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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是591-600 订阅

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Predicting Cryptocurrencies Market Phases through On-Chain data Long-Term Forecasting 5

Predicting Cryptocurrencies Market Phases through On-Chain D...

引用

5th IEEE international conference on Blockchain and Cryptocurrency (ICBC)

作者： Casella, Bruno Paletto, Lorenzo Univ Turin Dept Comp Sci Turin Italy Univ Turin Comp Sci Dpt Corso Svizzera 185 Turin Italy

ISBN: (纸本)9798350310191

Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network's activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies' future prices;however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.

关键词： Blockchain Bitcoin cryptocurrencies on-chain data artificial intelligence prediction asset allocation trading rules markets

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Studying the Energy Consumption of Stream processing Engines in the Cloud 11

Studying the Energy Consumption of Stream Processing Engines...

引用

11th IEEE international conference on Cloud Engineering (IC2E)

作者： Govind, K. P. Pierre, Guillaume Rouvoy, Romain Univ Rennes INRIA CNRS IRISA Rennes France Univ Lille INRIA CNRS UMR 9189CRIStAL Lille France

ISBN: (纸本)9798350343946

Reducing the energy consumption of the global IT industry requires one to understand and optimize the large software infrastructures the modern data economy relies on. Among them are the data stream processing systems that are deployed in cloud data centers by companies, such as Twitter, to process billion of events per day in real time. However, studying the energy consumption of such infrastructures is difficult because they rely on a complex virtualized software ecosystem where attributing energy consumption to individual software components is a challenge, and because the space of possible configurations is large. We present GreenFlow, a principled methodology and tool designed to automate the deployment of energy measurement experiments for data stream processing systems in cloud environments. GreenFlow is designed to deliver reproducible results while remaining flexible enough to support a wide range of experiments. We illustrate its usage and show in particular that consolidating a DSP system in the smallest number of servers that are capable of processing it is an effective way to reduce energy consumption.

关键词： Green computing data stream processing energy consumption reproducibility

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Prediction of Bitcoin Price through LSTM, ARIMA, XGBoost, Prophet and Sentiment Analysis on Dynamic Streaming data 9

Prediction of Bitcoin Price through LSTM, ARIMA, XGBoost, Pr...

引用

9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Ramani, K. Jahnavi, M. Reddy, P. Jagadeesh Venkatachakravarthi, P. Meghanath, P. Imran, S.K. Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College School of Computing Tirupati India Sree Vidyanikethan Engineering College B.Tech-Information Technology Tirupati India

ISBN: (纸本)9798350397376

The most popular cryptocurrency in the world is Bitcoin, which enables users to perform secure online transactions. When carrying out quick transactions, including cash transactions, this aids in keeping your money secret. Most of Consumers have been interested in the Bitcoin ecosystem in recent years. Predicting the bitcoin price accurately is a difficult task due to its high volatility. In this paper, we used deep learning and machine learning algorithms namely Long Short-Term Memory, Autoregressive Integrated Moving Average , XGBoost, Prophet and Sentiment analysis were performed on bitcoin *** algorithms were trained on live streaming finanacial data, and results are compared based on predicted metrics like Root mean Square Error,Mean Absolute Error and R2. The results show that Sentiment analysis combined with LSTM provide better performance in bitcoin price prediction of all other algorithms. © 2023 IEEE.

关键词： Long short-term memory

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Meta-learning. An Approach Applied to Clinical data 15th

Meta-learning. An Approach Applied to Clinical Data

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15th international conference on Ubiquitous computing and Ambient Intelligence

作者： Amador, Sandra Turbay, Kelly Montoro, Alicia Espinilla, Macarena Mora, Higinio Gil, David Univ Alicante Dept Comp Technol & Computat Alicante Spain Univ Jaen Jaen Spain

ISBN: (纸本)9783031486418;9783031486425

Currently, there is a large amount of digitized data. In the field of health, an important part of the data is obtained from electronic health records, related to the health of people. This stream of data has led to a multitude of research efforts in the healthcare domain, focusing on various aspects such as disease prediction, clinical risk assessment, mortality analysis, and more. These predictions assist healthcare providers in early identification of potential risks, leading to better patient care. The objective of this work is to propose a meta-learning model applied to clinical data in which the different algorithms included can be compared and the benefits of each of them appreciated. Meta-learning involves algorithms learning about other algorithms through experience. However, the application of meta-learning to diagnosis poses considerable practical challenges. A case study is used to test the model and assess its effectiveness. In this study, the application of meta-learning showed very promising initial results, adapting the algorithm to the database used depending on the disease to be predicted.

关键词： meta-learning clinical data artificial intelligence

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Storage aware data management system for Genomics 5

Storage aware data management system for Genomics

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5th international conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Shah, Zeeshan Ali Farid, Mohsen University of Derby School of Computing and Mathematics United Kingdom

ISBN: (纸本)9798400708923

In recent years, nucleotide sequencing has become increasingly instrumental in both research and clinical settings. This has led to explosive growth in sequencing data produced worldwide along with an increase in complex analysis algorithms. As the amount of data and analysis increases, so does the need for automated solutions for processing and analysis. The concept of workflows has gained favor in the bioinformatics community, but there is little in the scientific literature describing end-to-end operational automation systems. We provided an automation system that aims at providing a solution to the genomics related operational challenges that face sequencing of both research and clinical facilities. We built on existing open-source technologies, with a modular design allowing for a community-driven effort to create plug and play services. In this research, we describe the system and elaborate on the underlying conceptual framework. Which can be reduced to 3 conceptual levels: data tagging (using metadata automation), Classifying Storage systems (the steps involved in the classification of storage systems), and execution (using a series of rules to move data around on an operational level). © 2023 Owner/Author.

关键词： Genome

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Computer data processing and Pattern Construction in the Internet Era

Computer Data Processing and Pattern Construction in the Int...

引用

2023 international conference on Big data Mining and Information processing, BDMIP 2023

作者： Mo, Fan School of Intelligent Manufacturing Zhanjiang University of Science and Technology Guangdong524094 China

ISBN: (纸本)9798400709166

With the continuous improvement of technological level, computer data processing technology has also developed and advanced. The rapidly developing information age has also put forward higher requirements for data processing. People need to constantly improve their processing modes in order to ensure data and information security, achieve higher processing efficiency, and meet the growing needs of people. Therefore, this article conducts research on computer data processing and pattern construction from this perspective. Through data comparison methods, this article compares the performance of different data processing methods and technologies in various aspects such as processing quality, work efficiency, and classification and screening accuracy to evaluate their respective advantages and disadvantages. It proposes corresponding suggestions based on the research results, analyzes the computer data processing and models in the current Internet era, and promotes the continuous development and optimization of processing technology. The research results indicate that intelligent computing data processing methods have significant advantages, with a processing quality of 93%, which is much better than other methods in terms of processing efficiency. © 2023 ACM.

关键词： Security of data

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High-Capacity Reversible data Hiding in Encrypted Images Based on MSB Bit-Plane Serpentine Rearrangement and Compression 8

High-Capacity Reversible Data Hiding in Encrypted Images Bas...

引用

8th international conference on Communication, Image and Signal processing, CCISP 2023

作者： Hu, Tinghua Qin, Sheng Wang, Kaiyang Guangxi Key Lab of Brain-inspired Computing Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China International Joint Audit Institute Nanjing Audit University Nanjing China

ISBN: (纸本)9798350305838

Reversible data hiding techniques in encrypted images (RDH-EI) have been extensively researched to safeguard the privacy of images. To enhance the embedding capability of RDH-EI algorithms and security, this work leverages the topological coherence within the input plaintext image to create more additional space for embedding confidential information prior to image encryption. Initially, the prediction error of the input image is computed using the pixel prediction technique. Subsequently, the bit-plane serpentine rearrangement mechanism and the bit-stream compression method are employed to further enhance the process. This compression technique efficiently compresses the most significant bit-planes, resulting in a high-capacity space for embedding. Finally, the end-user successfully extracts the data and restores the input image using the possessed keys. The results clearly show that the proposed approach achieves not only attains a superior embedding capacity which achieves the maximum average embedding rate with a value of 3.624 (bpp), but also obtains a commendable reconstructed image quality that is PSNR=+∞ and SSIM=1. © 2023 IEEE.

关键词： Forecasting

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Cross Domain Few Shot NER Via data Enhancement Invariant Domain Feature Extraction Based on Diffusion Model 21

Cross Domain Few Shot NER Via Data Enhancement Invariant Dom...

引用

21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Guan, Kunlun Zhang, Yangsen Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350329223

When addressing the Named Entity Recognition (NER) challenge in low-resource environments, our primary focus is on identifying named entities in a limited number of target areas with sparse annotation data. Traditional cross-domain NER methods have been hampered by the discrepancies between different domains, resulting in limited generalization capabilities in specific scenarios. To overcome this limitation, we have developed an innovative approach that leverages the power of BERT, an advanced language representation model, to extract features from both the source and target domains. Furthermore, we have introduced the Latent OPT framework, a text content generation diffusion model based on ordinary differential equations (ODE), to expand the source domain data and generate high-quality data volumes. Concurrently, we propose a novel autoregressive framework that strengthens the association between tags and tokens, thereby promoting cross-domain NER and enhancing the transferability of tag information. This framework is also based on pre-trained language models (PLMs) with continuous prompts, which perform entity enhancement and context enhancement through five basic augmentation operations, generating examples of label flipping and label preservation. Our research is the first to successfully address the small sample NER problem using a generative diffusion model, and its innovation and effectiveness have been validated by comprehensive experiments across various benchmark tests. © 2023 IEEE.

关键词： Ordinary differential equations

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Big data Analytical Engine for Exploring the Power in Modern Business

Big Data Analytical Engine for Exploring the Power in Modern...

引用

2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Enquero, Shreya Patchala Padhi, Manoj Kumar Yamsani, Nagendar Anitha, M. Revanasiddappa, Revanasiddappa Global-A Genpact Company Bangalore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Sr University School of Computer Science and Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Most current applications of Big data focus on examining consumer preferences and feedback expressed through social media and legitimate websites. data is generated by all parties involved in the company, including mobile device location data and data created by machines with high-tech sensors, automated controls, and relevant software. We are currently living through an Internet revolution fueled by Big data following the Industrial and Internet revolutions. Therefore, the implementation of Big data platforms represents a significant opportunity for enterprises today. This article presents three case studies that highlight the potential of Big data in the cellular phone business, online retail, and insurance sales over the Internet. To help companies locate, gather, and analyze huge amounts of data, a Big data Analytical Engine has been proposed. From the analysis, it clearly shows that proposed model achieves better results in terms of accuracy (0.92), precision (0.93), recall (0.91) and f1-score (0.92) respectively. © 2023 IEEE.

关键词： Big data Analytics Comparative Study Customer Churn data Preprocessing Industry-Specific Recommendations Machine Learning algorithms Performance Evaluation

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Domain-Oriented Transformation Method for Big data Analysis Process Model

Domain-Oriented Transformation Method for Big Data Analysis ...

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2023 international conference on Networking, Informatics and computing, ICNETIC 2023

作者： Liu, Yijun Shi, Lei Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350313314

In order to ensure data mining analysis and knowledge discovery in the context of multi-disciplinary fields, big data analysis takes into account domain complexity, data analysis ease of use, and efficient execution. The domain-driven big data analysis process modeling is proposed to guide the construction and implementation of big data analysis process models. The big data analysis process is divided into a domain-oriented and platform-oriented two-layer model, and the transformation between the two is automatically done through the model-driven model mapping method. The domain-oriented analysis model is defined from the perspective of domain business, allowing users to focus on the analysis logic itself during the big data analysis process construction phase, without concern for the implementation details of specific algorithms. The platform-oriented analysis model is defined from the perspective of calculation and execution, so that the big data analysis process makes full use of the platform's computing resources, storage resources, and algorithm resources during the execution phase, the process execution efficiency and algorithm calculation speed are improved. Finally, the Hadoop platform as the underlying execution platform is taken as an example to illustrate the transformation process of the domain-oriented big data analysis process model. © 2023 IEEE.

关键词： Big data

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