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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1353 条记录，以下是21-30 订阅

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Outlier-aware Cross-Market Product Recommendation

Outlier-aware Cross-Market Product Recommendation

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Kang, HyeoungGuk Lee, Donghoon Cho, Hyunsouk Ajou Univ Dept Cyber Secur Suwon South Korea Ajou Univ Dept Software & Comp Engn Suwon South Korea Ajou Univ Dept Artificial Intelligence Suwon South Korea

ISBN: (纸本)9781665475785

Cross Market Recommendation (CMR) is a method of recommending in a resource-scarce market by using model-agnostic meta-learning. Generally, more interactions give more clues to identify the user preferences, so CF performs better with outlier users (who have more item interactions) than normal users. However, constructing each adapt batch set (support set) and evaluation batch set (query set) for meta-learning in CMR causes the model to underfit in outlier users. We aim at this phenomenon and propose a new hybrid strategy to solve this problem. By simply combining MAML and CF to target general users and outliers, respectively. We also validate our method with the benchmark dataset and the proposed model shows better performance compared to the original model.

关键词： recommendation, outlier, meta learning

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A prognostic model for classification of COVID-19 severity based on clinical and laboratory testing data

A prognostic model for classification of COVID-19 severity b...

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Lee, Jae -Bin Kim, Sang Cheol Lee, Ju-Hee Jo, Hye-Yeong Korea Natl Inst Hlth Div Healthcare & Artificial intelligence Cheongju South Korea

ISBN: (纸本)9781665475785

This study aimed to build an machine learningbased model to predict the COVID-19 severity and reveal risk factors related to COVID-19 severity based on laboratory testing and clinical data for 420 participants, using tree-based models such as XGBoost, LightGBM, random forest. We calculated the Odds Ratios (OR) to investigate whether the top-ranked features were statistically significant for severity classification, turning out that high sensitivity C-reactive protein (hs-CRP) was the most important feature for determining of COVID-19 severity and XGBoost model showed the highest performance in classifying COVID-19 severity and healthy controls with F1score (0.84) and AUC (0.87). We expect that our results are of considerable significance for early screening for diagnosing COVID-19 severity, which, in turn, assist in further retrospective research for uncommon infectious diseases.

关键词： COVID-19 machine learning clinical data laboratory testing data prognostic model

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Lazy Node-Dropping Autoencoder

Lazy Node-Dropping Autoencoder

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Lee, Jaewon Kim, Myung Jun Shin, Hyunjung Ajou Univ Dept Artificial Intelligence Suwon South Korea INRIA Saclay Soda Palaiseau France Ajou Univ Dept Ind Engn Dept Artificial Intelligence Suwon South Korea

ISBN: (纸本)9781665475785

Autoencoders are widely used for dimensionality reduction nonlinearly. However, determining the number of nodes in the autoencoder embedding space is still a challenging task. The number of nodes in the bottleneck layer, which is an encoded representation, is estimated and determined by users. Therefore, to maintain embedding performance and reduce the complexity of the model, an indicator that automatically selects the number of bottleneck nodes is needed. This study proposes a method for automatically estimating the adequate number of nodes in the bottleneck layer while training the model. The basic idea of the proposed method is to eliminate lazy nodes which rarely affect the model performance based on the weight distribution of the bottleneck layer. Since the proposed method takes place in the learning process of the autoencoder, it has the advantage of accelerating the training speed. The proposed method showed better or similar performances in classification accuracy.

关键词： Neural Networks Autoencoder Dimensionality Reduction Dimension Estimation

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Automated Recognition and Classification of Cat Pain Through Deep Learning

Automated Recognition and Classification of Cat Pain Through...

引用

international conference on big data intelligence and computing, datacom 2022

作者： Yang, Yang Sinnott, Richard O. School of Computing and Information Systems The University of Melbourne MelbourneVIC3010 Australia

ISBN: (纸本)9789819922321

Unlike humans, pets are unable to vocalize the details of specific pain they may be experiencing. Rather, this can only be determined through physical clues, behaviours and/or potential noises that the animals might make. For some animals, this may be obvious but for others this is often far less easy to identify. One example of the latter is cats. A cat in pain may appear to be simply subdued. This paper explores different methods used to detect cat pain automatically based on an international scale for pain assessment and classification for cats. A targeted (labelled) data set collected from veterinary practices across Melbourne is utilized in combination with diverse deep learning techniques including You-Only-Look-Once (YOLO) [1], Single Shot Detection (SSD) [2] and inceptionV3 [3] to automatically classify the potential pain of cats. We achieve an overall accuracy of 93% for pain classification using an enhanced multi-model approach and combining different machine learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Animals

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The Australian Digital Observatory: Social Media Collection, Discovery and Analytics at Scale

The Australian Digital Observatory: Social Media Collection,...

引用

international conference on big data intelligence and computing, datacom 2022

作者： Sinnott, Richard O. Li, Qi Mohammad, Abdul Morandini, Luca School of Computing and Information Systems The University of Melbourne MelbourneVIC3010 Australia

ISBN: (纸本)9789819922321

The Australian Digital Observatory was funded by the Australian Research data Commons in 2021. The goal of the project was to establish the national social media data repository for Australia. This includes collection of social media data at scale, in the first instance from numerous platforms including Twitter, Reddit, FlickR, FourSquare and YouTube. This paper describes the technical architecture of the ADO platform and provides examples of the capabilities that are offered for data discovery, analysis and subsequent download of targeted social media data to support diverse research purposes, noting that the platform needs to respect the terms and conditions of the various social media platforms on data licensing and use, i.e., direct user access to the original raw data is not possible as this would violate the licensing arrangements of the various platforms. We present a case study in the utilization of the platform. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games

CompeteNet: Siamese Networks for Predicting Win-Loss Outcome...

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Mun, Kyeongjin Cha, Bonggeon Lee, Jongwuk Kim, Jihyeon Jo, Hyuncheol Sungkyunkwan Univ Dept Artificial Intelligence Suwon South Korea LG Uplus AI Technol Team Seoul South Korea

ISBN: (纸本)9781665475785

Sports game prediction using machine learning techniques has gained much attention in the sports industry. Notably, this task is vital for sports-related fields, such as sports marketing, sports betting, in-game strategy establishment, etc. To improve prediction accuracy, it is necessary to consider various game factors, such as past match results and player features. In this paper, we formulate win-lose outcome prediction in baseball games as a binary classification problem. We first collect various team and player features from 2012-2021 Korean Baseball Organization (KBO) data. Then, we propose a novel prediction model using Siamese neural networks, CompeteNet, to learn a competitive relationship between a pitcher and a batter player for two teams. Despite the simplicity, extensive experimental results show that CompeteNet outperforms existing machine learning models and achieves the best accuracy of 60.00%.

关键词： Sports Prediction Logistic Regression Baseball Analysis Sabermetrics Siamese Network

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Fast Prediction for Suspect Candidates from Criminal Networks

Fast Prediction for Suspect Candidates from Criminal Network...

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Jhee, Jong Ho Kim, Myung Jun Park, Myunggeon Yeon, Jeongheun Kwak, Yoonshin Shin, Hyunjung Ajou Univ Sch Med Ctr KIURI BioArtificial Intelligence Suwon South Korea INRIA Saclay Soda Palaiseau France Marketboro Crop Seongnam South Korea Ajou Univ Dept Artificial Intelligence Suwon South Korea Ajou Univ Dept Ind Engn Dept Artificial Intelligence Suwon South Korea

ISBN: (纸本)9781665475785

Machine learning approaches have been introduced to support criminal investigations in recent years. In criminal investigations, Criminal acts may be similar, and similar incidents may occur consecutively by the same offender or by the same criminal group. Among the various machine learning algorithms, network-based algorithms will be suitable to reflect such associations. In general, however, inference by network-based algorithms is slow when the size of data is large, so it is fatal in crime scenes that require urgency. And worse, the criminal network must be able to handle complex information entangled with case-to-case, person-to-person, and case-to-person connections. In this study, we propose a fast inference algorithm for a large-scale criminal network. The network we designed has a unique structure like a sandwich panel, where one side is a network of crime cases and the other side is a network of people such as victims, criminals, witnesses, etc., and the two networks are connected by relationships between the case and its corresponding people. The experimental results on benchmark data showed that the proposed algorithm has fast inference time and competitive performance compared to the existing approaches. After performance validation, the proposed method was applied to the actual crime data provided by the Korean National Police to predict the suspect candidates for several cases.

关键词： criminal data crime network suspect candidate prediction machine learning graph-based semi-supervised learning

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Analyzing Role of Artificial intelligence in Project Management and Investment Risk: A CiteSpace Insight 24

Analyzing Role of Artificial Intelligence in Project Managem...

引用

16th international conference on Machine Learning and computing (ICMLC)

作者： Lai, Shuang Zhang, Shaoqian Hassan, Abual Mushtaq, Ray Tahir Northwestern Polytech Univ Sch Publ Policy & Adm Xian Peoples R China Univ Kebangsaan Malaysia Dept Econ & Management Bangi Malaysia Gdansk Univ Technol Fac Mech Engn & Ship Technol Gdansk Poland Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China

ISBN: (纸本)9798400709234

The current research work explores the artificial intelligence and sustainability impact on investment risk and project management based on trend analysis from 2017 to 2022. Deakin University;the University of London or other key contributors and institutions that contribute to this field is identified by a rigorous analysis of one hundred nineteen articles from Web of Science. In it, a considerable part of the world is shown with meaningful contributions from China, Korea and the United States. This research presents the changing AI phenomenon in risk management for investments and especially notes that there is increasing connection with sustainability. The results of this work emphasize the role of AI in renovating traditional risk paradigms and suggest appropriate international cooperation. The study thus not only uncovers the current state of knowledge but also positions itself as a starting point for future research in AI-driven investment risk management, providing valuable perspectives to both science and practice.

关键词： Investment Risk Sustainable Management Artificial intelligence CiteSpace Sustainability Machine Learning data Science

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Edge Cloud Selection Strategy for Micro-service Chain Migration in Mobile Edge computing

Edge Cloud Selection Strategy for Micro-service Chain Migra...

引用

international conference on big data intelligence and computing, datacom 2022

作者： Xu, Lanqing Wang, Hongman Huang, Jie Wang, Hongyan State Key Laboratory of Networking and Switching Technology School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789819922321

Micro-service, splitting and combining services in a finer granularity, is an emerging service architecture. With the rapid development of 5G and 6G mobile communication network, the migration of micro-services in edge computing has gradually become a hot topic. However, the range of edge cloud is limited, and user movement will increase the service delay. We consider the characteristics of service interdependence and serial parallel relationship in the micro-service chain and put forward an edge cloud selection strategy for micro-service chain migration based on Gibbs Monte Carlo sampling, in order to minimize the total micro-service chain migration delay and the distributed computing resources, and to achieve seamless and realtime response to mobile users’ service request. Experimental results show that, compared with other algorithms, the proposed algorithm can guarantee better global performance and reduce the total delay of service migration effectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile edge computing

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Research on Artificial intelligence Product Design Method Based on Product Semantics

Research on Artificial Intelligence Product Design Method Ba...

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2022 international conference on Computer Graphics, Artificial intelligence, and data Processing, ICCAID 2022

作者： Zhu, Zonghua Jingchu University of Technology China

ISBN: (纸本)9781510663350

With the development of Internet of Things, cloud computing, big data and other information technologies, the design objects and design methods have changed greatly. In order to meet the development direction of the times, this paper studies and analyzes the product design method of artificial intelligence based on product semantics. Firstly, this paper analyzes the design method and theoretical basis of product semantics, and then makes an in-depth research and analysis on the formation of artificial intelligence technology, including detailed discussion and planning of behaviorism, connectionism and symbolism of AI. Finally, it expounds the method of using artificial intelligence technology in product semantics design. By making full use of network technology, big data and artificial intelligence technology, a user model which is more in line with the user's thinking is established, and the application and development of artificial intelligence in product semantics are promoted. © 2023 SPIE.

关键词： Product design

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