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2nd International Conference on Artificial Intelligence and IoT (AIIoT 2024)
<!Doctype html> <html lang="en"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="id=edge"> <title>2nd International Conference on Artificial Intelligence and IoT (AIIoT 2024) </title> <link rel="icon" type="image/png" sizes="96x96" href="images/logo.png"> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css"> <link rel="stylesheet" href="bootstrap.css"> <link rel="stylesheet" href="style.css"> </head> <body id="home"> <nav class="navbar navbar-expand-md bg-dark navbar-primary fixed-top"> <div class="container"> <button class="navbar-toggler" data-toggle="collapse" data-target="#navbarCollapse"><span class="navbar-toggler-icon"></span>Menu</button> <a class="navbar-brand"><img src="images/logo.png" width="100px" height="40px" type="image/png" alt="brand-logo"></a> <div class="collapse navbar-collapse" id="navbarCollapse"> <ul class="navbar-nav ml-auto"> <li class="nav-item"> <a href="index" class="nav-link">Home</a> </li> <li class="nav-item"> <a href="papersubmission" class="nav-link">Paper Submission</a> </li> <li class="nav-item"> <a href="committee" class="nav-link">Program Committee</a> </li> <li class="nav-item"> <a href="papers" class="nav-link active">Accepted Papers</a> </li> <li class="nav-item"> <a href="contact" class="nav-link">Contact Us</a> </li> </ul> </div> </div> </nav> <!-- Home Page /--> <header id="home-section"> <div class="dark-overlay"> <div class="home-inner"> <div class="container"> <div class="row"> <div class="col-xs-12 col-sm-10 col-md-10 col-lg-8 d-block bg-primary"> <h1 class="text-light p-2">2<sup>nd</sup> International Conference on Artificial Intelligence and IoT (AIIoT 2024)</h1> <h4 align="center">September 13- 14, 2024, Virtual Conference</h4> </div> </div> </div> </div> </div> </header> <!-- Scope section /--> <!-- Scope & Topic Section /--> <section id="scope-section" class="bg-light text-dark py-5"> <div class="container"> <div class="row"> <div class="col-md8 text-justify"> <h3 class="text-secondary2 text-center display4">Accepted Papers</h3><br> <h6 style="color:black;font-family:classic wide,sans-cserif;font-size:20px"><b>Design of Proprietary Frameworks for Neural Models: Methodology and Best Practices</b></h6> <p style="color:black;text-align:justify;font-size: 15px;">Jos茅 Gabriel Carrasco Ramirez, CEO at Quarks Advantage, Jersey City, New Jersey. United States of America </p> <h6 style="color:black;font-family:classic wide,sans-serif;"><b>ABSTRACT</b></h6> <p style="color:black;text-align:justify;">The creation of proprietary frameworks for the development of neural models is essential to meet specific needs that generic frameworks cannot address. This article examines the key stages in the design of these frameworks and offers best practices for their effective implementation. It explores everything from needs identification and resource assessment to architectural design and implementation. Additionally, it emphasizes the importance of user-centered design and continuous evaluation to ensure the framework's usability and adaptability to changing needs. </p> <h6 style="color:black;font-family:classic wide,sans-serif;"><b>Keywords</b></h6> <p style="color:black;text-align:justify;">Proprietary frameworks, neural models, artificial intelligence, framework design, model optimization, user-centered design, continuous evaluation, scalability, performance optimization, data management, model training, regulatory compliance, explainable AI (XAI), agile methodology, security and privacy. </p> <br> <h6 style="color:black;font-family:classic wide,sans-cserif;font-size:20px"><b>Multi-classification of CAD Entities: Leveraging the Entity-as-node Approach with Graph Neural Networks </b></h6> <p style="color:black;text-align:justify;font-size: 15px;">Sheela Raju Kurupathi<sup>1</sup>, Park Dongryul<sup>1</sup>, Sebastian Bosse<sup>1</sup>, and Peter Eisert<sup>1, 2</sup>, <sup>1</sup>Fraunhofer Heinrich Hertz Institute (HHI), <sup>2</sup>Humboldt-Universit篓at zu Berlin </p> <h6 style="color:black;font-family:classic wide,sans-serif;"><b>ABSTRACT</b></h6> <p style="color:black;text-align:justify;">The construction industry faces challenges in extracting and interpreting semantic information from CAD floor plans and related data. Graph Neural Networks (GNNs) have emerged as a potential solution, preserving the structural integrity of CAD drawings without rasterization. Accurate identification of structural symbols, such as walls, doors, windows, etc. is vital for generalizing floor plans. This paper investigates GNN methods to enhance the classification of these symbols in CAD floorplans, proposing an entity-as-node graph representation. We evaluate various preprocessing strategies and GNN architectures, including Graph Attention Networks (GAT), GATv2, Generalized Aggregation Networks (GEN), Principal Neighborhood Aggregation (PNA), and Unified Message Passing (UniMP) on the CubiCasa5K dataset. Our results show that these methods significantly outperform current state-of-the-art approaches, demonstrating their effectiveness in CAD floor plan entity classification. </p> <h6 style="color:black;font-family:classic wide,sans-serif;"><b>Keywords</b></h6> <p style="color:black;text-align:justify;">BIM, CAD, Floor Plans, GNN, Entity-as-Node, Multi-Classification. </p> <br> </div> </div> </div> </section> <!-- Footer Section /--> <section id="footer-section" class="bg-dark text-light py-3 text-center"> <div class=""> <div class="container"> <div class="row"> <div class="card-body col-sm-6 col-md-4"> <h6>Contact Us</h6> <p><a href="mailto:aiiot@aiiot2024.org" class="text-white">aiiot@aiiot2024.org</a></p> </div> <div class="card-body col-sm-6 col-md-4 text-center"> <br> </div> <div class="card-body col-xs-6 col-sm-6 col-md-4 col-lg-3 col-xl-2 text-center"> <h6 class="header-h6">Follow Us</h6> <div> <a href="https://www.facebook.com/AIRCCPC" target="blank" class="fa fa-facebook" aria-hidden="true"></a> <a href="https://twitter.com/AIRCCFP" target="blank" class="fa fa-twitter" aria-hidden="true"></a> <a href="https://www.youtube.com/channel/UCzkuYvuKuNCIc3jbE52IeZg" target="blank" class="fa fa-youtube-play" aria-hidden="true"></a> </div> </div> </div> </div> </div> </section> <section class="copyright bg-dark text-light text-center py-3"> <div class="container-fluid"> <p>Copyright © AIIoT 2024</p> </div> </section> <script src="jquery.min.js"></script> <script src="popper.min.js"></script> <script src="bootstrap.min.js"></script> </body> </html>