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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals data

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data intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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Pricing and Charging Scheduling for Cooperative Electric Vehicle Charging Stations via Deep Reinforcement Learning

Pricing and Charging Scheduling for Cooperative Electric Veh...

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2022 IEEE international conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2022

作者： Liu, Jie Wang, Shuoyao Tang, Xiaoying Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Shenzhen University Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China Guangdong Provincial Key Laboratory of Future Networks of Intelligence China

ISBN: (数字)9781665432542

ISBN: (纸本)9781665432542

The rapid adoption of electric vehicles (EVs) stimulates the proliferation of charging stations (CSs), motivating the cooperative management of growing CSs. However, cooperative CS management still remains an open problem, due to the uncertain user behavior and heterogeneous service capabilities. To capture the CS dynamics caused by uncertain user behavior, we propose a deep reinforcement learning (DRL)-based cooperative method for multiple CSs, towards maximizing the total profit. The proposed method determines pricing and charging scheduling decisions for CSs, considering stochastic CSs selection and its impact on CSs energy supply. In order to reduce the computational burden of dimensions caused by the time-varying decisions, we design a discretization strategy for action space, based on the current market rule of tiered pricing and CS types. The simulations using real data demonstrate that our proposed method can obtain higher profit than the independent operation and benchmark cooperative algorithms such as Q-learning. © 2022 IEEE.

关键词： Reinforcement learning

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Clustering Stock Prices of Industrial and Consumer Sector Companies in Indonesia Using Fuzzy C-Means and Fuzzy C-Medoids Involving ACF and PACF 7th

Clustering Stock Prices of Industrial and Consumer Sector Co...

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7th international conference on Soft computing in data Science (SCDS)

作者： Muda, Muhammad Adlansyah Prastyo, Dedy Dwi Akbar, Muhammad Sjahid Inst Teknol Sepuluh Nopember Fac Sci & Data Analyt Dept Stat Surabaya Indonesia

ISBN: (纸本)9789819904044;9789819904051

Fundamental and technical analysis that investors generally use to select the best stocks cannot provide information regarding the similarity of stock price characteristics of companies in one sector. Even though companies are in the same sector, each company has a different ability to earn profits and overcome financial difficulties. So, clustering is done to find the stock prices of companies with the same characteristics in one sector. This research uses data on industrial and consumer sector companies' stock prices because the industrial and consumer sectors are one of the largest sectors in Indonesia. The variables used in this research are open, close, and HML (High Minus Low) stock prices. Several clustering methods that can be used to cluster time series data are Fuzzy C-Means and Fuzzy C-Medoids. In addition, this research also uses several approaches with ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function), which can handle data with high dimensions and allow the comparison of time series data with different lengths. Based on the highest FS (Fuzzy Silhouette) value, the empirical results show that the two best methods for clustering open, close, and HML stock prices are Fuzzy C-Means and Fuzzy C-Medoids. The clustering results using Fuzzy C-Means are the same for open stock prices and close stock prices data. Meanwhile, there are different clustering results for HML stock price data.

关键词： ACF Fuzzy C-Means Fuzzy C-Medoids PACF Stock prices

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Machine Learning-Based Crack Detection Methods in Ancient Buildings 24

Machine Learning-Based Crack Detection Methods in Ancient Bu...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area international conference on Digital Economy and Artificial intelligence, DEAI 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P. Yang, Aihua Liu, Bin He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China College of Software Engineering Xiamen University of Technology Xiamen Fujian361024 China

ISBN: (纸本)9798400717147

This research paper introduces an innovative machine learning-based methodology for detecting cracks in ancient buildings, a vital aspect of architectural heritage conservation. Recognizing the limitations of conventional techniques in addressing the complex nature of historical structures, this study explores the integration of deep learning algorithms with enhanced image processing methods tailored for aged and varied materials typical of ancient architecture. The paper includes a comprehensive process involving meticulous data collection from diverse ancient structures, employing specialized preprocessing methods to handle the unique challenges of old surfaces. The development of a custom machine learning model, designed to adapt to the intricacies of historical construction, is thoroughly detailed. Experimental results demonstrate the model's superior performance in accurately identifying structural cracks compared to traditional methods. This research not only significantly contributes to the preservation of historical buildings but also exemplifies the versatile application of machine learning in the field of architectural conservation. The findings hold promise for revolutionizing the approach to maintaining and restoring our architectural heritage, ensuring longevity and integrity. © 2024 ACM.

关键词： Image enhancement

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Exploration and Practice of Course Homework Metaverse Based on Extended Reality Under Edge computing 1

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3rd EAI international conference on Edge computing and IoT, EAI ICECI 2022

作者： Fu, Jinrong Liu, Yiwen Yan, Haobo Gao, Yanxia Xiao, Jianhua School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

The metaverse originated from science fiction at first, but it gradually came into reality with the continuous power of technology. Aiming at the problems of course work in Chinese universities, such as its function is weakened or suppressed, its form is too abstract, and it lacks elaborate design, this paper proposes a design method of course work meta-universe based on extended reality under edge computing. XR technology is used to realize the interactive immersion of the coursework meta-universe, in order to improve students’ learning enthusiasm and ability to solve abstract problems. Based on the international perspective, this paper systematically combs and analyzes the integration architecture, application scenarios and future trends of the meta-universe education application based on Edge computing (MEC). Four key processes are described: teacher-student interaction, data collection, data feedback and comprehensive evaluation. In order to promote the application of the metaverse in the field of education to provide mirror. The validity of the method is illustrated by the data. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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THE GENERAL APPROACH OF SENSOR NETWORK 1

THE GENERAL APPROACH OF SENSOR NETWORK

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IMEKO TC6 1st international conference on Metrology and Digital Transformation, M4Dconf 2022

作者： Havlíček, Marek Koval, Martin Tesař, Jiří Czech Metrology Institute Brno Czech Republic

ISBN: (纸本)9781713862239

The Sensor Network (SN) [1] has become already a well-established concept widely used in many areas of industry such as automotive, mechanical engineering, food industry and energy supply, health care, etc. Rapid development of new technologies has brought novel approaches and implementation of new technologies such as Artificial intelligence (AI), Cloud computing, big data storage, management, etc. Current trends focus predominantly on monitoring and optimization of processes in order to increase effectiveness and reliability which can be effectively reached only with the help of sensor networks. This article focuses on the general sensor network structure, chances and pitfalls which are related to their use. © 2022 IMEKO TC6 international conference on Metrology and Digital Transformation. All rights reserved.

关键词： Artificial intelligence

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Efficient Mining of Regional Semantic Patterns from Semantic Trajectories on Cloud computing 9

Efficient Mining of Regional Semantic Patterns from Semantic...

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9th international conference on Advanced Cloud and big data, CBD 2021

作者： Liu, Yushi Han, Jingjing Zhao, Bin Nanjing Normal University School of Computer and Electronic Information School of Artificial Intelligence Nanjing China Jiangsu Open University Quality Assurance Office Nanjing China

ISBN: (纸本)9781665407458

Semantic trajectory pattern mining is becoming more and more important with the efficient semantic enrichment process methods, which is the process of semantic annotation for spatiotemporal trajectories. Existing works on semantic trajectory pattern mining focus on sequential patterns that occur frequently globally in the semantic trajectory data, ignoring the ones that are locally significant within a region, which is called regional semantic pattern. As a consequence, the existing regional semantic pattern mining algorithm RegMiner is time consuming. Based on this motivation, we propose an improved efficient regional semantic pattern mining algorithm called MRSP, which consists of three parts: candidate semantic category sequence discovery, regional semantic pattern recognition, and parallelization. The innovations of MRSP lie in that we use an improved neighborhood pruning algorithm (NPSP) and an improved neighborhood query algorithm (NSSI) in candidate semantic category sequence discovery mentioned above, and adopt a parallelization algorithm to do calculation based on Hadoop platform. Finally, we evaluate our approach on real dataset in Shanghai. Compared with RegMiner, MRSP has the same effect in mining regional semantic patterns, and the efficiency is greatly improved. © 2022 IEEE.

关键词： Pattern recognition

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Detecting Online Outlier for data Streams using Recursive Residual 7

Detecting Online Outlier for Data Streams using Recursive Re...

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7th international conference on Informatics and computing, ICIC 2022

作者： Dani, Yasi Gunawan, Agus Yodi Indratno, Sapto Wahyu Institut Teknologi Bandung Industrial and Financial Mathematics Research Group Faculty of Mathematics and Natural Sciences Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processingand Big Data Analytics Institut Teknologi Bandung Faculty of Mathematics and Natural Sciences Department Statistics Research Division Bandung Indonesia

ISBN: (纸本)9798350345711

Outlier analysis is a statistical procedure that involves the identification of anomalous observations. Outlier detection is important in many fields and involved in numerous applications since if the outliers are not detected then it can lead us to a wrong decision. There are a wide variety of techniques that can be used to identify outliers in datasets. Most of the existing methods are based on static data, while in case of data stream these methods are computational inefficiency and require big memory storage. This is due to the data stream being a dataset that is generated in a real-time data stream and requires real-time data analysis. The recursive least squares approach is mainly used in many regression analysis and the outlier detection process is referred to as supervised outlier detection. In this paper, we propose an online outlier detection algorithm using recursive residuals via recursive least squares method. The recursive residuals are sequentially calculated on the distance of the observation and the prediction values. To estimate online parameters in this research, we apply a recursive least squares method to predict an iterative model. In recursive least squares method a single new data point is analyzed each algorithm iteration to improve the estimation of our model parameters. Then, our simulation is performed on historical data for Tesla stock for a given period. Results that our online algorithm can demonstrate its ability to maintain high accuracy and find better outliers with low computational cost for anomaly detection. © 2022 IEEE.

关键词： Anomaly detection

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Implicit Maximum Likelihood Clustering 18th

Implicit Maximum Likelihood Clustering

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18th international conference on Artificial intelligence Applications and Innovations (AIAI)

作者： Vardakas, Georgios Likas, Aristidis Univ Ioannina Dept Comp Sci & Engn GR-45110 Ioannina Greece

ISBN: (纸本)9783031083372;9783031083365

Clustering is a popular unsupervised machine learning and data mining problem defined as a process of assigning objects to groups so that objects in the same group are similar to each other and differ from objects in other groups. In this paper, a data clustering method is proposed that is based on unsupervised training of a generative neural network using the technique of Implicit Maximum Likelihood Estimation (IMLE). Given a dataset, IMLE is an unsupervised method that trains a neural network that takes random noise as input and produces synthetic data samples whose distribution is close to the original data. We have developed an appropriate adaptation of the IMLE generative approach that also achieves clustering of the dataset. The proposed clustering method has been evaluated on several popular datasets of various types and complexity yielding promising results.

关键词： Clustering Neural networks Implicit likelihood maximization Synthetic data generation

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Electric vehicle charging load forecasting based on ownership 21

Electric vehicle charging load forecasting based on ownershi...

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21st IEEE international conference on Ubiquitous computing and Communications / 5th IEEE international conference on data Science and Computational intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Deng, Li Lei, Guoping Dai, Nina Li, Shenghao Chongqing Three Gorges Univ Sch Elect & Informat Engn Chongqing 404130 Peoples R China Chongqing Vocat Inst Engn Sch Big Data & Internet Things Chongqing 402260 Peoples R China

ISBN: (纸本)9781665477260

Under the guidance of national policies, the number of electric vehicles has shown a sudden growth. Therefore, to achieve friendly interaction between electric vehicles and the power grid at the information technology level, this paper introduces an impact factor in the Gray Model (GM (1,1)) to obtain the change of electric vehicle ownership under the guidance of policy. Through experimental comparison, it is found that the optimized model has improved its accuracy compared with the previous model. The charging characteristics of electric taxis, electric buses, and electric private cars are analyzed, then the Monte Carlo method is applied to quantize the charging load requirements of electric vehicles at different penetration rates. In addition, the results show that under high permeability, the load pressure on the grid will increase by six percent on the peak.

关键词： electric vehicle grey prediction model ownership Monte Carlo

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