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RouteRover: AI-Enabled Traffic Congestion Prediction and Route Optimization for Indian Urbanites

RouteRover: AI-Enabled Traffic Congestion Prediction and Rou...

引用

Recent Advances in Science and Engineering Technology (ICRASET), International conference on

作者： S Naveen G Sanketh M G Jayanthi M P Shreya Prashanth Kannadaguli Artificial Intelligence and Machine Learning Cambridge Institute of technology Bengaluru India Computer science and engineering Cambridge Institute of technology Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Here in this paper, a model that has been proposed for predicting and visually representing congestion of traffic using machine learning as well as deep learning. This initiative with the purpose of examining traffic data to obtain insights for traffic regulation and planning. The process initiates with an initial exploration of data through an Exploratory data analysis (EDA) and to perceive the distribution and patterns within the dataset. Descriptive statistics, correlation analysis, and visual aids are employed for this purpose. Following the EDA phase, tactics for manifold learning are utilized to simplify the dataset while retaining crucial information. Identifying significant features influencing the prediction of traffic volume is facilitated by methods for feature importance and selection. The obtained features are rigorously evaluated to comprehend traffic patterns and traffic jam levels. visual representations capture the distribution of traffic volume based on factors like time, day, and month, assisting in pinpointing congestion hotspots and optimizing traffic flow. This project underscores the powerful impact of data analysis and feature extraction in offering practical insights for traffic regulation strategies.

关键词： visualization data analysis Scalability Transportation Traffic control Predictive models Routing Regulation Real-time systems Traffic congestion

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Self-Supervised visual Representation Learning Framework for Road Sign Classification 1

Self-Supervised Visual Representation Learning Framework for...

引用

2023 1st DMIHER International conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Dsilva, Godson Michael Ashok, Vinayak Finolex Academy of Management and Technology Information Technology Department Ratnagiri India

ISBN: (纸本)9798350338423

In the landscape of machine learning, the dominance of supervised learning in crafting specialized models for specific tasks is evident, yet its confinement in terms of broader generalization has underscored the quest for more versatile models capable of acquiring multiple skills without an insurmountable reliance on labelled data. A pivotal avenue toward this goal lies in self-supervised learning (SSL), which promises to instill foundational knowledge and a semblance of common sense into AI systems. This investigation undertakes a comprehensive exploration of diverse SSL strategies, elucidating their inherent constraints on the road sign detection and classification usecase. Through an intricate comparative analysis spotlighting Context Autoencoders (CAE) and MoCoV3 - two prominent state-of-the-art methodologies leveraging masked image modelling and contrastive learning, respectively - we unravel their distinct attributes and limitations. Furthermore, we introduce an innovative end-to-end framework meticulously tailored for self-supervised road sign classification. Our roadmap encompasses the infusion of domain-specific expertise in road signs into this framework, followed by rigorous benchmarking against state-of-the-art SSL models across diverse road sign datasets, employing a spectrum of evaluation metrics. This study emerges as a stepping stone, propelling AI closer to comprehesive intelligence by harnessing the potency of self-supervised paradigms. © 2023 IEEE.

关键词： Supervised learning

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exploration of Cultural IP Image and Common Pattern Gene Extraction in Virtual Reality Design Interaction 16th

Exploration of Cultural IP Image and Common Pattern Gene Ext...

引用

16th International conference on Virtual, Augmented and Mixed Reality (VAMR)

作者： Zhou, Junyu Chen, Ruiyang Nanjing Normal Univ Dept Visual Commun Nanjing 210098 Peoples R China Nanjing Normal Univ Art Educ Major Nanjing 210098 Peoples R China

ISBN: (纸本)9783031610400;9783031610417

This paper first studies the origin and flow of IP images from the interactive perspective of design, explores the definition and rules of IP in the form of knowledge graph based on semiotics, and puts this problem into the background of the current wave of AI and virtual reality, and raises the following questions: - The definition of IP in virtual reality and the original IP in the network? What is the way forward? Based on the AI data era background of IP business image for induction automation design processor, to achieve better IP cultural communication and exchange. The author selected the female ethnic characteristic clothing of Hui 'an area of Fujian Province as the representative object of design. In this realization part, the article first takes Hui 'an women's ethnic clothing as an example to explore the characteristics of ethnic clothing induction methods - structure multi-modal form and cross-comparison analysis of horizontal multi-cultural pattern design and extract the elements. Then, according to the formula points summarized, the virtual IP image is designed with the characteristic ethnic dress of women in Hui 'an area of Fujian Province, China. According to the cultural gene and regional characteristics contained in the regional ethnic image, the pattern factors are decomposed reconstructed, and applied to the IP image design under the meta-structure virtual reality technology. On this basis, the VR system of the minority cultural IP generator is built. It allows users to generate IP images of featured nationalities in a more immersive environment through shapes, colors, lines, etc., summarized by patterns. Finally, the team tested the VR system. According to the five aspects of "whether the generated IP image is more vivid/whether it can integrate national characteristics/whether it is more immersive/whether it is more memorable/whether it is easier to understand", a total of 20 members participated in the evaluation. In the answer, we finally fin

关键词： IP image design National characteristic pattern finishing Virtual reality interaction

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visualization Techniques for the Design and analysis of Dynamic Programming Algorithms

Visualization Techniques for the Design and Analysis of Dyna...

引用

International conference on Information visualisation (iv)

作者： Ying Zhu Creative Media Industries Institute Georgia State University Atlanta USA

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

Dynamic programming is one of the most challenging algorithm design techniques for computer programmers. Students frequently struggle with dynamic programming algorithms in data Structures and Algorithms courses, and software developers often find it difficult to solve dynamic programming problems during job interviews. To address this issue, we have developed a new visualization technique to help students better understand, analyze, and design dynamic programming algorithms. Previous dynamic programming visualizations have primarily focused on illustrating the execution sequence of programs. Our visualizations are designed to help students recognize the common logical patterns shared by a diverse set of problems. Additionally, students can use these visualizations as visual aids to assist them in designing dynamic programming algorithms.

关键词： visualization Heuristic algorithms Software algorithms Education data visualization data structures Software Dynamic programming Pattern recognition Interviews

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Faces in the Fog: A Deep Dive into Face De-Identification Techniques and Their Comparative analysis

Faces in the Fog: A Deep Dive into Face De-Identification Te...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Shaonti Goswami Sudipty Konica Paul Sanyam Jawlia Anurag Goel Department of CSE Delhi Technological University New Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Face de-identification have drawn a lot of attention due to growing privacy concerns and the growing requirement for anonymization in visual data. This review suggests a systematic exploration of current face de-identification methods, emphasizing three main deep learning-based methods: diffusion models, latent Variable based transformations, and GAN-based models. The in-depth analysis of this literature provides a comparison of how well each strategies protect privacy while retaining data utility by focusing on the advantages and disadvantages of them. Our analysis delves deeper into the benefits and limitations of these models, focusing on important issues such as adversarial robustness, systematic biases, and trade-offs between realistic image generation and privacy preservation. This comprehensive overview synthesizes data from several studies, identifies important gaps in existing approaches and provides guidance to researchers and industry practitioners directing to more reliable and ethical de-identification frameworks.

关键词： Deep learning data privacy Privacy visualization Systematics Reviews Focusing Diffusion models Protection Faces

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Predicting Bike-Sharing Demand: A Machine Learning Approach for Urban Mobility analysis 14

Predicting Bike-Sharing Demand: A Machine Learning Approach ...

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14th International conference on Information and Communication Technology Convergence, ICTC 2023

作者： Shanto, Md Javed Ahmed Akter, Rubina Kim, Dong-Seong Jun, Taesoo Kumoh National Institute of Technology Department of It Convergence Engineering Gumi39177 Korea Republic of Kumoh National Institute of Technology Ict Convergence Research Center Gumi39177 Korea Republic of

ISBN: (纸本)9798350313277

Due to the increasing environmental pollution and the greenhouse effect, individuals are increasingly inclined towards adopting environmentally friendly modes of transportation, such as electric cars and eco-friendly machinery. The bicycle is considered one of the most environmentally friendly modes of transportation due to its low cost and minimal environmental impact. The use of bicycles is steadily increasing daily, especially in urban areas. This study seeks to investigate the level of demand for bike-sharing services in urban areas. It will explore various feature engineering methods, thoroughly analyze ensemble machine learning techniques, and employ artificial intelligence for time analysis. © 2023 IEEE.

关键词： Artificial intelligence bike-sharing machine learning

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Enabling Activity Based Intelligence with visual Analytics

Enabling Activity Based Intelligence with Visual Analytics

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International conference on Military Communications and Information Systems (ICMCIS)

作者： Valérie Lavigne Alexandre Bergeron-Guyard Valcartier Research Centre Defence Research and Development Canada Québec QC Canada

ThornViz is a visual analytics tool presenting data along multiple perspectives for discovering the unexpected and performing Activity Based Intelligence (ABI) analysis. It leverages a flexible data model along with a graph database to support ABI principles through interactive visual exploration of intelligence data including dynamic filtering and social network analysis techniques. ThornViz was applied to explore adversarial communication network data, to investigate crypto-currency transactions for suspicious financial activity, as well as to look for potential collaborations between NATO scientific activities, which demonstrated its multi-domain support. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY — the ICMCIS, held in Skopje, North Macedonia, 16—;17 May 2023

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visualisation Tool to Support Fraud Detection 25

Visualisation Tool to Support Fraud Detection

引用

25th International conference Information visualisation (iv) - AI and visual Analytics and data Science

作者： Silva, Pedro Macas, Catarina Polisciuc, Evgheni Machado, Penousal Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9781665438278

Automatic fraud detection and prevention are challenging problems that have attracted the attention of many researchers in academia and industry. Over the last few years, many improvements have been achieved, especially in predictive models based on Machine Learning. However, a considerable amount of these models only provide a prediction score and a short explanation which may not be enough to make informed decisions. This paper presents a visualization tool that aims to assist fraud analysts in making informed decisions and increase their effectiveness in the detection of fraud. To this end, we designed three visualisation models that apply state of the art techniques to support the analysis of fraudulent transactions. To demonstrate the analytic capabilities and benefits of the proposed tool, we discussed a real use case scenario and conducted user testing with real fraud analysts. Through the feedback from both studies, we were able to conclude that the tool is an asset to facilitate the detection of suspicious events as well to improve the analysis times of the fraud analysts' work process.

关键词： Information visualization data Analytics Fraud

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A Personalized AI Assistant For Intuition-Driven visual explorations

A Personalized AI Assistant For Intuition-Driven Visual Expl...

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IEEE International conference on e-Science and Grid Computing

作者： James Hammer Tanner Hobson David Pugmire Scott Klasky Kenneth Moreland Jian Huang Dept. of EECS University of Tennessee Knoxville Knoxville USA CSMD Division Oak Ridge National Laboratory Oak Ridge USA

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Understanding the time-varying magnetic field within a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the analysis and visualization of magnetic field lines in fusion devices. Current state-of-the-art relies on manually and iteratively generating Poincaré plots from simulation data. Using Poincaré plots in deep analysis is very time consuming because Poincaré plots can be very computationally expensive, especially for a time-varying simulation with thousands of time steps. Further, the visualization results are so complex that only expert users know how to explore, interpret, and control. In addition, collaboration is hampered due to the high barrier to entry. To this end, we contribute Fugent, a reinforcement learning-based agent capable of recommending and evaluating the importance of exploration regions based on training data captured from historic expert user usage. Using Fugent, we show that important regions can be identified and recommended for further exploration. Fugent is open source.

关键词： Training visualization data visualization Training data Prototypes User experience Robustness

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EMNLP 2022 - 2022 conference on Empirical Methods in Natural Language Processing, Proceedings of the Demonstrations Session

EMNLP 2022 - 2022 Conference on Empirical Methods in Natural...

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2022 conference on Empirical Methods in Natural Language Processing, EMNLP 2022

ISBN: (纸本)9781959429418

The proceedings contain 42 papers. The topics discussed include: CogKTR: a knowledge-enhanced text representation toolkit for natural language understanding;LM-Debugger: an interactive tool for inspection and intervention in transformer-based language models;EasyNLP: a comprehensive and easy-to-use toolkit for natural language processing;an explainable toolbox for evaluating pre-trained vision-language models;ELEVANT: a fully automatic fine-grained entity linking evaluation and analysis tool;a pipeline for generating, annotating and employing synthetic data for real world question answering;DeepKE: a deep learning based knowledge extraction toolkit for knowledge base population;evaluate & evaluation on the hub: better best practices for data and model measurements;and KeywordScape: visual document exploration using contextualized keyword embeddings.

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