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2nd Asian Conference on Frontiers of Power and Energy (ACFPE)

作者： Liu Bin Zhang Yunxiao Huo Yu Yi Fengchao Yuan Xinggang Liu Gang Zhao Zefeng Wang Xueyan Zhao Feng Xu Yongtao Inner Mongolia Autonomous Reg Water Resources Dev Hohhot City Inner Mongolia Peoples R China Inner Mongolia Kedian Data Serv Co Ltd Hohhot Inner Mongolia Peoples R China Water Resources Dept Inner Mongolia Autonomous Re Hohhot City Inner Mongolia Peoples R China

ISBN: (数字)9798350303896

ISBN: (纸本)9798350303896

In order to comprehensively, accurately and timely grasp the water consumption of agricultural irrigation in the West Liaohe River Basin, this study constructs a data receiving system of "Conversion from electricity consumption into water consumption"(CE2W) in the West Liaohe River Basin, collects large data of electricity of typical wells, and calibrates the rate coefficient of "CE2W". In order to improve the accuracy of the water conversion from electricity to water in the West Liaohe River Basin, and to achieve the goal of "CE2W".[Method]This study is based on the case of the construction project of the "CE2W" receiving software system in the West Liaohe River basin. Based on big data and Internet of Things technology, the technical route and system function construction of real-time monitoring data collection and reception of 2,172 typical wells from six typical well data receiving equipment manufacturers are studied.[Results] In view of the current situation and problems of the "CE2W" and typical well monitoring equipment data reception in the West Liaoning River Basin, the solution of the data reception system is proposed, and the overall architecture design and system function construction are described in detail. Finally, we describe the application of the data reception system in the "water for electricityCE2W" and show the results. [Conclusion] The data reception system is an inaccessible part of the goal of "CE2W", and the construction of the data reception system can continuously improve the accuracy of the water conversion system of "CE2W" and save the investment of tens of billions of dollars for the installation of metering equipment in 700,000 wells. Electricity big data applications to expand to the water industry.

关键词： CE2W big data technology data receiving system AI BP neural network

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A plant disease image using convolutional recurrent neural network procedure intended for big data plant classification

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 4173-4186页

作者： Gopinath, S. Sakthivel, K. Lalitha, S. Gnanamani Coll Technol Dept Comp Sci & Engn Namakkal Tamil Nadu India KS Rangasamy Coll Technol Dept Comp Sci & Engn Tiruchengode Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India

The recent advancement of big data technology causes the data from agriculture domain to enter into the big data. They are not conventional techniques in existence to process such a large volume of data. The processing of large datasets involves parallel computation and analysis model. Hence, it is necessary to use big data analytics framework to process a large image datasets. In this paper, an automated big data framework is presented to classify the plant disease condition. This framework consists of a series operations that leads into a final step. When the classification is carried out using novel image classifier. The image classifier is designed using a Convolutional Recurrent Neural Network Classifier (CRNN) algorithm. The classifier is designed in such a way that it provides classification between a normal leaf and an abnormal leaf. The classification of plant images over large datasets that includes banana plant, pepper, potato, and tomato plant. Which is compared with other existing big data plant classification techniques like convolutional neural network, recurrent neural network, and deep neural network, artificial neural network with forward and backward propagation. The result shows that the proposed method obtains improved detection and classification of diseased plants compared to other the convolutional neural network (94.14%), recurrent neural network (94.07%), deep neural network (94%), artificial neural network with forward (93.96%), and backward propagation method (93.66%).

关键词： big data technology convolutional recurrent neural network weighted naive bayesian network classifier algorithm plant disease classification

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data-Driven Revolution: Advancing Scientific and Technological Innovation in Chinese A-Share Listed Companies

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JOURNAL OF THE KNOWLEDGE ECONOMY 2023年第3期15卷 9975-10002页

作者： Wei, Xing Northeastern Univ Quantitat Finance Boston MA 02115 USA

The transformative role of big data technology in fostering scientific and technological innovation, leading to sustainable development and economic growth, has become increasingly crucial in modern business environments. This study utilizes text analysis of annual financial reports from Chinese A-share listed companies to assess the frequency of keywords related to big data application technology. Through panel data regression, the research investigates the significant impact of big data technology on scientific and technological innovation across diverse industries while controlling for relevant financial and corporate governance variables. The findings reveal a positive correlation between big data application technology and scientific and technological innovation, even after accounting for control factors. Moreover, private enterprises emerge as influential contributors to scientific and technological advancement. The study highlights the theoretical implications of integrating big data technology with the real economy to optimize resources effectively, and the policy implications call for targeted strategies to nurture innovation in established and growing enterprises. As future research prospects, this study lays the groundwork for exploring additional dimensions of big data technology's impact on innovation and its implications for sustainable development in the ever-evolving business landscape.

关键词： big data technology Technological innovation Sustainable development Economic growth

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Prospects for the database development in electrical engineering materials

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第4-5期24卷 2199-2211页

作者： Sheng, Peng Li, Shengyi Xu, Li Wang, Bo Bai, Huitao Li, Hui Xue, Qing State Grid Smart Grid Res Inst Co Ltd Beijing Peoples R China

With the proposal and development of the Material Genome Engineering program, artificial intelligence has played a significant role in accelerating the research and development of new materials. In the field of electrical engineering materials, high-throughput experimental and computational methods provide a huge amount of data. It also poses new challenges to how to manage material data scientifically and efficiently. database technology has become a hot topic for material scientists and engineers. This paper makes a comprehensive overview of the development, demand analysis and application of database technology in the electrical engineering materials, and discusses the existing problems and the future development trend of the database. Compared with many materials, such as energy materials, catalytic materials, biomedical materials, etc., the electrical material database still has a long way to go in the process of database platform construction, management and operation, and practical application. However, driven by governmental support and market demand, the construction of electrical material database will gradually improve and play an important role in the data-driven new materials researches.

关键词： Material genome engineering electrical engineering materials database big data technology machine learning

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Innovative Risk Early Warning Model under data Mining Approach in Risk Assessment of Internet Credit Finance

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COMPUTATIONAL ECONOMICS 2022年第4期59卷 1443-1464页

作者： Lin, Min Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China

The financial risks of commercial banks are classified and evaluated through the Internet of Things (IoT) technology and big data technology to reduce the financial risk loss of commercial banks in the context of Internet finance. Firstly, based on the analysis of financial risks in the context of IoT technology, an IoT tail loss mathematical model of financial operation risk is constructed to classify the operation risks of commercial banks. Secondly, the BP neural network algorithm is applied to determine the number of nodes, activation function, learning rate, and other parameters of each BP neural network layer. Also, many data samples are used to build an early warning model of Internet credit risk. The constructed model is trained and tested. Finally, the genetic algorithm (GA) is used to optimize the neural network to improve early warning accuracy. The results show that introducing Internet technology can reduce the risk loss of commercial banks. In addition, based on 450 data samples of 90 companies in 5 years and the risk interval divided by the "3 sigma" rule, the Internet credit risk level was initially determined. Then, the neural network is trained and tested. The prediction accuracy of the neural network reaches 85%. In order to avoid the defects of BP neural network falling into local extreme values, GA is used to optimize the neural network. The warning is more accurate and the error is smaller, and the accuracy rate can reach 97%. Therefore, the use of BP neural network for early warning and assessment of Internet credit risk has good accuracy and computing efficiency, which expands the application of BP neural network in the field of Internet finance, and provides a new development direction for the early warning and assessment of Internet credit risk.

关键词： big data technology BP neural network Internet credit Risk early warning model

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Intelligent Matching System of Clauses in International Investment Arbitration Cases Based on big data Statistical Model

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第1期8卷 1909-1918页

作者： Li, Liao Shanghai Lixin Univ Accounting & Finance Sch Law Shanghai 201620 Peoples R China

The international investment arbitration system between investors and host countries originated from the international commercial arbitration system. International investment disputes are usually disputes caused by the host country's policies to protect public interests and the dissatisfaction of private investors caused by regulations. There are few methods for intelligently recommending judgment documents based on factual texts in international investment arbitration cases. Because of this, this paper proposes a recommendation algorithm for international investment arbitration adjudication documents based on multimodal feature fusion. This paper teaches the structural information of paragraph tags and the relationship between paragraph contexts and incomplete judgment documents. The algorithm achieves a good structuring effect of international investment arbitration documents.

关键词： big data technology Statistical model Text matching International investment arbitration Intelligent matching

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HMA-ID mechanism: a hybrid mayfly optimisation based apriori approach for intrusion detection in big data application

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TELECOMMUNICATION SYSTEMS 2022年第1期80卷 77-89页

作者： Dasgupta, Sarbani Saha, Banani Techno Int Newtown Dept MCA Kolkata 700156 W Bengal India Univ Calcutta Dept Comp Sci Dept Kolkata 700073 W Bengal India

Rapid growth of the internet facilitates various facilities in everyday lifestyle, but intrusion becomes a significant threat in internet usage. Thus, the detection of intrusion is essential for smooth and secure communication in a network. In literature, many techniques have been proposed for the detection of intrusion. But those techniques either complex or fails to provide better performance in a big data application. Therefore, this paper proposed a novel Hybrid Mayfly Apriori-Intrusion Detection mechanism for effective intrusion detection in big data applications. In the proposed mechanism, Mayfly optimization based Apriori is used to detect the intrusion. Unlike conventional classification based intrusion detection, in the proposed mechanism, the network data processed to form an apriori rule based on frequent itemset. The infrequent itemset or transactions are marked as an intrusion. Comparison with established algorithms such as Artificial Neural Network, Random Forest, K-Nearest Neighbour and Support Vector Machine analyses the efficacy of the suggested mechanism. Ultimately, the proposed mechanism has shown its effectiveness by providing better results as 97% accuracy, 99% precision, and 97% recall. Thus, this mechanism is more suitable for intrusion detection in big data.

关键词： Intrusion detection Hybrid mayfly apriori big data technology Mayfly optimization Apriori rule

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E-Commerce Product Classification in the Era of big data: Leveraging Advanced Algorithms for Precision 11

E-Commerce Product Classification in the Era of Big Data: Le...

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11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Ning, Xiaohong Shao, Tsz Hon Lin, Xiaolan Maalla, Allam Maalla, Maher School of Information Technology and Engineering Guangzhou College of Commerce Guangdong Guangzhou511363 China School of Engineering Guangzhou College of Technology and Business Guangdong Guangzhou510850 China ITMO University Saint Petersburg197101 Russia

ISBN: (纸本)9798350333664

In the era of rapid informatization, the Internet has permeated every facet of daily life, with a profound impact on communication, work, and commerce. E-commerce platforms have emerged as pivotal players in shaping the daily routines of people, especially in China. This ubiquity of e-commerce platforms has brought forth the challenge of effectively categorizing retail product titles within these vast digital marketplaces. With the imperative for precise classification and the complexities arising from category imbalances, the quest to enhance the accuracy of retail product title classification has taken center stage. This article delves into this critical issue, particularly focusing on the classification of short texts in the context of e-commerce. We introduce an enhanced BERT model, incorporating refinements inspired by the fine-tuning method proposed herein. To evaluate the effectiveness of our approach, we conducted comprehensive experiments comparing the BERT algorithm with the traditional Naive Bayes algorithm, making necessary adjustments to parameters such as learning rate and batch size. Our findings demonstrate significant improvements in classifier precision and recall, underscoring the superior classification performance of the BERT model. © 2023 IEEE.

关键词： BERT big data technology Classification Deep Learning E-commerce Natural Language Processing

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Enhancing E-Commerce Retail Product Classification Based on big data Analysis and Natural Language Processing 11

Enhancing E-Commerce Retail Product Classification Based on ...

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11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Shao, Tsz Hon Maalla, Maher Lin, Xiaolan Ning, Xiaohong Maalla, Allam School of Engineering Guangzhou College of Technology and Business Guangdong Guangzhou510850 China ITMO University Saint Petersburg197101 Russia School of Information Technology and Engineering Guangzhou College of Commerce Guangdong Guangzhou511363 China

ISBN: (纸本)9798350333664

Currently, most E-commerce platforms employ conventional machine learning algorithms for product classification, particularly when dealing with Chinese product titles. This classification process primarily centers on techniques like word segmentation, stop word removal, and feature engineering, with limited exploration of advanced technologies in this domain. However, recent years have witnessed significant breakthroughs in natural language processing, particularly in classification tasks, addressing various challenges like text classification, named entity recognition, and summarization of key content. Extracting valuable insights from textual data and delivering value to relevant industries has garnered substantial interest from researchers and developers. To underscore the strengths of our algorithmic model, this study leverages the traditional machine learning approach of the naive Bayes algorithm, using experimental data as a foundation. Additionally, we harness the cutting-edge BERT model from the realm of natural language processing to serve as a classification model for handling concise Chinese product titles. © 2023 IEEE.

关键词： BERT big data technology Deep Learning E-commerce

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Research and application of a communication construction engineering subcontracting platform based on AI and big data

Research and application of a communication construction eng...

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2024 International Conference on Computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2024

作者： Xie, Yixiang Zheng, Dajiang Xiangsihu College of Guangxi Minzu University Guangxi Nanning 530225 China Nanning City Yuanxing Investment Group Co. Ltd. Guangxi Nanning 530022 China

ISBN: (纸本)9781510689275

The communication construction industry is facing higher technical standards, more complex construction environments, and stricter regulatory requirements. With the rapid development of technologies such as 5G, Internet of Things, and cloud computing. Professional engineering subcontracting is also a common construction method in the field of communication construction engineering. This article proposes a specialized subcontracting platform for communication construction engineering, based on AI and big data, and explores its research and application in detail. This platform integrates functions such as subcontractor and personnel management, professional project contracting, project acceptance, project progress monitoring, cost settlement management, and project acceptance management. It uses big data technology to deeply process and analyze engineering data, personnel data, and financial information, and intelligently recognizes documents through AI technology to support the operation and decision-making of the platform. The research results show that the platform can significantly improve the efficiency of engineering project management, reduce operating costs, and improve engineering quality. © 2025 SPIE.

关键词： big data technology Communication engineering Deep learning Professional engineering subcontracting

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