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Journal of Network Intelligence 2023年第3期8卷 839-855页

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Lin, Tao Kong, Lingping College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science VSB-Technical University of Ostrava Czech Republic

With the development of smart electricity technology and demand response, optimization of household electricity consumption behavior has become an important research element for energy saving in residential buildings. In the study of smart electricity consumption in households, the differences in users’ lifestyles and their preferences for the use of various appliances can have a great impact on the results. And many existing methods need to rely on users’ awareness, which does not meet the popular demand. In this paper, we propose a new method for residential load scheduling that takes into account the load characteristics of appliances and electricity consumption habits. By analyzing the household electricity consumption data set and mining the personalized needs and us-age preferences of this user for various appliances, we establish an optimization model for electricity consumption behavior that combines the minimization of electricity expenses and user comfort. Finally, an improved artificial bee colony algorithm is proposed for solving the optimization model and generating a personalized dispatching strategy combined with real-time electricity pricing (RTEP) tariff. The proposed improved artificial swarm algorithm is compared with other classical algorithms, including GA, PSO, ABC, and QABC, and the analysis of cases shows that the model can effectively reduce the electricity consumption cost and ensure the customer satisfaction, and the proposed improved ABC-based algorithm outperforms other algorithms in terms of cost and user comfort. © 2023, Journal of Network Intelligence.

关键词： Customer satisfaction

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Preliminary Study on data Governance in data Resource System 6

Preliminary Study on Data Governance in Data Resource System

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6th International Conference on Artificial Intelligence and big data, ICAIBD 2023

作者： Miao, Fang Yang, Wenhui Xie, Yan Fan, Wenjie Chengdu University The Big Data Research Institute Chengdu China Chengdu University of Technology College of Computer & Network Chengdu China Big Data Center of Sichuan Province Department of Gov-technology Chengdu China

ISBN: (纸本)9781665491259

The 'Overall Layout Plan for the Construction of Digital China' provides a scientific and feasible framework for the construction of digital systems of different scales. Among them, the foundation of the data resource system and the smooth circulation of data resources are the most basic, crucial, and core parts of the overall plan. The data resource system should include at least two parts: data resources and data governance. This paper proposes a method to construct a data governance layer and a digital governance support layer between the data layer and the business layer to promote the circulation of data resources and achieve isolation and coupling between the data layer and the business layer. Through data registration and encryption technology, the data can be known, managed, used, authenticated, authorized, protected, and benefit-secured, providing an effective technical means to solve the problems related to data control, sharing, ownership, security, and benefit protection in data governance. © 2023 IEEE.

关键词： Cryptography

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Service competition in two-tier healthcare markets with heterogeneous patients balking

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Journal of Management science and Engineering 2025年第1期10卷 18-36页

作者： Yuanbing Miao Xiaoqing Cai Yanfei Lan Ruiqing Zhao Yinlian Zeng College of Management and Economics Tianjin UniversityTianjin300072China School of Data Science The Chinese University of Hong KongShenzhen518172China Shenzhen Research Institute of Big Data Shenzhen518172China College of Urban Transportation and Logistics Shenzhen Technology UniversityShenzhen518118China

Recently,public and private providers within two-tier healthcare markets have engaged in a fierce competition for patients,which is significantly influenced by patients'multifaceted sensitivities to delays and payments,particularly when patients'potential balking is ***,existing literature studying the two-tier healthcare market often presupposes patients as homogeneous or one-dimensionally heterogeneous,and neglects their balking *** stark contrast,this paper delves into the combined effects of patients’delay-and payment-sensitive heterogeneities,alongside their joining or balking decisions,on the service competition dynamics within the two-tier healthcare *** a game-theoretic framework,we initially derive subgame equilibria,both inclusive and exclusive of patient balking,uncovering that patient balking can lead to a subgame perfect Nash equilibrium in the presence of two-dimensional patient ***,our findings further suggest that the non-profit public provider may,counterintuitively,curtail its service capacity despite budgetary adequacy,contradicting the conventional assumption that such providers would fully allocate funds towards patient ***,from a patient welfare standpoint,we find heightened payment heterogeneity discrimination proves detrimental to patients,whereas increased differentiation in delay heterogeneity benefits *** revelation underscores a pressing social issue inherent in the two-tier healthcare market model:It has the potential to exacerbate healthcare disparities rooted in economic inequalities within the population.

关键词： OR in health services Queuing theory Game theory Joining-or-balking behavior

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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Stock Price Analysis Using Sentiment Analysis of Twitter data

Stock Price Analysis Using Sentiment Analysis of Twitter Dat...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Jeba Sonia, J. Goenka, Akshat Jain, Ansh SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9798350375237

The intricate interplay between stock prices and the myriad of influencing factors delineates a fertile domain for exploration. Factors like economic conditions, corporate performance, and market sentiment emerge as pivotal determinants of stock valuation within this complex ecosystem. Leveraging sentiment analysis methodologies, our study delves into vast textual datasets, with a particular emphasis on deciphering public opinions voiced through social media channels like Twitter. Through meticulous scrutiny of these sentiments, our objective is to unveil their profound impact on stock price fluctuations across diverse companies. Engaging in a comparative analysis between sentiment trends and real-time stock price movements, our research aims to bolster the predictive efficacy of sentiment-driven stock price models. By honing our comprehension of how public sentiment resonates within market dynamics, we aspire to foster the evolution of more resilient and precise frameworks for stock price prognostication. This inquiry not only propels the domain of sentiment-driven stock price prediction forward but also carries significant ramifications for stakeholders navigating the intricate terrain of financial markets with heightened acumen and precision. © 2024 IEEE.

关键词： Sentiment analysis

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Design and Application of a Traffic Light System at Airport Level Crossing Based on Video Surveillance technology 5

Design and Application of a Traffic Light System at Airport ...

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5th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2023

作者： Miao, Jiahe Le, Ningning Zhang, Xinyue Li, Qian Shi, Hongfang Li, Yang China Academy of Civil Aviation Science & Technology Big Data Analysis and Application Center Beijing China

ISBN: (纸本)9798350310603

This paper presents a design scheme of traffic light system at airport level crossing based on video surveillance technology. The system uses cameras and edge computing to detect the aircraft about to pass through the intersection with signals transmitted through data radio stations, and controls the traffic lights to alert vehicle drivers, to reduce the risk of conflict or collision between vehicles and the aircraft. The overall framework of the system and the design scheme of each functional module are introduced in detail, and the practical application of the system at a domestic airport is shown in the end. The system has the characteristics of low cost, easy deployment, high reliability, low delay, and strong expansibility, which can provide help for the safety management of airport surface operation. © 2023 IEEE.

关键词： Object detection

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The discussion on data system in the era of big data

The discussion on data system in the era of big data

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2023 International Conference on Computer Applications technology, CCAT 2023

ISBN: (纸本)9798350301540

For a long time, people have been using information technology to improve the quality and efficiency of various business operations in enterprises, economy, government, society, etc., and have achieved remarkable benefits. This has also formed a mindset and habitual practices, which is business-centric, process-conduct, business-oriented and business-driven informative methods. With the development of the big data era and digital economy, the emergence of new technologies, the expectation of new demands, the generation of new ideas, and the exploration of new methods will break the outdated understanding and limitations of traditional methods in the past, and will open up a new pattern of information system construction and digital transformation that is centered on data, solidify the foundation of data resource system, and promote the circulation of data elements. This paper starts with the impact of data on the future of humanity, and analyzes and discusses the role, scope, composition, and implementation paths of new information system, namely data system, through comparing information and data. It proposes feasible and valuable solutions for the new digital transformation at various levels, the construction and capacity enhancement of data system supported by the "one-body-two-wings"data architecture. © 2023 IEEE.

关键词： Information systems

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Responsive Farming Calendar 15

Responsive Farming Calendar

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pawar, Harshit Pandey, Ritesh Reddy, Mudimala Vignaneswar Rai, Puneet Kumar Jasmine, R.Anita Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331300579

This project introduces a comprehensive agricultural decision support system for India, combining machine learning models and real-time data sources to predict the optimal crop selection for a given state, district, and *** meteorological data, local soil conditions, and historical crop performance, the system provides precise recommendations for farmers, enhancing crop yield and resource *** addition, it offers real-time news feeds relevant to the agricultural sector and current meteorological weather updates for farmers, empowering them with the information needed to make informed decisions and adapt to changing *** innovative solution contributes to sustainable farming practices and economic viability while addressing the dynamic challenges of Indian agriculture. © Grenze Scientific Society, 2024.

关键词： Livestock

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises’ demand for dynamic multidimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Identification of Starch Types by Using Terahertz Time-Domain Spectroscopy Combined with SG-KNN 10

Identification of Starch Types by Using Terahertz Time-Domai...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Heng Wei, Tao Jiang, Yuying Guo, Chunyan Zhou, Wenhui Henan University of Technology College of Information Science and Engineering Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China

ISBN: (纸本)9798331520861

The terahertz (THz) spectra of four different types of starch were investigated by terahertz time domain spectroscopy (THz-TDS). Subsequently, standard normal variate (SNV) and savitzky-golay (SG) were used to remove the interference of spectral noise, and then a K-Nearest Neighbor (KNN) method was applied to identify the four types of starch. The results showed that the samples were identified with average accuracy, Kappa, and F1 scores for the test set were 0.9375,0.9158, and 0.9383, respectively. This method was also contrasted with logistic regression (LR). The comparisons demonstrated that SG-KNN surpassed the other methods and indicated that the integration of THz technology with SG-KNN was an effective approach for identifying starch types. © 2024 IEEE.

关键词： Nearest neighbor search

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