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Spatial-Temporal Graph Neural Networks Fusing Multiple Data 4

Spatial-Temporal Graph Neural Networks Fusing Multiple Data

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4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Xu, Haonan Xue, Wenfang Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Beijing China

ISBN: (纸本)9798350321951

Recently, quantitative investment has become a new way of investment finance, which combines the model with the computer to predict the investment stock trend more efficiently and accurately. Stock trend forecasting is one of the difficult and important tasks in modern quantitative investment systems. With the development of artificial intelligence technology, many deep learning models have emerged. the application of deep learning in the stock market not only reduces the difficulty of stock analysis and trend prediction, but also introduces new investment methods and ideas for investors. However, most of the models are based on volume and price data, and the consideration of stock relationships are relatively simple, so we propose a Spatial-Temporal Graph Neural Network model fusing Barra factors (BSTGNN), which integrates factor knowledge, mines potential inter-stock relationships, and fully consider the heterogeneity of the relationship. the experiments conducted on the real-world stock market dataset prove that BSTGNN perform better than the baseline methods. © 2022 IEEE.

关键词： Investments

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Research on Deep Applications Based on Visual recognition Technology 4th

Research on Deep Applications Based on Visual Recognition Te...

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4th international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024

作者： Yu, Yi Sebial, Archival Canillo, Angie Ceniza Jiangxi Vocational College of Industry and Engineering Pingxiang China Department of Computer Information Sciences and Mathematics School of Arts and Sciences University of San Carlos Cebu Philippines

ISBN: (纸本)9789819653171

this paper conducts an in-depth study on the application of YOLO-v4, Faster R-CNN, and DenseBox neural networks for training deep learning models, using smart restaurants and dish recognition as examples. Unmanned settlement systems in smart restaurants can effectively reduce labor costs and improve operational efficiency. the core of an unmanned settlement system is the accurate identification of dishes. To address this issue, deep learning methods were used to recognize images from Chinese dish databases. YOLO-v4, Faster R-CNN, and DenseBox neural networks were selected for training deep learning models, and their recognition results were compared and analyzed. Experimental results show that the Faster R-CNN network model performs the best, with the ability to automatically extract image features, achieving a recognition rate of 95.4% for dishes, a recall rate of 82%, and an F1 value of 88.2%. this study provides a reliable foundation for the intelligent recognition of dishes and the application of smart catering. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning DenseBox Dish recognition Faster R-CNN model Smart Catering YOLO-v4

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Detection and recognition of Deformed Multiple QR Codes Based on SR_ESAGAN Algorithm 4

Detection and Recognition of Deformed Multiple QR Codes Base...

引用

4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Zhao, Meijuan Li, Juntao Wang, Haiquan Beijing Wuzi University Beijing Key Laboratory of Intelligent Logistics Systems Beijing China Zhongyuan University of Technology Zhongyuan-Petersburg Aviation College 41 Zhongyuan Road Zhengzhou450007 China

ISBN: (纸本)9798350321951

In the study of detection and recognition of distorted, skewed, and other deformation multi-QR codes using the YOLO_CBAM algorithm, there were problems that the detection effect was poor in the case of stacked QR codes, and the small resolution QR codes couldn't be well recognized. In this paper, we used the Real-ESRGAN model for super-resolution reconstruction of polymorphic QR code images. Combining the characteristics of the multi-QR code dataset, we used 3 convolutional layers to reduce the complexity of image super-resolution processing and replaced the LEAKYRELU activation function in the discriminative network with the GELU function. then we conducted experiments using the SR_ESAGAN algorithm. Experiments showed that after the improvement, the detection rate and recognition rate of multiple QR codes with deformations were enhanced by 3.71% and 5.27%, respectively, compared to those using only the YOLO_CBAM algorithm. © 2022 IEEE.

关键词： Optical resolving power

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Machine Learning Assisted intelligent Reflecting Surface MIMO Communication-Gateway for 6G—A Review 4th

Machine Learning Assisted Intelligent Reflecting Surface MIM...

引用

4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Srivastava, Praveen Khera, Shelej School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara144411 India Department of Electronics and Communication Engineering Pranveer Singh Institute of Technology Kanpur208001 India

ISBN: (纸本)9789819914784

intelligent reflecting surfaces (IRSs) are gaining interest due of their potential coverage and spectral efficiency advantages. However, there are a number of issues that must be resolved in order to materialise these surfaces in practise. A collaborative design for the BS and IRS beamformers is necessary, and the IRS components must be easily modified accordingly. Channel information is crucial to the accuracy of beamformer design deep learning (DL) is a data-driven technique that is essential in overcoming these difficulties. Deep learning is resistant to data flaws and environmental changes because of its faster computation speeds and model-free structure. DL has been demonstrated to be effective at the physical layer for active/passive beamforming, channel estimation, and IRS signal recognition utilising supervised, unsupervised, and reinforcement learning architectures. the methods for creating deep learning-based IRS-assisted wireless systems are summarised in this article. It gives insight to the different deep learning techniques like reinforcement learning, supervised learning, and federated learning which could be used along with IRS. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Beamforming

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recognition of Handwritten Digits Using Convolutional Neural Network in Python and Comparison of Performance for Various Hidden Layers 6th

Recognition of Handwritten Digits Using Convolutional Neural...

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Gupta, Himansh Kaur, Amanpreet Kavita Verma, Sahil Rawat, Poonam Department of CSE Chandigarh University Gharuan India Uttaranchal University Dehradun India

ISBN: (纸本)9789819930098

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic change in the field of machine learning by making it more artificially intelligent. Deep learning is used in various fields because of its wide range of applications such as surveillance, health, medicine, sports, robotics, and drones. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that combine Artificial Neural Network (ANN) and keep updating deep learning strategies. It is used in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. the goal of this paper is to observe the change of accuracies of CNN to classify handwritten digits using various numbers of the hidden layers and epochs and to see the difference between the accuracies. For this performance evaluation of CNN, we performed our experiment using the Modified National Institute of Standards (MNIS) and Technology (MNIST) dataset. Further, the network is trained using the stochastic gradient descent and the backpropagation algorithm. Deep learning is a machine learning technique that teaches the computers to do what comes naturally to the humans. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stochastic systems

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Single Plane Temperature Sensors Placement Optimization for Aligned Air-Cooled Cylindrical LiB Module 4

Single Plane Temperature Sensors Placement Optimization for ...

引用

4th IEEE international conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Daniels, Rojo Kurian Kumar, Vikas Prabhakar, Aneesh Mnit Jaipur Centre for Energy and Environment Rajasthan Jaipur302017 India

ISBN: (纸本)9798350383997

In lithium-ion batteries (LiBs), thermal runaway (TR) poses a significant risk of fire that requires early identification to control its propagation throughout the battery module and possible module-wide fires. In an attempt to improve the TR cell position prediction performance by Machine Learning (ML) models in the aligned arrangement of cells and single-mid plane uniform thermal sensor distribution scenario that under-performed in the previous work, the present study incipiently implements the randomization approach of temperature sensor distribution on the single-mid plane of the battery computational domain. In addition to the regular sensor distribution pattern discussed in the previous study for the aligned cell arrangement and single-mid plane regular thermal sensor distribution scenario, two more random patterns of distribution of temperature sensors were considered in the present study, maintaining the initial number of temperature sensors distributed to be 100 in all three cases. the work aims to optimize the temperature sensors for all the chosen sensor distribution patterns, which could be further used to develop an ML model and test its accuracy in predicting the cell position undergoing TR in the battery domain under different operation conditions. To identify the optimized sensors, the Pearson Correlation Coefficient (heat map) optimization approach is implemented by analyzing the correlations between temperature sensors and potential fault positions based on the coefficient limiting threshold of 0.85. Utilizing the optimized set of temperature sensors obtained from this study for each of the chosen distribution patterns of temperature sensors, the effects of randomization of temperature sensor distribution on a single-mid plane and associated variation in the TR cell position prediction accuracy from the ML can be accounted for. © 2024 IEEE.

关键词： intelligent systems

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Research Data Reusability with Content-Based Recommender System 1

引用

4th international conference on Deep Learning theory and Applications (DeLTA)

作者： Yazdi, M. Amin Politze, Marius Heinrichs, Benedikt Rhein Westfal TH Aachen IT Ctr Seffenter Weg 23 Aachen Germany

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

the use of content-based recommender systems to enable the reusability of research data artifacts has gained significant attention in recent years. this study aims to evaluate the effectiveness of such systems in improving the accessibility and reusability of research data artifacts. the study employs an empirical study to identify content-based recommender systems' strengths and limitations for recommending research data-collections (repositories). the empirical study involves developing and evaluating a prototype content-based recommender system for research data artifacts. the literature review findings reveal that content-based recommender systems have several strengths, including providing personalized recommendations, reducing information overload, and enhancing retrieved artifacts' quality, especially when dealing with cold start problems. the results of the empirical study indicate that the developed prototype content-based recommender system effectively provides relevant recommendations for research data repositories. the evaluation of the system using standard evaluation metrics shows that the system achieves an accuracy of 79% in recommending relevant items. Additionally, the user evaluation of the system confirms the relevancy of recommendations and enhances the accessibility and reusability of research data artifacts. In conclusion, the study provides evidence that content-based recommender systems can effectively enable the reusability of research data artifacts.

关键词： Recommender systems Content-based RDF graph data models Research data management

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intelligent Concrete Surface Cracks Detection using Computer Vision, pattern recognition, and Artificial Neural Networks 4

Intelligent Concrete Surface Cracks Detection using Computer...

引用

4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Mirbod, Majid Shoar, Maryam Department of Industrial Management Tehran North Branch Islamic Azad University Tehran Iran

this paper presents an intelligent concrete surface crack detection model, using machine vision, pattern recognition, artificial neural networks, and histogram of oriented gradients to feature extraction. this is an applied research category that so far, has been used in face recognition systems, and scaled conjugate gradient to training purpose multilayer neural networks, to achieve the monitoring of concrete surfaces to increase the protection that these are the innovations of this research. the main purpose of this research is to provide a reliable method for inspecting sensitive structures such as bridges using a continuous intelligent inspection system consisting of a camera and intelligent software. For this purpose, we used the Utah State University image dataset that contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. Moreover, it includes data on cracks as narrow as 0.06 mm and a width of 25 mm with a variety of obstructions, shadows, surface roughness, scaling, edges, holes, and background debris. the ANN proposed in this study can recognize cracking images from non-cracking images. the efficiency of this network is 84.88% and it reduces network error from 0.631 to 0.199 and gradient index from 25.5 to 0.0801. this model will be useful for structural health monitoring and other sensitive surfaces such as aircraft engine shells and train moving rails. Based on the obtained results, the difference between the proposed method and other neural networks such as Convolutional Neural networks is that the former is simpler in coding and needs cheaper hardware. © 2022 Elsevier B.V.. All rights reserved.

关键词： Multilayer neural networks

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An intelligent recognition Method and System of “Phubber” based on Object Detection and Pose Estimation Algorithm

An Intelligent Recognition Method and System of “Phubber” ...

引用

pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Tongshu Liu High School Affiliated to Shanghai Jiao Tong University Shanghai China

the existing research works regard the recognition of “phubber” as single task of object detection or human pose recognition. Its disadvantage is that its accuracy is insufficient, and it cannot effectively solve the problems of human body behavior understanding and human body overlaps. As a result, this paper provides an intelligent “phubber” recognition method that integrates object detection and pose estimation algorithms. the basic idea is to use the human pose estimation network to predict key points of each human body in video frame image, and then comprehensively determine whether the human body is a “phubber” according to the defined body behavior judgment rules, the type of hand-held object and distance of the hand-held objects from body. the experimental results show that this method outperforms existing baseline models of object detection in terms of recognition accuracy. As well, a system for cameras-based recognizing phubber” is developed based proposed method, and a verification of phubber” recognition on campus is conducted, reaching an overall recognition accuracy of 89.7%.

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SSMT: Few-Shot Traffic Forecasting with Single Source Meta-transfer 27th

SSMT: Few-Shot Traffic Forecasting with Single Source Meta-t...

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27th international conference on pattern recognition, ICPR 2024

作者： Bhaumik, Kishor Kumar Kim, Minha Niloy, Fahim Faisal Ali, Amin Ahsan Woo, Simon S. Sungkyunkwan University Seoul Korea Republic of University of California Riverside Riverside United States Center for Computational and Data Sciences Independent University Dhaka Bangladesh

ISBN: (纸本)9783031781940

Traffic forecasting in intelligent Transportation systems (ITS) is vital for intelligent traffic prediction. Yet, ITS often relies on data from traffic sensors or vehicle devices, where certain cities might not have all those smart devices or enabling infrastructures. Also, recent studies have employed meta-learning to generalize spatial-temporal traffic networks, utilizing data from multiple cities for effective traffic forecasting for data-scarce target cities. However, collecting data from multiple cities can be costly and time-consuming. To tackle this challenge, we introduce Single Source Meta-Transfer Learning (SSMT) which relies only on a single source city for traffic prediction. Our method harnesses this transferred knowledge to enable few-shot traffic forecasting, particularly when the target city possesses limited data. Specifically, we use memory-augmented attention to store the heterogeneous spatial knowledge from the source city and selectively recall them for the data-scarce target city. We extend the idea of sinusoidal positional encoding to establish meta-learning tasks by leveraging diverse temporal traffic patterns from the source city. Moreover, to capture a more generalized representation of the positions we introduced a meta-positional encoding that learns the most optimal representation of the temporal pattern across all the tasks. We experiment on five real-world benchmark datasets to demonstrate that our method outperforms several existing methods in time series traffic prediction. Our code is available at https://***/Kishor-Bhaumik/SSMT. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： intelligent systems

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