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Integration of Neural Network Models in Spatial data analysis Systems 6

Integration of Neural Network Models in Spatial Data Analysi...

引用

6th International Scientific conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Yamashkin, Stanislav A. Yamashkina, Ekaterina O. Yamashkin, Anatoliy A. National Research Mordovia State University Institute of Electronics and Lighting Engineering Saransk Russia Geography Faculty National Research Mordovia State University Saransk Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

The article presents a practical solution in the field of integration of neural network models in spatial data analysis systems. The article shows that the deep neural network model can to be differentiated by topology that defines availability in her structure layers certain type and connections between them and architecture neural networks defines format representation input data and view weekend signal models, parameters functioning and strategies learning. Component visual design neural network models allows visualize neural network models in form graph - diagrams with opportunity interactive online editing them topology and architecture. For efficient analysis spatial data implemented broadcast graphic representation models in performance on the language Python based on the application frame deep machine learning Keras, and also on the language MATLAB and package applied modules deep Learning toolbox. For solutions tasks broadcasts graphic representation neural network models in program the code her structure described oriented acyclic graph, and key algorithm transformations graphic representation models in instructions on the language programming general destination is topological sorting. © 2022 IEEE.

关键词： Neural network models

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visual analysis of Spatio-temporal Phenomena with 1D Projections

Visual Analysis of Spatio-temporal Phenomena with 1D Project...

引用

Eurographics / IEEE vGTC conference on visualizaation

作者： Franke, M. Martin, H. Koch, S. Kurzhals, K. Univ Stuttgart Stuttgart Germany Swiss Fed Inst Technol Zurich Switzerland Inst Adv Res Artificial Intelligence Vienna Austria

It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.

关键词： Computer graphics

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Decoded visual Stimulus Studies Using Functional Magnetic Resonance Imaging 22

Decoded Visual Stimulus Studies Using Functional Magnetic Re...

引用

Proceedings of the 2022 6th International conference on Computational Biology and Bioinformatics

作者： Junzhu Ma Yiming Zhu Stuart Country Day School USA the Hong Kong polytechnic university China

ISBN: (纸本)9781450397636

In this paper, data from Kay experiment were used to explore how different brain regions involved in visual processing encode different types of visual stimuli, and whether this process has similarities to principal component analysis (PCA). By correlating brain activity with projections on principal components (PCS) of images extracted from the Kay image dataset, v1 and v2 voxel activities were found to have the highest average correlations with image projections on the top 10 PCS. Meanwhile, v2 processes "animal" images, a set of photos of animals, such as a giraffe, that most closely resemble PCA across all brain regions. Next, the brain voxels were correlated with the vectors of each image label, showing an increasing correlation between brain activity and the semantic information of the corresponding visual stimuli. This trend was further demonstrated on "artifact" images, with the exception of v4, after the same correlations were calculated based on image categories.

关键词： visual stimulus magnetic resonance image principal component analysis

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AI-Driven Personalized Skincare Recommendations

AI-Driven Personalized Skincare Recommendations

引用

Electronics and Renewable Systems (ICEARS), International conference on

作者： Iyyanar Perumal Kalaivani P Naveenkumar A Saran v Sriram K Suruthi N Department of Information Technology Sona College of Technology Salem India Department of CSE Kongu Engineering College Perundurai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study investigates the utilization of artificial intelligence (AI) to transform skincare product recommendations and the formulation of individualized routines through the analysis of user inputs and characteristics. The system utilizes data including skin type, acne count, oiliness level, and Uv exposure, using KNearest Neighbors (KNN) for personalized product suggestions. Natural Language Processing (NLP) approaches are utilized to develop tailored 7-day skincare regimen recommendations by examining product and user problems, converting unstructured data into actionable insights. Moreover, deep learning algorithms are employed for real-time image-based assessments of skin conditions, facilitating precise acne diagnosis and oiliness evaluation. The system integrates visual data with textual analysis, continually adapting through user feedback to improve accuracy and relevance. This article highlights the capacity of AI-driven methods to automate routine generation and provide tailored product recommendations, fostering innovation in the cosmetics sector through a thorough, data-oriented strategy.

关键词： Deep learning visualization Accuracy Transforms Nearest neighbor methods Skin Natural language processing Real-time systems Artificial intelligence Wearable devices

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vEHICLE: validation and exploration of the Hierarchical Integration of Conflict Event data

VEHICLE: Validation and Exploration of the Hierarchical Inte...

引用

Eurographics / IEEE vGTC conference on visualizaation

作者： Mayer, B. Lawonn, K. Donnay, K. Preim, B. Meuschke, M. Univ Magdeburg Dept Simulat & Graph Magdeburg Germany Univ Jena Dept Comp Sci Jena Germany Univ Zurich Dept Polit Sci Zurich Switzerland

The exploration of large-scale conflicts, as well as their causes and effects, is an important aspect of socio-political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi-automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user-defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developed vEHICLE, a web-based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map-based depictions as well as advanced interaction techniques. We showed the usefulness of vEHICLE in two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.

关键词： Integration

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Smart Tunnel Traffic Control System using IoT and Machine Learning

Smart Tunnel Traffic Control System using IoT and Machine Le...

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2025 International conference on visual Analytics and data visualization, ICvADv 2025

作者： Subramani, v. vishnu Kumar, S. Rishikanth, S.R. Poonkuzhali, Ramadoss K S Rangasamy College of Technology Department of Information Technology Tiruchengode India

ISBN: (纸本)9798331521394

Smart Tunnel Traffic Control System Using IoT and Machine Learning' is designed to offer safety and effective management of traffic in single-lane tunnels. The conventional tunnel system does not prohibit big vehicles from entering the tunnel through either end, which may lead to congestion and increase the likelihood of accidents. The designed system uses an ESP32-CAM module to sense the images of the vehicles in real time at any entry point of the tunnel. Using a CNN model, the system classifies incoming vehicles by size, and if a large vehicle is detected, it triggers a red 'STOP' signal and message at the opposite end. In this way, only one large vehicle occupies the tunnel at any time. The system further calculates the transit time in terms of the length of the tunnel and updates it dynamically to handle incoming vehicles. As soon as the tunnel becomes free, a green 'GO' signal allows the next vehicle to move forward. Keywords: loT, ESP32-CAM, CNN, traffic, tunnel safety, automation, image processing, classification, monitoring, accident prevention, congestion, ML. © 2025 IEEE.

关键词： Image analysis

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Adaptive visualizations for enhanced data understanding and interpretation 21

Adaptive visualizations for enhanced data understanding and ...

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29th ACM conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Amyrotos, Christos UCLan Cyprus Pyla Cyprus

ISBN: (纸本)9781450383660

In a data driven economy where data volume and dimensions are explosively increasing, businesses rely on business intelligence and analytics (BI&A) platforms for analysing their data and coming to beneficial decisions. With the ever-growing generation of data, the process of data analysis is becoming more complicated for the business users, as the exploration of more demanding use cases increases. While the existing BI&A platforms provide myriads of data visualizations that support data exploration, none of those account for the user's individual differences, needs or requirements, and thus may hinder the user's understanding of visual data and consequently their decision-making processes. This work embarks on an interdisciplinary endeavour to introduce a human-centred adaptive data visualizations framework in the context of business, as the core of an adaptive data analytics platform, that aims to enhance the business user's decision making by increasing her understanding of data. The framework is built using a multi-dimensional human-centred user model that goes beyond traditional user characteristics and accounts for cognitive factors, domain expertise and experience and factors related to the business context i.e., data, visualizations and tasks;a data visualization engine that will recommend to the unique-user the best-fit data visualizations based on the abovementioned user model;and an intelligent data analytics component that enhances the efficiency and effectiveness of the data exploration process by leveraging user interactions during the explorations to further inform the user model on the user's expertise and experience. © 2021 Owner/Author.

关键词： data visualization

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A Comparative and exploration analysis of Cryptographic Algorithms for Securing Digital Device

A Comparative and Exploration Analysis of Cryptographic Algo...

引用

International conference on Contemporary Computing and Informatics (IC3I)

作者： Likith Sai Reddy Syamala v N K D Sai Chandana Kavuluri Sai Sneha Dasa D.S Lakshman Munish Kumar Sandeep Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The term ‘Cryptography’, derived from ‘crypt’ meaning hidden and ‘graphy’ meaning writing, is vital for data encryption, ensuring confidentiality, integrity, and availability (CIA). Encryption transforms plain text into cipher text, securing data at rest and in transit. Confidentiality is upheld by encryption, rendering data unreadable without the decryption key. Encryption also maintains integrity by preventing unauthorized data alterations. It operates at the sender's end, with decryption at the receivers. This paper focuses on password security and modern encryption algorithms for message protection. Securing sensitive data in digital communication relies heavily on password practices. Symmetric key algorithms like AES and DES, along with asymmetric ones such as RSA and Elliptic Curve Cryptography (ECC) are explored. A thorough analysis of their strengths and weaknesses is conducted, considering their applicability in contemporary digital security contexts.

关键词： Computers Quantum computing Passwords Transforms Writing Elliptic curve cryptography Encryption Cryptography Faces Resilience

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Predictive analysis of Kidney Diseases with Artificial Intelligence and data Science

Predictive Analysis of Kidney Diseases with Artificial Intel...

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2025 International conference on visual Analytics and data visualization, ICvADv 2025

作者： Eswar, G. Hariharan, C. Lavein, S. Akilandeswari, v. Velammal College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331521394

In the Field of Medical diagnosis, nowadays there are many new methods are getting introduced each day and also gets advanced by many people around the world. The Kidney is one of the most vital organ of a Human body that helps a lot by filtering blood and throws the waste toxic contents through excretory organs. But the food habits and other bad habits can affect the process of Kidney and further even affects kidney. As a result our kidney can also suffer from various disease. Most vital Kidney diseases can include Chronic Kidney Disease(CKD), Diabetic Nephropathy at the 2nd stage of Diabetes, and still many more. In the process of predicting of these diseases, we can use machine learning algorithms like Random Forest with Decision Tree and Supervised Learning algorithms we can train a model that can use the previous datasets to clarify if the new person is diagnosed with the particular Kidney disease. The Random Forest algorithm provides around 90% accuracy that is better in prediction of results for the collected information. Using these techniques we can detect the problem at early stage which can give a greater chance to prevent it from getting worse. © 2025 IEEE.

关键词： Supervised learning

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Smart City Applications: A Patent Landscape exploration 8

Smart City Applications: A Patent Landscape Exploration

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8th International conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Kalleya, Calista Purnomo, Agung Madyatmadja, Evaristus Didik Meiryani Karmagatri, Mulyani Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Management Information System Department Bina Nusantara University Jakarta11480 Indonesia Accounting Department School of Accounting Bina Nusantara University Jakarta11480 Indonesia

Smart cities thrive on supporting digital technology innovation of applications and patent protection in the entrepreneurial ecosystem. This research aimed to explore the significant trends in patents related to smart city applications worldwide. The researcher conducted a patent landscape analysis using 22,655 patent materials from the *** database. The study's findings indicate that the number of patents for applications related to smart cities is rising annually. There is an opportunity to use innovations in smart city applications without having to pay royalties by using 1,443 patents that have been discontinued. With seven companies, American corporations have most of the patents for applications in smart cities. The most productive individual investor was Kim Soenghun from Korea, with 946 patents. The electricity (H), physics (G), and new technological developments (Y) patent sections dominate patents related to smart city applications. There were 11,903 simple family patents for smart city applications in seven country jurisdictions;the United States occupied the highest region. © 2023 The Authors. Published by Elsevier B.v.

关键词： Application Entrepreneurship Innovation Patens Smart City

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