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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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CAPTURING thE MOTION OF EVERY JOINT: 3D HUMAN POSE AND SHAPE ESTIMATION WIth INDEPENDENT TOKENS 11

CAPTURING THE MOTION OF EVERY JOINT: 3D HUMAN POSE AND SHAPE...

引用

11th international conference on learning Representations, ICLR 2023

作者： Yang, Sen Heng, Wen Liu, Gang Luo, Guozhong Yang, Wankou Yu, Gang School of Automation Southeast University China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Tencent PCG China

In this paper we present a novel method to estimate 3D human pose and shape from monocular videos. this task requires directly recovering pixel-alignment 3D human pose and body shape from monocular images or videos, which is challenging due to its inherent ambiguity. To improve precision, existing methods highly rely on the initialized mean pose and shape as prior estimates and parameter regression with an iterative error feedback manner. In addition, video-based approaches model the overall change over the image-level features to temporally enhance the single-frame feature, but fail to capture the rotational motion at the joint level, and cannot guarantee local temporal consistency. To address these issues, we propose a novel Transformer-based model with a design of independent tokens. First, we introduce three types of tokens independent of the image feature: joint rotation tokens, shape token, and camera token. By progressively interacting with image features through Transformer layers, these tokens learn to encode the prior knowledge of human 3D joint rotations, body shape, and position information from large-scale data, and are updated to estimate SMPL parameters conditioned on a given image. Second, benefiting from the proposed token-based representation, we further use a temporal model to focus on capturing the rotational temporal information of each joint, which is empirically conducive to preventing large jitters in local parts. Despite being conceptually simple, the proposed method attains superior performances on the 3DPW and Human3.6M datasets. Using ResNet-50 and Transformer architectures, it obtains 42.0 mm error on the PA-MPJPE metric of the challenging 3DPW, outperforming state-of-the-art counterparts by a large margin. Code will be publicly available. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Image enhancement

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Enumerable learning-Based Machine learning Techniques for Sentiment Analysis 11

Enumerable Learning-Based Machine Learning Techniques for Se...

引用

11th IEEE international conference on Communication Systems and Network Technologies, CSNT 2022

作者： Singh, Deepti Yadav, Himanshu Agrawal, Chetan Radharaman Institute of Technology and Science Computer Science and Engineering MP Bhopal India

ISBN: (纸本)9781665480383

People express their views on goods, services, governments, and events via words and phrases on social media. Sentiment analysis is a technique used in natural language processing to gather positive and negative feedback on a piece of writing on social media. Academics have been forced to research sentiment analysis by exponential growth in business and ecommerce companies. this study examined emotions in two distinct domains: one for electronics product evaluations and another for film reviews. Due to the enormous volume of data gathered for analysis, it is referred to as massive data. this vast data is multidimensional and includes useless information. Furthermore, this data complicates the analysis since it reduces the effectiveness of machine learning categorization algorithms. As a consequence, the data must be cleansed and pertinent features identified. Numerous methods have been used to eliminate noise from data and identify essential components. there are two methods for determining suitable features: feature selection and feature extraction. the research is split into three stages: the first step involves converting unstructured data to structured data and choosing relevant and feature information. Second, machine learning classification algorithms are employed in conjunction with feature selection approaches, and third, machine learning algorithms are used in conjunction with ensemble learning methods. © 2022 IEEE.

关键词： Sentiment analysis

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OGEScript: an OGC-oriented Interoperable Script API for Online Geospatial Analysis 11

OGEScript: an OGC-oriented Interoperable Script API for Onli...

引用

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

作者： Wang, Kaixuan Yue, Peng Yu, Dayu Liu, Ruixiang Deng, Haipeng Liu, Haoyu 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

Recent advance in online geospatial analysis shows the promise to adopt an online interactive development environment (IDE). Scripts in IDE can be improved to follow geospatial 'recipes' such as geospatial concepts and operators to facilitate online geoprocessing. this could be done by incorporating OGC (Open Geospatial Consortium) style APIs into scripts, thus improving the interoperability of scripts across online coding environments in different geospatial cloud platforms. the paper proposes an OGC-oriented interoperable script API called OGEScript. By leveraging the object-oriented design pattern, geospatial resources defined in OGC specifications are mapped to classes within the OGEScript. this mapping allows for a uniform script-based object access to data and processing functions (traditionally in OGC APIs) available in background cloud platforms. Existing scripting languages like Python can be extended to develop such functionality. the approach is implemented in a cloud platform named Open Geospatial Engine (OGE) to demonstrate the applicability of the approach. © 2023 IEEE.

关键词： Interoperability

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Automatic License Plate Recognition: automated Tax Verification for Registered Vehicles via SAMBARA Server 10

Automatic License Plate Recognition: Automated Tax Verificat...

引用

10th international conference on Wireless and Telematics, ICWT 2024

作者： Nawawi, Aif Umar Dendyana, Arya Muhammad Prastyo, Galuh Yudha Al Azhima, Silmi Ath thahirah Hakim, Nurul Fahmi Arief Ridwan, Azwar Mudzakkir Universitas Pendidikan Indonesia Dept. of Electrical Eng. Education Bandung Indonesia UIN Sunan Gunung Djati Dept. of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350377057

the technology known as Automatic License Plate Recognition (ALPR) originated from the application of computer vision, a subfield of artificial intelligence systems. the purpose of developing this ALPR system was to verify the tax status and comprehensive vehicle information in the West Java region of Indonesia. the objective of this study was to instill in each motorist a sense of traffic order and to assist Indonesia in the implementation of an intelligent Traffic System (ITS). In order to operate efficiently, the system incorporated in this design is constructed using a variety of Python programming language modules. YOLOv8 (You Only Look Once version 8), EasyOCR, OpenCV, and Selenium are the primary Python modules utilized;additional modules include RegEx (Regular Expression), Time, Numpy, and BytesIO. In this application, number plates are searched for and detected using the dilation method so that EasyOCR can extract every character with minimal error. Additionally, machine learning serves as the foundation for each of the aforementioned procedures and processes. the fundamental computation of machine learning from bespoke data sets is performed in Google Colab. the outcome of this investigation is a system capable of furnishing information pertaining to motor vehicle systems to an individual who has failed to remit vehicle tax. Aside from that, the developed system can detect counterfeit license plates, making it a valuable tool for educating drivers about the importance of remaining vigilant. © 2024 IEEE.

关键词： Unsupervised learning

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Control Configuration Based on AOE and Its Application in the Power Allocation of BESSs 11

Control Configuration Based on AOE and Its Application in th...

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11th international conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Dong, Shufeng Hua, Yibo Tang, Kunjie Xu, Chengsi Zhejiang University College of Electrical Engineering Hangzhou China

ISBN: (纸本)9798350399660

As modern industrial control shifts to a more intelligent and distributed system, control configuration technology has become an important method to achieve the control objective. However, the current control configuration technology is limited by high learning cost and long deployment cycles in practical applications. Based on the rich engineering experience in the field of electrical, this paper proposes a novel control configuration technology based on activity on edge network and provides the relevant configuration details. Our method has shown advantages such as intuitiveness, low learning cost, and short deployment cycles over existing control configuration technology, which is validated in a case study with the method directly applied to power allocation of battery energy storage system (BESS). © 2022 IEEE.

关键词： Battery storage

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A Critical Analysis of the Methods Used to Classify Medical data 11

A Critical Analysis of the Methods Used to Classify Medical ...

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11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Ramteke, Nidhi Maidamwar, Priya G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350348422

In order to handle medical data, it is necessary to first sort the collected information into several categories. this method is one that has been implemented successfully in a variety of other contexts as well. the difficulties that are involved with the medical data classification are numerous and intricate, and they are easily affected by malfunctions of instruments and noise that occur during the process of data processing. this is because the classification of medical data is dependent on the accuracy of the data processing instruments. Because of this piece of information, the physician will have a substantially more difficult time determining the exact location of the lesion in the body. As a result of this, the topic of how to enhance the efficiency of medical data categorization is beneficial to the growth of technology as well as the use of that technology in the real world. this article provides a concise account of the history of medical data classification, the current state of the trend in the development of classification technology, as well as the fundamental information points. In light of these findings, the study presented here presents a strategy for classifying medical data by using the CNN model as the organizing principle. this experiment, like the previous one, demonstrates that the categorization impact is beneficial. © 2023 IEEE.

关键词： Deep learning

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Deep learning-Based Recommender Systems—A Systematic Review and Future Perspective 11th

Deep Learning-Based Recommender Systems—A Systematic Review...

引用

11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Krishnamoorthi, S. Shyam, Gopal K. Department of Computer Science and Engineering School of Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819967056

With the ever-increasing volume of online information, recommender systems (RS) have been an effective technique to overcome such information overload. Given its widespread implementation in a variety of web applications, the value of RS cannot be emphasized enough. RS has the potential to improve numerous issues identified with over-choice. Deep learning's influence is also widespread, with new evidence of its efficacy in information retrieval and RS research. In RS, the field of deep learning is emerging. this article presents a thorough evaluation of recent research findings on traditional RS and deep learning-based recommender systems (DLRS). this review covers articles published between 2018 and 2022 in four major research databases: Science Direct, IEEE Explore, Springer, and Wiley. First, we provide a complete overview, comparison, and summary of traditional RS. We then systematically analyze DLRS challenges and related solution approaches. Finally, under the discussion of open issues at DLRS, we highlight possible research directions in this area. In this review, we report quantitative data from previous studies to highlight comparisons based on metrics such as F1-score accuracy, recall, accuracy, and error functions. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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A Genome-Wide Prediction Method of Biological Multiple Traits Using Feedback Stacking Deep Networks 11

A Genome-Wide Prediction Method of Biological Multiple Trait...

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11th international conference on Bioinformatics and Computational Biology (ICBCB)

作者： Zhu, Wenqi Cao, Junzhe Dalian Univ Technol Sch Control Sci & Engn Key Lab Intelligent Control & Optimizat Ind Equip Minist Educ Dalian Peoples R China

ISBN: (纸本)9798350397871

Genome-wide prediction methods establish a genetic statistical model using molecular marker genotypes distributed throughout the whole genome and biological trait data to predict phenotype values, in order to providing a guide for individual selection breeding. Most of the existing genome-wide selection methods focus on the prediction of a single trait, which can only examine each trait one by one when predicting multiple traits that would translate into neglect of the correlation among traits. In this paper, we proposed a joint prediction method for multiple biological traits based on feedback stacking deep networks, which stack orderly the output of the model to the non-shared layer so as to introduce the correlation among traits into the learning network. the method selected the single nucleotide polymorphism (SNP) loci obtained from whole genome sequencing as molecular markers, extracted the genotype of each SNP allele to constitute sample features, and adopted a two-stage feature selection method to pick up the SNP loci highly correlated with target, and then constructed a deep network model with feedback stacking for joint prediction of multiple traits. the proposed approach was compared with other models by using oyster and corn datasets, and the results showed that the method on this work could achieve better prediction effect than the others.

关键词： Genome-wide prediction multiple traits feedback stacking deep networks feature selection

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Generating Custom Learned Cost Model for Query Optimizer of DBMS 11th

Generating Custom Learned Cost Model for Query Optimizer of ...

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11th international conference on Model-Driven engineering and Software Development (MODELSWARD)

作者： Ouared, Abdelkader Amrani, Moussa Schobbens, Pierre-Yves Univ Tiaret Dept Comp Sci BP 78 Zaaroura Tiaret 14000 Algeria Univ Namur Fac Comp Sci NaDI Rue Grandgagnage 21 B-5000 Namur Belgium

ISBN: (纸本)9783031663383;9783031663390

Designers and developers of database management systems (DBMS) build increasingly specialized efficient query optimizer in order to deliver improved performance. this query optimizer uses a database Cost Model (CM) that is embedded inside these DBMSs. Currently, database CMs have to be manually calibrated for each database technology evolution and new requirements which is a costly and error prone process. Moreover, writing CM programs on DBMS distribution code like PostgreSQL and MySQL for specific platforms is hard since this requires the code to be tuned at a low level with architecture of the query optimizer that are fundamentally based on general-purpose programming languages. In this paper, we propose a framework to design and calibrate automatically the cost model of the query optimizer automatically. Our framework provides a code generation that allow user applications to fully generate the cost of database operation of a target DBMS by changing the model specification (e.g. the data primitives of the used data layout, data indexing, and the chosen algorithm of database operations). In addition, to CM code specialization for a specific hardware platform requires a machine learning component that is used as an external tool to automatically calibrate the CM under design. A proof-of-concept prototype is implemented by considering the CM that exists in PostgreSQL optimizer.

关键词： Learned database cost model database management system Meta-learning Generic interface Code generator

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RESNET-50-Based Feature Extraction and Classification Model for Alzheimer’s Disease Detection 11th

RESNET-50-Based Feature Extraction and Classification Model ...

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11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Rani, K. Emily Esther Baulkani, S. Department of Computer Science and Engineering Jayaraj Annapackiam CSI College of Engineering Tamil Nadu Nazareth India Department of Electronics and Communication Engineering Government College of Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9789819717231

Alzheimer disease (AD) is a brain disease that affects any senior people of the country. the burden of care givers can be reduced by detecting the disease at early stage itself. In neuro imaging, deep learning creates new intuitions in finding brain cell variations for detecting various brain diseases such as schizophrenia and Alzheimer’s disease. In this paper, Resnet-50 based deep learning model is constructed for feature extraction and classification to diagnose AD. To do this, we have collected resting state functional magnetic resonance image (rsFMRI) dataset from ADNI 3 database. Initially, the input rsFMRI images are preprocessed and augmented. then, the dataset is divided into training set and testing set. After that, the RESNET-50-based deep learning feature extraction and classification model is constructed to extract features from the training set and the pre trained model is used to classify AD patients from the test dataset. the experimental results show that this model diagnoses different AD stages with the classification accuracy of 95%, and it also outperforms some recent methods for diagnosing AD. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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