 95%. © 2024 IEEE.

关键词： Gesture recognition

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Joint Optimization and Scheduling of Computing and Caching Resource for Blockchain-Empowered IoT via CDRL Approach 11

Joint Optimization and Scheduling of Computing and Caching R...

引用

2023 IEEE 11th international conference on Information, Communication and Networks, ICICN 2023

作者： Pei, Pan Li, Meng Huang, Yudian Yang, Ruizhe Sun, Yanhua Wang, Zhuwei Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350314014

Recently, the rise of the virtual reality (VR) and augmented reality (AR) application has led the significant evolution of smart Internet of things (IoT). To improve the transmission efficiency and system overheads for IoT system, edge computing and caching have been treated as a prospective technology. However, there are some inevitable issues need to be emphasized such as limited computing and caching resources, as well as centralized learning and training by agents for resource allocation. In order to tackle above issues, in this article, based on deep reinforcement learning (DRL), we propose and introduce a novel collective and intelligent optimization algorithm - collective deep reinforcement learning (CDRL). through collective learning and training by multi-agents, it can avoid excessive consumption of energy resources, and realize intelligent resource allocation. Moreover, blockchain technology is viewed to guarantee the security of shared data for the cloud-edge-end collaborative IoT network. optimized design for the offloading decision of computation tasks of VR device and the consensus task of blockchain, as well as caching decision, the computing overheads and energy consumption aim to be minimized. then, we design the formulated problem as a Markov decision process, the optimal scheduling and policies can be achieved based on the CDRL. Simulation results reveal that the proposed scheme outperforms other existing schemes. © 2023 IEEE.

关键词： Reinforcement learning

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A Cube-enabled Cloud Geoprocessing Engine for Big Earth data 11

A Cube-enabled Cloud Geoprocessing Engine for Big Earth Data

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Liu, Ruixiang Yue, Peng Yu, Dayu Wang, Kaixuan Deng, Haipeng Li, Hao Wuhan University School of Remote Sensing and Information Engineering Wuhan China Wuhan University Hubei Province Engineering Center for Intelligent Geoprocessing Wuhan China

ISBN: (纸本)9798350303513

In this age of data explosion, the ever-increasing volume of Earth observation (EO) data poses a big challenge in terms of EO data storage structures and geoprocessing methods. Currently, data cubes and cloud computing show promise in facilitating data storage and data processing owing to their exceptional scalability, elasticity, and cost-efficiency. this paper presents a cube-enabled cloud geoprocessing engine that provides a fast and efficient way to conduct geospatial data processing. the engine is equipped with the capability to provide cloud storage and processing resources to meet the demands for large-scale geospatial data processing. this engine not only incorporates a number of cloud-native geospatial operators based on Apache Spark but also integrates existing operators from open-source Geographic Information System (GIS) software, thus delivering an extensive array of computational methodologies. In addition, it offers two modes for geoprocessing: on-the-fly and batch, which improve the user experience in performing large-scale geoprocessing. © 2023 IEEE.

关键词： Engines

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Analyzing the Effects of K-Means and Random Forest Algorithms on Resume Classification through Manipulation of Cluster Sizes and datasets 18

Analyzing the Effects of K-Means and Random Forest Algorithm...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Patil, Sweety Mahesh Shinde, Vinayak D. Shree L.R. Tiwari College of Engineering Mira Road Mumbai India

ISBN: (纸本)9789380544519

In online job recruitment, precise job classifications are crucial for both job seekers and recruiters. Categorizing resumes is a crucial stage in the multi-step recruitment process. the success of an organization heavily relies on the personality of its candidates. this system is based on two modules. Questionnaires in the MCQ format are used in Module 1 to collect input from user. Module 1 includes pre-processing using min-max scaling, enrichment using cluster optimization technique, pattern recognition using k-means clustering algorithm, and by changing the cluster's size, personality traits can be identified and personality traits predicted. the output of Module 1 is detecting appropriate personality based on categories are Extroversion, Neuroticism, Agreeableness, Conscientiousness and Openness. When appropriate category of personality is detected in Module 1, which will be helpful in further analysis of job, resume will be passed to the system as input in PDF format to Module 2. Module 2 is based on two stages. data collection, text vectorization on training datasets, and job title encoding are all included in stage one. Stage 2 involves input resumes in PDF format using the PyPDF2 library. the NLTK library and regular expression are used to pre-process resumes, followed by TF-IDF vectorization and Random Forest Algorithm to classify them based on similarity to job title. Accurate resume classification can be achieved by optimizing K-Means and Random Forest Algorithms, yielding a 70% accuracy rate. Ensemble learning techniques or Linear Support Vector Classifier (SVC) can potentially enhance accuracy. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Classification Clustering data sets job seekers K-means Machine learning (ML) Random Forest(RF) recruiters Resume Resume classification

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Crude Palm Oil Price Forecasting Based on ECoS-MARS: in data Science Models 11

Crude Palm Oil Price Forecasting Based on ECoS-MARS: in Data...

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11th international conference on Cyber and IT Service Management, CITSM 2023

作者： Al-Khowarizmi Ramlan, Fitria Wulandari Efendi, Syahril Lubis, Arif Ridho Riza, Ferdy Amrullah Sary, Yoshida Universitas Muhammadiyah Sumatera Utara Department of Information Technology Medan Indonesia National University Ireland College of Engineering and Informatics Galway Ireland Universitas Sumatera Utara Department of Computer Science Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering & Informatics Medan Indonesia Universitas Muhammadiyah Sumatera Utara Department of Information System Medan Indonesia

ISBN: (纸本)9798350305968

the crude palm oil (CPO) industry is highly competitive due to fluctuating and unpredictable prices. To accurately predict future CPO prices, a forecasting technique is necessary. this can be achieved through various regression techniques and data mining approaches. this paper proposes a Multivariate Adaptive Regression Splines (MARS) model, which utilizes the ECoS machine learning algorithm to obtain optimal forecasting values. this research mainly focuses on forecasting CPO prices using the ECoS-MARS approach. the functional basis (BF) validation has yielded an accuracy of 0.0532%. Furthermore, validation has been conducted to achieve accurate forecasting values, resulting in an accuracy of 0.11714%. the best parameter combination for this forecasting model consists of a learning rate of 0.6 for both learning rates 1 and 2, a sensitivity threshold of 0.3, and an error threshold of 0.05. © 2023 IEEE.

关键词： Forecasting

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Heterogeneous Multi-core Task Scheduling Based on Adaptive Weight Whale Optimization Algorithm 11

Heterogeneous Multi-core Task Scheduling Based on Adaptive W...

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11th international conference on intelligent Computing and Wireless Optical Communications, ICWOC 2023

作者： He, Yulu Li, Dejian Sen, Haisen Huang, Tiancong Liu, Guojin Jiang, Yang Chongqing University School of Microelectronics and Communication Engineering Chongqing China Microelectronics Technology Company Limited Beijing Smart-chip Beijing China State Grid Shanxi Electric Power Company Jinzhong China

ISBN: (纸本)9798350321791

An adaptive weight whale optimization algorithm (AWWOA) is proposed to solve the task adaptive scheduling problem in heterogeneous computing systems, improve the efficiency of heterogeneous multi-core task scheduling, and shorten the task completion time in this paper. the AWWOA adds weight factors to the position update of the whale algorithm and makes use of the dynamic changes of weight factors to enhance the global search ability to a great extent so that the algorithm can search for the optimal solution more precisely in the local development stage and avoid the whale population falling into the local optimal. the experimental results indicate that the AWWOA is able to effectively avoid falling into local optimization in the process of task scheduling optimization and improve the efficiency of task scheduling in heterogeneous multi-core systems. © 2023 IEEE.

关键词： Efficiency

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Grab and Go: Leveraging Computer Vision for intelligent Shopping 11

Grab and Go: Leveraging Computer Vision for Intelligent Shop...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Awad, George Fawzy, Joseph Yacoub, Sandra Abdelgalil, Mahmoud Khaled, Nada Bernaba, Jan Mohamed, Omar Hindy, Hanan Ain Shams University Faculty of Computer and Information Sciences Artificial Intelligence Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Cairo Egypt Ain Shams University Faculty of Computer and Information Science Computer Sciences Cairo Egypt

ISBN: (纸本)9798350322101

In an effort to address the challenges posed by long queues and slow payment processes in shopping, new technologies like Amazon's "Just Walk Out"have emerged to enable checkout-free purchases. Computer vision methods, specifically the application of On-Shelf Availability (OSA) monitoring, have been highlighted as crucial for efficient inventory management in grocery stores, using semi-supervised learning and YOLO deep learning architecture. However, the high cost associated with implementing such comprehensive smart market solutions, including the need for RFID sensors on every item, may hinder adoption in developing countries. this paper aims to provide a more affordable alternative by leveraging computer vision techniques, replacing sensors with cameras, and utilizing deep learning models. the proposed approach involved training YOLO V5 and various versions of YOLO V8 models, incorporating annotations, data augmentations, and exploring different techniques. the achieved results demonstrated a mean Average Precision (mAP) of 78.3%, precision of 64.2%, and recall of 61.4% using YOLO V8 with augmentations such as Mosaic, Horizontal Flip, Blur, etc. While the results were considered acceptable even with limited access to natural images, the performance is expected to improve further with increased access to a wider range of training images, especially natural ones. © 2023 IEEE.

关键词： Computer vision

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Metrics and Testing Methods for Artificial Intelligence Software Quality Models and their Application Examples 11

Metrics and Testing Methods for Artificial Intelligence Soft...

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11th international conference on Dependable Systems and their Applications, DSA 2024

作者： Guo, Mengfei Guo, Dong Li, Mo China Aerospace Academy of Systems Science and Engineering Beijing China

ISBN: (纸本)9798331532390

As artificial intelligence technology rapidly advances, AI software is being increasingly employed across various industries. To ensure the quality and reliability of these software systems, the paper extends the quality measurement and testing methods for artificial intelligence software by utilizing the ISO/IEC 25059 quality model. It not only refines traditional software testing methods to better suit artificial intelligence software but also introduces specific testing methods for quality attributes unique to AI systems, which include five main characteristics: User contrallability ensures timely user intervention in system operations to avoid potential errors or security risks, thereby enhancing system usability;transparency increases the level of information disclosure, allowing users and stakeholders to better understand and trust the AI system, facilitating greater user acceptance;functional adaptability emphasizes the system' s learning and adaptability in dynamic environments, ensuring it can effectively handle changing data and diverse usage scenarios, which is crucial for maintaining functionality;robustness assesses the stability and reliability of the system under abnormal inputs or attacks, ensuring it can continue operating under all conditions;and intervenability ensures users can quickly and effectively manage system faults, thereby minimizing potential damage. To validate the proposed quality measurement methods, face recognition software based on deep learning was employed as a case study to evaluate its performance through experimental analysis. these validations not only demonstrated the effectiveness of the quality measurement methods in practical applications but also further enriched the testing methods for AI software. © 2024 IEEE.

关键词： Software quality

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ECG-based intelligent model for coronary heart disease screening 24

ECG-based intelligent model for coronary heart disease scree...

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11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Chen, Xin Niu, Yanxiang Fan, Jianqi Lu, Lu Fan, Haojun Emergency Medicine Research Institute Tianjin University Tianjin China

ISBN: (纸本)9798400718274

As the number of patients with coronary heart disease increases year by year, the intervention and management of coronary heart disease (CHD) patients have become the top priority of public health management. Electrocardiogram (ECG) has the characteristics of non-invasive, low cost, is recognized as an effective screening method for cardiovascular diseases, currently mainly used in hospital patients with heart disease detection, limited by the size of the ECG equipment and acquisition channels, it is difficult to be widely used in the community environment. In this study, we first analyzed and extracted patient information from the MIME-IV database and extracted 12-channel ECG data of 4309 patients with coronary heart disease and 4293 healthy subjects from the MIMIC ECG data sub-database. Secondly, a fusion model for early diagnosis of CHD patients based on the ReliefF module and ResNet-34 is proposed. the feature selection function of the ReliefF module can screen the features and sort the ECG channels by weight. Finally, we explored the performance of models trained using different combinations of ECG channels. the experimental results showed that the fusion model achieved the best fitting effect in the early diagnosis of CHD patients, and the classification accuracy of our model reached 96.6%, which was better than the existing CNN-LSTM model of 93.7%. At the same time, sorted the input channels of the model (II, V1, AVR, V3, AVL, I, V4, V2, III, V5, AVF, V6) and the recognition accuracy of the model trained by using four-channel ECG data (II, V1, AVR, V3) was 91.2%. the model performance has reached the approximate performance of the model trained using 12 channels while the model's response time is significantly reduced (Corresponding time is 2.58s and 4.72s, respectively). the requirement on the computing power and volume of the acquisition device hardware is significantly reduced, making the model easier to transplant and apply on the existing platform. Copyr

关键词： Digital storage

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