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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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visual Communication Design of Multi-dimensional Image visualization Fusion System Based on Wavelet Transform 3

Visual Communication Design of Multi-dimensional Image Visua...

引用

3rd IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Li, Wenjie Changchun University of Chinese Medicine Art Education Teaching and Research Section of Marxism College Changchun130000 China

ISBN: (纸本)9798350398410

With the rapid development of remote sensing technology, multi-source remote sensing image fusion has become one of the hot issues in remote sensing field in recent years. Aiming at the poor edge registration of images obtained by conventional remote sensing image fusion system, a multidimensional visual fusion system of remote sensing images based on visual communication is designed. CABAC encoder is selected to make data flow and control flow cooperate with each other to obtain feedback data. However, the rapid increase of data also increases the difficulty of comprehensive observation, because the image data obtained by different sensors have their own differences and limitations in spectral information and spatial details, so we want to bring the fusion technology into this field, expand the working range of the system, improve the reliability of the system, obtain a more efficient representation of information and improve the cost performance of the system. Image fusion technology plays an extremely important role in remote sensing, medicine, natural resources exploration, marine resources management, topography analysis, biology and other fields. The left and right focused images, infrared images and visible images are used for simulation experiments, and the objective evaluation indexes of images are used to verify the feasibility of this method. © 2023 IEEE.

关键词： Wavelet transforms

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Design of a University Book Push System Based on Big data 4

Design of a University Book Push System Based on Big Data

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4th International conference on Computer Science and Management Technology, ICCSMT 2023

作者： Sheng, Liqiang Xia, Hui Li, Xiangquan School of Information Engineering Jingdezhen University Jingdezhen333000 China

ISBN: (纸本)9798400709517

In response to the existing university book recommendation systems that mostly recommend readers based on library resources and lack exploration of recommended books, this paper proposes a system service architecture that accurately pushes both readers and libraries simultaneously. The system is designed using technologies such as web spiders, big data, NLP, and visual analysis. The system integrates reader data entry, web crawler, user profile, book similarity measurement, and intelligent push functions. Book managers can use the system to push books accurately and discover recommended books. The system fully utilizes book attribute data and reader attribute data, avoiding common cold start issues in recommendation systems, and providing support for improving the service level of the library and scientifically allocating collection resources. © 2023 ACM.

关键词： Recommender systems

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Academic Mapping Research on Elderly Consumption 2

Academic Mapping Research on Elderly Consumption

引用

2nd Asia conference on Computer vision, Image Processing and Pattern Recognition (CvIPPR)

作者： Ning, Meijun Ma, Luoru Ning, Meili North China Univ Technol Beijing Peoples R China Zhengzhou Business Univ Gongyi Henan Peoples R China

ISBN: (纸本)9798400716607

This study aims to comprehensively analyze the development of big data in elderly consumption literature. To achieve this, the study employs CiteSpace v.5.7.R5 to map the research hotspots and frontier trends, conducting visual analysis from various dimensions to investigate the research hotspots, frontiers, and development trends in elderly consumption. Furthermore, it seeks to explore their evolutionary pathways and patterns, with the ultimate goal of deepening the understanding of elderly consumption research and practice in China and providing reference significance for the high-quality development of an elderly consumption system with Chinese characteristics.

关键词： Elderly Consumption Academic Map visualization analysis

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Process of data Cleansing and handling of Memory in data Streams 15

Process of Data Cleansing and handling of Memory in Data Str...

引用

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

作者： Kavitha, N. Kalpana, Y. Kumar, v. Department of Information Technology Vels Institute of Science Technology and Advanced Studies Tamil Nadu India Agricultural Engineering Department ACRI-TNAU Tamil Nadu India

ISBN: (纸本)9798331300579

Real-time Mining [1][2][3] and Streaming of data have become more popular in the data field, with access to the fastest and the latest data. Real-time data Mining seeks the development of a real-time framework for enhancing resource efficiency while minimizing environmental impact. Real-time analysis has a huge rate of change in data, and it has to be processed and updated frequently. data mining is an interdisciplinary subject comprising machine learning, statistics, database technology, and artificial intelligence. data mining primarily aims to decipher the past and anticipate the future through data exploration and analysis, known as Knowledge Discovery in a database. data mining attempts to store the data in the local data set hosted by local computers connected to the computer networks. In the real world, data has become abundant with the advent of data streams, invariably raising the question of data storage. Also, data will not be clean when received in the form of streams. Non-clean data cannot be stored in the database and will not be effective for data analysis. An organized repository of related information must be stored in the database;hence, data must be cleaned before storage. data is cleaned as per the analysis required of the data. Once the data is cleaned, the question of the memory to store the data arises. Storing real-time data is a relatively trivial process, and there should not be any missing data from the streams. data cleaning and memory management in real-time data is always challenging. This research proposes a novel methodology for data cleaning and management of memory to overcome these issues. An algorithm is executed using the scheduler at the specific interval separating the test and trial data. Trial data will be used for further analysis, and test data will be discarded at the specific interval. The test data will be a derivative of trial data. © Grenze Scientific Society, 2024.

关键词： data consistency

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Extending Instance Space analysis to Algorithm Configuration Spaces

Extending Instance Space Analysis to Algorithm Configuration...

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2024 Genetic and Evolutionary Computation conference Companion, GECCO 2024 Companion

作者： Rasulo, Anthony Smith-Miles, Kate Muñoz, Mario Handl, Julia López-Ibáñez, Manuel The University of Manchester The University of Melbourne Manchester United Kingdom The University of Manchester Manchester United Kingdom

ISBN: (纸本)9798400704956

This paper describes an approach for deriving visual insights about the joint relationship between algorithm performance, algorithm parameters, and problem instance features. This involves the combined analysis and exploration of a 2D instance space, to which instances from some problem space are projected, and a 2D configuration space, to which (algorithm) configurations are projected. Extending on the dimensionality reduction problem solved in Instance Space analysis, we define an optimisation problem for finding projections to these two spaces, with an interpretable relationship between them. Then, we describe the tools developed for probing those spaces in an investigation of the question: What characterises the algorithm configurations that perform best on a selected group of instances (or vice versa)? We demonstrate the use of these tools on synthetic data with known ground truth. © 2024 held by the owner/author(s).

关键词： Consensus algorithm

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If CLIP Could Talk: Understanding vision-Language Model Representations Through Their Preferred Concept Descriptions

If CLIP Could Talk: Understanding Vision-Language Model Repr...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Esfandiarpoor, Reza Menghini, Cristina Bach, Stephen H. Department of Computer Science Brown University United States Data Science Institute Brown University United States

ISBN: (纸本)9798891761643

Recent works often assume that vision-Language Model (vLM) representations are based on visual attributes like shape. However, it is unclear to what extent vLMs prioritize this information to represent concepts. We propose Extract and Explore (EX2), a novel approach to characterize textual features that are important for vLMs. EX2 uses reinforcement learning to align a large language model with vLM preferences and generates descriptions that incorporate features that are important for the vLM. Then, we inspect the descriptions to identify features that contribute to vLM representations. Using EX2, we find that spurious descriptions have a major role in vLM representations despite providing no helpful information, e.g., Click to enlarge photo of CONCEPT. More importantly, among informative descriptions, vLMs rely significantly on non-visual attributes like habitat (e.g., North America) to represent visual concepts. Also, our analysis reveals that different vLMs prioritize different attributes in their representations. Overall, we show that vLMs do not simply match images to scene descriptions and that non-visual or even spurious descriptions significantly influence their representations. Code: https://***/BatsResearch/ex2. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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exploration on Environmental Protection Supported by Big data 3

Exploration on Environmental Protection Supported by Big Dat...

引用

3rd International conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Baifang Sui, Liqiu School of Business Beijing Language and Culture University Beijing China WEI Fang Bank Weifang China

With the rapid development of social economy, environmental problems are becoming increasingly severe, and human society is facing serious environmental pollution. Therefore, it is very necessary to apply big data technology to environmental protection work. Through the analysis of the application characteristics of big data technology, it can be seen that big data technology has made environmental protection work data-driven, and also further embodied the fundamental value of environmental protection. This article analyzed the changes in the environment by establishing environmental protection measures and applying big data, and calculated whether there was a significant improvement in the environment supported by big data technology. This article calculated the dust reduction data after the intervention of big data environmental protection measures in 2022 through experiments, which was significantly lower than the dust reduction data in 2019. Therefore, it has great advantages in environmental detection and other aspects. © 2023 Elsevier B.v.. All rights reserved.

关键词： Big data

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Crayfish Optimization Algorithm for Enhanced Feature Selection Accuracy for datasets

Crayfish Optimization Algorithm for Enhanced Feature Selecti...

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International conference on Computing and Machine Learning, CML 2024

作者： Rambabu, Bandi Archana, M. Swathi, v.N.v.L.S. Lakshmi, Gundala venkata Rama Reddy, Jakkala Ravichandar CVR College of Engineering Hyderabad India Vijay Rural Engineering College Borgaon India

ISBN: (纸本)9789819778386

Feature selection plays a crucial role in machine learning and data analysis, enabling efficient model training, improved accuracy, and enhanced interpretability. However, traditional methods often struggle to achieve optimal feature subsets when dealing with high-dimensional data, redundant features, or complex interactions. The Crayfish Optimization Algorithm (COA) emerges as a bio-inspired approach inspired by the foraging behavior of crayfish, offering a promising solution to these challenges. This paper presents COA, a novel metaheuristic algorithm for feature selection that mimics the crayfish's exploration–exploitation balance. COA utilizes random walks for global exploration, guided by a fitness function that evaluates feature subset relevance and model performance. Simultaneously, COA employs a unique "pheromone-like" memory mechanism to exploit promising feature neighborhoods, accelerating convergence and enhancing accuracy. We evaluate COA's performance on diverse datasets and benchmark problems, comparing it to established feature selection algorithms. Our results demonstrate that COA significantly outperforms conventional methods in terms of selection accuracy, model performance, and robustness to noise and irrelevant features. Additionally, COA exhibits faster convergence and reduced computational complexity, making it particularly suitable for large-scale datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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Factor analysis: Uncovering Factor Structure in the Indonesian Teacher Engagement Index (ITEI) Survey Results data 11

Factor Analysis: Uncovering Factor Structure in the Indonesi...

引用

11th International conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Cam, Grace Shirley Sasmoko Indrianti, Yasinta Manalu, Sonya Rapinta Moniaga, Jurike v. Data Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Primary Teacher Education Department Faculty Humanities Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Podomoro University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331542313

This study aims to explore the latent structure of the Indonesian Teacher Engagement Index (ITEI) based on the ITEI survey results of primary school teachers in various regions of Indonesia. exploration of the ITEI structure is obtained by identifying the main factors hidden in the ITEI survey data with machine learning. The machine learning method used is Exploratory factor analysis (EFA) with Principal Component analysis (PCA). The ITEI dataset consists of 10,365 records, covering 44 items from 6 ITEI dimensions. The machine learning analysis yielded 4 main factors that became new dimensions, explaining the variation of 29 items. The four new factors found are summarized into the dimensions of values and Ethics in Education, Personal and Social Development in Education, Character and Leadership in Education, and Social Competence and Justice in Education. This exploration was conducted to show the latent structure of the ITEI that matches the real situation and provide a deeper understanding of the main components that make up the teacher engagement index in Indonesia, so that conclusions can be drawn accurately from the survey results. The implications of these findings can be used to improve understanding and development of more effective education policies in the future. © 2024 IEEE.

关键词： Adversarial machine learning

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data Processing Methods for Financing Terrorism: The Role of Microsoft Power BI in Money Laundering Detection 15

Data Processing Methods for Financing Terrorism: The Role of...

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15th International conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International conference on Emerging data and Industry 4.0, EDI40 2024

作者： Kulbayeva, A.K. Rakhmetulayeva, S.B. Bolshibayeva, A.K. Yasar, Ansar-Ul-Haque Information Systems Department International Information Technology University Almaty050040 Kazakhstan Transportation Research Institute Hasselt University Hasselt3500 Belgium

This article explores the imperative landscape of money laundering detection, emphasizing the role of advanced financial monitoring and the integration of Microsoft Power BI for in-depth data analysis. Addressing challenges in fraud detection, the study employs synthetic datasets from Kaggle, overcoming limitations in accessing real financial data. Through a meticulous process, the article demonstrates the power of Power BI in uncovering potential money laundering activities via visual analyses such as stacked column charts, line charts, pie charts, and clustered bar charts. The significance of appropriate data preparation, column selection, and visual representations is highlighted, offering a systematic approach for fraud detection in financial datasets. This research showcases the effectiveness of Power BI as a strategic tool, providing insights and methodologies that contribute to the ongoing discourse on fraud prevention in financial analytics. © 2024 Elsevier B.v.. All rights reserved.

关键词： data handling

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