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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1561 条记录，以下是311-320 订阅

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Exploring Urban Residents' Spatiotemporal Travel using Coupled Optimal Sequence Alignment and Frequent Pattern Mining

Exploring Urban Residents' Spatiotemporal Travel using Coupl...

引用

2023 international conference on cognitive computing and complex data, ICCD 2023

作者： Fu, Jia Zhao, Feng University of Jinan School of Civil Engineering and Architecture Jinan250002 China

ISBN: (纸本)9798350306293

This article explores the method of combining sequence analysis and data mining to explore spatiotemporal behavior sequence patterns based on fine-grained spatiotemporal activity sequence data of urban residents. Obtain activity log data from 1000 households through a questionnaire survey. Utilize the most effective sequence alignment algorithm to mine sequence patterns in spatiotemporal behavior sequence data, and identify potentially useful behavior patterns for each type of sequence pattern through frequent pattern mining. The experimental results indicate that the combination of sequence alignment method and frequent pattern mining has been successfully applied in the analysis of urban residents' behavior patterns, making up for the shortcomings of traditional data analysis methods. This provides a new perspective for the analysis of complex spatiotemporal behavior data. © 2023 ieee.

关键词： data mining

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Research on Big data Financial Fraud Detection System Based on Machine Learning 5

Research on Big Data Financial Fraud Detection System Based ...

引用

5th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Li, Yutong School of Management and Innovation SciencesPo Paris Paris75007 France

ISBN: (纸本)9798350368284

To solve the problem of fraud detection in financial transaction data, this paper proposes an innovative detection scheme that integrates machine learning and big data analysis technology. We combine the subtleties of time series analysis with the power of modern data analysis to design a model that is both efficient and accurate in identifying financial fraud. First, advanced data acquisition technology is used to capture high-frequency financial transaction data in real time to ensure the freshness and integrity of data. Then, the ARIMA (autoregressive integral moving average) model is used to analyze these time series data in depth to reveal the hidden information behind the trading patterns and trends. On this basis, the LightGBM (Lightweight Gradient Elevator) algorithm is introduced, which is a machine learning tool that performs well when dealing with high-dimensional, sparse data, especially for complex data structures in the financial sector. By integrating the time series features mined by ARIMA with multi-dimensional information such as credit history and geographical location of both parties, as the input of LightGBM model, we construct a multi-modal fraud detection framework. Empirical research shows that compared with using ARIMA model alone or traditional machine learning methods, our hybrid model has achieved a significant breakthrough in the accuracy and response speed of fraud detection, which can not only perceptively capture abnormal transaction activities, but also proactively warn potential financial risk points. © 2024 ieee.

关键词： data integrity

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Enhancing Personalized Medicine Using data Analytics and Artificial Intelligence 7

Enhancing Personalized Medicine Using Data Analytics and Art...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Tejpal, Karan Singh, Simar Preet University of Massachusetts Lowell United States Dav University Jalandhar India Bennett University Greater Noida India

ISBN: (纸本)9798350350067

Personalized medicine is a tailored method in healthcare that uses specific genetic, environmental, and lifestyle information to develop individualized treatment plans. Its aim is to improve treatment outcomes by staying away from one-size-fits-all model, which offers more precise and effective care. The goal of personalized medicine is to tailor the medical treatment to specific patient characteristics, which further improves the outcome and reduces the adverse effects. This paper discusses how data analytics, machine learning (ML), artificial intelligence (AI), and python can be integrated to provide enhanced personal medicine. The paper demonstrates how analytical methods are able to process complex datasets to predict patient response and optimize the treatment plans. Our study highlights notable improvements in predictive accuracy and clinical decision support, which reveals the potential of these technologies to transform health care delivery. © 2024 ieee.

关键词： artificial intelligence data analytics Machine learning medicine personalized medicine python programming

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Synthetic data Generation for Document Text Recognition 1

Synthetic Data Generation for Document Text Recognition

引用

1st international conference on cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Ashlin Deepa, N.R. Srija, Boda Jabeen, Maria Kankar, Kushi Reddy Lakshmi, Bollepalli Jhansi Vijayalata, Y. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India Vardhaman College of Engineering Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350361780

Handwritten text recognition software is used to recognize and extract text from scanned documents. The fun-damental goal of this technology is to transform printed or handwritten text into an easily readable electronic format. The numerous characters and enormous quantity of the information for Indian languages, however, causes explicit preprocessing to take time. This necessity for explicit preprocessing has been replaced by synthetic data creation techniques, which overcome several difficulties and speed up the process. Artificially produced data that closely mimics actual observations is referred to as synthetic data. In situations where getting real data is difficult or expensive, it offers a workable substitute for training machine learning models. In this work, we propose a data preprocessing technique that creates synthetic data files from the already-existing collection of Indian languages. We use a pre-trained language model called FastText model, which is capable of creating word embeddings, to generate synthetic dataset from the real-time dataset. With the aid of the generated synthetic datasets, document text recognition systems can undergo extensive training and testing, allowing them to grasp the complexities of Indian languages and perform accurate text extraction from scanned documents. © 2024 ieee.

关键词： Embeddings

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data Visualization Analysis of Metacognition in BE Problem Solving: Re-categorization and Its Implications 8

Data Visualization Analysis of Metacognition in BE Problem S...

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8th international conference on Cloud computing and Big data Analytics (ICCCBDA)

作者： Yin, Xueyan Guo, Guihang Guangdong Univ Foreign Studies South China Business Coll Sch English Language & Culture Guangzhou Peoples R China Guangdong Univ Foreign Studies Sch English Int Business Guangzhou Peoples R China

ISBN: (纸本)9781665455336

Widely acknowledged as knowledge and cognition about cognitive phenomena, metacognition has become a successful tool for researchers to investigate problem solving and other thinking processes in the instructional domain. However, much of the work on metacognition has studied these constructs piecemeal and mostly qualitatively with little evidence found about cognition in business English problem solving. In light of this, by statistically based re-categorization of metacognition in business English context, we investigated the interplay of metacognitive aspects in BE problem solving. 105 third-year college students participated in the study. Metacognitive aspects (metacognitive experiences, metacognitive knowledge and metacognitive regulation) were quantitatively measured with statistical tools of SPSS, AMOS and Wordclouds, in which questionnaires and semi-structured interviews were employed to further explore 2 questions: (1) What are the prominent factors constituting BE metacognition? (2) To what extent is metacognition positively related to BE problem solving and perceived performance? Three categories of BE metacognition are identified and the data visualization analysis indicate a complex interplay of metacognition in BE problem solving, in which metacognitive knowledge and metacognitive regulation play a statistically significant role.

关键词： data visualization analysis metacognition re-categorization BE problem solving metacognitive regulation metacognitive knowledge metacognitive experience

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Piecewise Computation of Persistent Homology

Piecewise Computation of Persistent Homology

引用

2024 ieee international conference on Big data, Bigdata 2024

作者： Singh, Rohit P. Malott, Nicholas O. Wilsey, Philip A. Univ of Wisconsin MilwaukeeWI United States Interstates CincinnatiOH United States Univ of Cincinnati CincinnatiOH United States

ISBN: (纸本)9798350362480

Persistent Homology (PH) is a widely used tool of Topological data Analysis (TDA) that measures the persistence of homological features present in data. PH is computed on a sequence of nested sub-complexes that form a filtration KF of data. In general, each nested sub-complex is defined by a scale parameter ϵ that defines the connectivity distances used to for its construction. These sub-complexes are arranged by increasing distances, ϵ0 to ϵ∞, in the filtration;PH is then computed from KF. However, due to its exponential space and time complexity, computing PH on big data is beyond the capabilities of contemporary machines. This paper explores the Piecewise computation of PH (PwPH) using subset constructions of the filtrations of data. PwPH is a framework for solutions that compute PH of data. In some embodiments, PwPH can be support computations of PH can be assembled into a complete picture of the homologies;in others, PwPH can only compute an approximation of the PH. The general solution of PwPH can be organized to use significantly less memory and provides a partition of computational PH elements that is, in some embodiments, embarrassingly parallel. This paper explores two complementary foundations to organize filtrations for PwPH. © 2024 ieee.

关键词： Spatio-temporal data

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Exploring Advanced Algorithms in Multi-Dimensional Spatial data Modeling for Enhanced Geospatial Analysis

Exploring Advanced Algorithms in Multi-Dimensional Spatial D...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Asha, V. Sreekanth, S. Nijhawan, Ginni Kansal, Vipashi Ram Kumar, R.P. Abd Ulabbas Abedi, Tamam Ali Department of Computer Applications N.H.C.E Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal500043 India LPU Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of CSBS GRIET Bachupally Telangana Hyderabad India College of Medical Technology The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

This study examines map analysis with the goal of improving current methods by looking at cutting-edge algorithms for modeling multidimensional spatial data. Tensor Decomposition (TD), Convolutional Neural Networks (CNNs), and Spatiotemporal Regression (STR) are all parts of the suggested method that work together to solve the complex problems that come up when representing spatial data. The abstract provides a concise summary of the primary objectives and methodologies of the study, while also elucidating the rationale behind its execution. In our work, we use new developments in deep learning and machine learning to go beyond common ways of looking at space, such as graph-based spatial analysis, geostatistical methods, and space partitioning strategies. When you combine convolutional neural networks with tensor decomposition, you can get hierarchical features out of the data and figure out what the complex spatial patterns mean. Using spatial-temporal regression also helps us understand spatial events better by adding temporal changes to the analysis framework. Our study's goal is to push the boundaries of geographic research by combining the newest techniques. This will make it possible to create more precise and all-encompassing methods that can correctly understand the complex nature of location data in many dimensions. © 2024 ieee.

关键词： Spatio-temporal data

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Exploring data Visualization Techniques for Large datasets 1

Exploring Data Visualization Techniques for Large Datasets

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1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sikarwar, Shailendra Singh Kumawate, Bhawesh Vaishnav, Arun Kumar, Love Bushra, Seema Devi, S. Teitsana PGV College Dept. of Computer Science Gwalior India Madhav University Dept. of CSE Pindwara India Mangalayatan University Dept. of Computer Eng. and Applications Beswan India Deen Academics Lingayas Vidyapeeth Faridabad India Dhanalakshmi Srinivasan University Dept. of CSE Coimbatore India

ISBN: (纸本)9798350356816

In a world of Big data, being able to easily visualize large data sets can make the difference between understanding & wisely judging that data - or not. Big data is the word with which not many people are aware of, but these success stories make sure that organizations sit back & take notice to what this large data can show then when it collated together over time from different sources. The methods traditionally used for visualization rarely fit into the Big-data world because they do not have scalability, given that volume (huge amounts), velocity (high speed), or variety (complex details) are now more prevalent in big-data processing. This paper then explores data visualization techniques that can be used to manage very large datasets, including streaming data visualization, dimensionality reduction (e.g. T-SNE), & hierarchical clustering among other Node XL analyses. In this paper, based on accumulated observations & implementations of these techniques over the years, an extensive review is provided for researchers or practitioners to select suitable visualization methodologies that not only suit the scale but also discover patterns in their data. The results highlight the need for enabling tools, methods, & practices to make sense of big data in an interactive visual context that enables better orchestration between complex systems using diverse hardware/software stacks. Additionally, the pros & cons of each method are explained, with real-world use cases & case studies demonstrating their practicality. © 2024 ieee.

关键词： data assimilation

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Beyond the Stats: How Investment Decisions Are Influenced by Non-Accounting data 2

Beyond the Stats: How Investment Decisions Are Influenced by...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Sharma, Rohit Prajkta Yawalkar, Dr. Sanghvi, Mahima Bikash Acharjee, Purnendu Ashfaq, Alaina Dixit, Aviral Christ University Bengaluru India

ISBN: (纸本)9798350384277

Making investment decisions is a complex process that is influenced by data from non-accounting and accounting sources. In order to better understand the importance of financial reports in comparison to non-accounting data [1], this article examines this complexity. The study is guided by three main objectives: determining the relative importance of financial reports against non-accounting sources;determining the effect of non-accounting information on investment decisions [2];and investigating the role of demographic factors on this effect. The study finds that, when making investment decisions, shareholders more frequently turn to non-accounting sources through thorough analysis and statistical testing. Notably, credit rating agencies, stock indices, and brokers all have a big say in how decisions are made, highlighting their significance. This work improves our knowledge of how accounting and non-accounting data interact to influence investment decision-making. It emphasizes how crucial it is to take into account a variety of information sources in order to make wise financial decisions [3]. When navigating the ever-changing market landscape of today, investors, financial analysts, and politicians can benefit greatly from these ideas. © 2024 ieee.

关键词： accounting information decision-making factors financial markets financial reports investment analysis Investment decision-making market information non-accounting information shareholders

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DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting 2

DAM: A Universal Dual Attention Mechanism for Multimodal Tim...

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2024 international conference on Metaverse computing, Networking, and Application

作者： Fu, Yihang Zhou, Mingyu Zhang, Luyao Duke Kunshan Univ Data Sci Res Ctr 8 Duke Ave Suzhou 215316 Peoples R China Duke Kunshan Univ Social Sci Div 8 Duke Ave Suzhou 215316 Peoples R China

ISBN: (纸本)9798331516000;9798331515997

In the realm of distributed systems, blockchain technology has spurred the development of cryptocurrencies, merging enhanced security and decentralization with significant investment potential. Despite their promise, existing cryptocurrency trend forecasting often oversimplifies sentiment data, failing to fully capture the complex interplay between financial market dynamics and external sentiment influences. This paper introduces a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach integrates key cryptocurrency metrics with sentiment data from news and social media, analyzed through CryptoBERT, effectively addressing the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models, such as LSTM and Transformer, by up to 20% in prediction accuracy. This advancement not only deepens the understanding of distributed systems but also has practical implications for stakeholders in the cryptocurrency and blockchain sectors.

关键词： Cryptocurrency Multimodality Time-series Forecasting Sentiment Analysis Deep Learning Blockchain CryptoBERT LSTM Transformer

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