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

共 1364 条记录，以下是911-920 订阅

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Study on the Preparation of C4 Alkenes by Ethanol Coupling Based on Bootstrap Method 12th

Study on the Preparation of C4 Alkenes by Ethanol Coupling B...

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12th international conference on Frontier computing, FC 2022

作者： Yan, Zhendong Yan, Liqiang Ying, Jiajie Guo, Jiang Yu, Hangfeng School of Artificial Intelligence and Big Data Chongqing Metropolitan College of Science and Technology Chongqing Yongchuan China School of Construction Management Chongqing Metropolitan College of Science and Technology Chongqing Yongchuan China School of Architecture and Civil Engineering Chongqing Metropolitan College of Science and Technology Chongqing Yongchuan China

ISBN: (纸本)9789819914272

The manufacture of C4 olefins via ethanol coupling was investigated in this work [1]. The Bootstrap method of statistics was used to obtain the accurate relationship between the ethanol conversion and C4 olefins selectivity and temperature and time. First, analyze the relevant data [8], where the amount of data is too small and discrete individuals. In order to better fit ethanol conversion and C4 olefins selectivity at linear temperatures, Bootstrap method was used for statistics and self-iterative method system was used to make the fitting data closer to the real data. A visual analysis of the fitted large data set was performed to obtain the relationship between the ethanol conversion rate and temperature and the relationship between the selectivity of C4 olefins and temperature in each catalyst type. And verify the results to check the goodness of fit of the equation. When T = 350℃, the change of C4 olefins selectivity within min∈ [ 20, 273 ] was investigated [3]. By resampling, the resulting discrete data approximates the linear variation in the response. A large number of data are fitted into a continuous curve, and the relationship between C4 olefins selectivity and time can be known after analysis [4]. That is, the conversion rate of ethanol decreased significantly with time, from 43.5% in 20 min to 29.9% in 273 min. However, the selectivity of ethylene, C4 olefins, methyl benzaldehyde and methylbenzyl alcohol hardly changed over time [12]. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data visualization

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Joint Optimization of Multi-vector Representation with Product Quantization 1

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11th CCF international conference on Natural Language Processing and Chinese computing (NLPCC)

作者： Fang, Yan Zhan, Jingtao Liu, Yiqun Mao, Jiaxin Zhang, Min Ma, Shaoping Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Inst Artificial Intelligence Dept Comp Sci & Technol Beijing 100084 Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Method Beijing 100872 Peoples R China

ISBN: (数字)9783031171208

ISBN: (纸本)9783031171208;9783031171192

Dense retrieval models represent queries and documents with one or multiple fixed-width vectors and retrieve relevant documents via nearest neighbor search. Recently these models have shown improvement in retrieval performance and have drawn increasing attention from the IR community. Among a variety of dense retrieval models, the models that employ multiple vectors to represent texts achieve the state-of-the-art ranking performance. However, the multi-vector representation schema imposes tremendous storage overhead compared with single-vector representation, which may hinder its application in practical scenarios. We therefore intend to apply vector compression methods such as Product Quantization (PQ) to reduce the storage cost and improve retrieval efficiency. However, the gap between the original embeddings and the quantized vectors may degenerate retrieval performance. Recently, improved dense retrieval models such as JPQ have been proposed to reduce storage space while maintaining ranking effectiveness by jointly training the encoder and PQ index. They have achieved promising improvement in the single-vector dense retrieval scenario. We therefore try to introduce this joint optimization framework to tackle the storage overhead of the multi-vector models. The key idea is to Jointly optimize Multi-vector representations with Product Quantization (JMPQ). JMPQ prevents effectiveness degeneration by leveraging a joint optimization framework for the query encoding and index compressing processes. We evaluate the performance of JMPQ on publicly available ad-hoc retrieval benchmarks. Extensive experimental results show that JMPQ substantially reduces the memory footprint while achieving ranking effectiveness on par with or even better than its uncompressed counterpart.

关键词： Dense retrieval Index compression Neural ranking

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Impact of Disruptive Technologies on Customer Experience Management In ASEAN: A Review

Impact of Disruptive Technologies on Customer Experience Man...

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2022 IEEE international conference on computing, ICOCO 2022

作者： Adke, Vyankatesh Bakhshi, Priti Askari, Muniza Sydney Australia

ISBN: (纸本)9781665489966

The financial services sector in the Association of South East Asian Nations (ASEAN) region has seen significant growth, driven by digitalization and the rise of fintech firms. Financial services accounted for about 8% of the overall Gross Domestic Product (GDP) at around 3 Trillion in 2021 [1]. While the GDP contracted slightly due to the COVID-19 pandemic, the overall outlook over the next five years remains *** further boost this growth, and foster innovation, regulators across ASEAN are establishing foundations for open finance, as is clear from policies in Singapore [2], the Philippines [3], and Indonesia [4].The main objectives of the open finance framework are to offer integrated financial services by making customer experiences that are fully digital, frictionless, empathetic, and anticipatory to customer *** today are more digitally empowered, expect personalized service, and often maintain relationships with multiple retail banks. As such, Customer Experience (CX) management is a top priority for retail banks to ensure overall brand recall, customer loyalty, and *** however also poses a new challenge to incumbent banks, as they need to embark on complex digital transformation journeys to stay relevant and competitive with due consideration for costs and accrued *** this context, this study explores the impact of cloud, Artificial intelligence (AI), and digital channels, collectively referred to as disruptive technologies, on customer experience *** does so by critically examining existing literature on the evolution of digital technologies, their applications for customer engagement and the consequent impact on customer behaviours, and customer experience measures such as the Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS). Based on the review, the study identifies opportunities for future research in the form of research questions, which include factors like experience quality, behaviour traits

关键词： big data

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Research and Application of Drilling Engineering Monitoring and Remote Support Technology for CNPC Key Wells 1

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11th international Field Exploration and Development conference, IFEDC 2021

作者： Zhao, Tong Wu, Hua Xu, Sheng-jiang Ge, Yun-hua Mei, Yun-yi Zhang, Yan-long CNPC Engineering Technology R&D Company Limited Beijing China Institute of Engineering and Technology Xinjiang Oilfield Company Karamay China Unconventional Petroleum Research Institute China University of Petroleum Beijing China

ISBN: (数字)9789811921490

ISBN: (纸本)9789811921483

The global oil and gas industry is in a critical period of digital transformation. Digital technologies such as the Internet of Things (IoT), cloud computing, big data, artificial intelligence (AI), and blockchain help accelerate the digital transformation and intelligent development of petroleum companies. The remote drilling support technology (RDST) integrates drilling engineering and digital technologies to promote lean management in the drilling process. Engineering Information Smart Center (EISC) was developed based on RDST for intelligent drilling optimization design. The center could be used to improve the drilling speed, efficiency, and quality based on Measurement While Drilling (MWD) data. It could also be used to enhance the early detection rate and disposal rate of complex drilling accidents. The application of EISC has promoted economic development and safety production in many oil and gas fields. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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An Ingenious Method to Detect COVID in X-Ray Images Using Machine Learning Techniques 4

An Ingenious Method to Detect COVID in X-Ray Images Using Ma...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Kumari, Palak Rani, Neha Suresh Kumar, N. Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9781665474368

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. The World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. The COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. The global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. This paper proposes a deep neural network modification based on the Xception model. The model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. The layer addition is intended to avoid overfitting the proposed model. The proposed as a result, we compare the model's loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. Machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. The architecture of this study has been presented for the diagnosis of COVID-19. © 2022 IEEE.

关键词： COVID-19

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Research on big data Mining Application of Internet of Things Based on Artificial intelligence Technology

Research on Big Data Mining Application of Internet of Thing...

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computing, Robotics and System Sciences (ICRSS), international conference on

作者： Zihao Han Cardiff University Wales UK

In order to scientifically deal with the problem of network extension, optimize the application performance of the Internet of Things technology and equipment, and truly meet the requirements of big data application of the Internet of Things, in the development of modern economic construction, scholars from various countries in the integration of their own research experience on the basis of data mining technology as the main topic. The integration and application of artificial intelligence technology in iot big data mining not only changes the thinking mode of traditional data mining analysis, but also grasps a variety of technical methods in practical research. Therefore, on the basis of understanding artificial intelligence technology and big data mining algorithm of Internet of Things, this paper deeply discusses how to apply artificial intelligence technology algorithm in big data mining of Internet of things. The final experimental results show that the value of F -measure can be increased by 15.01 % and 17.52% at most, while R1 can be increased by 20.32 % and 25.03 % at most.

关键词： Economics data analysis Neural networks big data Market research data mining Internet of Things

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Network Cluster Reliability with Enhanced Security and Privacy of IoT data for Anomaly Detection Using a Deep Learning Model 3

Network Cluster Reliability with Enhanced Security and Priva...

引用

3rd international conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Sagar, Maloth Vanmathi, C. Vellore Institute of Technology School of Information Technology and Engineering Tamilnadu Vellore India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Cyber Physical Systems (CPS), which contain devices to aid with physical infrastructure activities, comprise sensors, actuators, control units, and physical objects. CPS sends messages to physical devices to carry out computational operations. CPS mainly deals with the interplay among cyber and physical environments. The real-time network data acquired and collected in physical space is stored there, and the connection becomes sophisticated. CPS incorporates cyber and physical technologies at all phases. Cyber Physical Systems are a crucial component of Internet of Things (IoT) technology. The CPS is a traditional concept that brings together the physical and digital worlds inhabit. Nevertheless, CPS has several difficulties that are likely to jeopardise our lives immediately, while the CPS's numerous levels are all tied to an immediate threat, therefore necessitating a look at CPS security. Due to the inclusion of IoT devices in a wide variety of applications, the security and privacy of users are key considerations. The rising level of cyber threats has left current security and privacy procedures insufficient. As a result, hackers can treat every person on the Internet as a product. Deep Learning (DL) methods are therefore utilised to provide accurate outputs from big complex databases where the outputs generated can be used to forecast and discover vulnerabilities in IoT systems that handles medical data. Cyber-physical systems need anomaly detection to be secure. However, the rising sophistication of CPSs and more complex attacks means that typical anomaly detection approaches are unsuitable for addressing these difficulties since they are simply overwhelmed by the volume of data and the necessity for domain-specific knowledge. The various attacks like DoS, DDoS need to be avoided that impact the network performance. In this paper, an effective Network Cluster Reliability Model with enhanced security and privacy levels for the data in IoT for Anomaly Detection

关键词： Internet of things

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Automatic Recognition of Parallel Sentence Based on Sentences-Interaction CNN and Its Application 7

Automatic Recognition of Parallel Sentence Based on Sentence...

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7th international conference on Computer and Communication Systems, ICCCS 2022

作者： Liu, Guanghui Fu, Lijun Yu, Bo Cui, Li University of Chinese Academy of Sciences Laboratory of Big Data and Artificial Intelligence Technology Shandong University !!!Shenyang Institute of Computing Technology @@@Chinese Academy of Sciences Beijing China Nangong No.1 Middle School Hebei China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Automated essay evaluation is to make the computer simulating human evaluate students' essay. The final grade of one essay is highly related to its literacy. The use of rhetorical devices can increase the literacy of one essay, and parallelism is one of the common rhetoric. Therefore, the identification of parallelism can contribute to automated essay evaluation. However, the research of automatic recognition of parallel sentence is so rare so far. In this paper, we design a deep learning method based on sentences interactive-convolutional neural network to recognize parallelism in the light of word co-occurrence, similar syntactic structure and tone of expression. Firstly, one or more interactive matrices whose elements represent the similarities in some ways between every token in two sentences are constructed. Then the convolutional neural network is employed to capture local features. The experimental results show that our feature selection is effective and the recognition effect is comparable with similar studies. © 2022 IEEE.

关键词： Convolutional neural networks

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A Design and Development of Internet of Things (IoT) System and Learning Activity to Promote Computational Thinking 7

A Design and Development of Internet of Things (IoT) System ...

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7th international Science, Technology, Engineering, and Mathematics Education (ISTEM-Ed) conference on Sustainable Development and Lifelong Learning

作者： Sonsilphong, Suphachoke Sonsilphong, Amornthep Hormdee, Daranee Sae-Joo, Phongthanat Khon Kaen Univ Acad Affairs Fac Med Khon Kaen Thailand Raj Amangala Univ Technol Isan Khon Kaen Campus Dept Elect Engn Khon Kean Thailand Khon Kaen Univ Fac Engn Dept Comp Engn Khon Kaen Thailand Khon Kaen Univ Fac Educ Comp Educ Program Khon Kaen Thailand

ISBN: (数字)9781665498210

ISBN: (纸本)9781665498210

Computational Thinking (CT), problem-solving and programming skills are important for the students with major of computer education of higher levels education which related to the computing science contents. The instructions must provide the basic contents and learning activities, including Internet of Things (IoT), big-data and programming, as well as Artificial intelligence (AI) and machine learning. However, these courses present learning difficulties based upon the students' individual skills. Development and integration of the IoT system would offer supporting tools for learning activities and improve the students' computational thinking, problem-solving, and programming skills. This paper presents the design and development of IoT system to support students' computational thinking, problem-solving, and programming skills training through block-based programming and integrated platforms. The IoT system comprises three layers (hardware, middleware, and applications), using existing platforms and devices as well as newly developed. To validate the viability of the proposed pipelined realization, the developed systems were implemented for higher education level students of computer education program. The students' computational-thinking, problem-solving, and programming skills were significantly improved through the proposed learning system throughout the course.

关键词： computing skills computational thinking problem solving Internet of Things computer programming

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Teaching Text Classification Models Some Common Sense via Q&A Statistics: A Light and Transplantable Approach 1

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11th CCF international conference on Natural Language Processing and Chinese computing (NLPCC)

作者： Tao, Hanqing Zhu, Guanqi Xu, Tong Liu, Qi Chen, Enhong Univ Sci & Technol China Anhui Prov Key Lab Big Data Anal & Applicat Hefei Peoples R China Univ Sci & Technol China Sch Data Sci Hefei Peoples R China

ISBN: (数字)9783031171208

ISBN: (纸本)9783031171208;9783031171192

Sociolinguistics believes that common sense is the key factor for people to ensure the robustness of language understanding and text classification in complex social environments. However, commonsense facts are often vague and implicit, and hard to be formalized or combined with current text classification techniques. As an opportunity for us, the Q&A community is a socialized platform for people to communicate knowledge, and implicit common sense can be more easily reflected by the distribution of keywords contained in Q&A data. To this end, our key insight in this paper is to obtain implicit common sense by innovatively exploiting statistical keyword distributions from annotated Q&A corpora to present a Statistics-based Label Interactive Model (SLIM) as an enhancement framework for current deep learning text classification models. Specifically, we first draw inspirations from the sociality shared by Q&A community and commonsense knowledge, and propose a light pre-classification strategy. Then, to exploit the valuable semantics of Q&A domain labels, we devise a commonsense attention module to enhance the textual representation. Afterwards, we design a label interactive attention module to make our methods transplantable to any other deep learning models and text classification datasets. Finally, extensive experiments with several deep learning text classification models on three datasets exactly verify the rationality and effectiveness of our methods.

关键词： Text classification Statistical learning Deep learning Label learning Implicit commonsense modeling

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