ROBOT NAVIGATION IN INDOOR ENVIRONMENT THROUGH SELF LEARNING by Chandan Kalita, Kishore Kashyap, Mirzanur Rahman, Satyajit Sarma, Parvez Aziz Boruah

“…This evaluation is crucial as it helps assess the model's adaptability and effectiveness in navigating diverse indoor spaces. The results are presented through score graphs, showcasing how the model's performance varies across different environments and object settings. …”
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Supporting statistical literacy skills for prospective teachers: A learning trajectory used South Sumatra local wisdom context through hybrid learning by Rahma Siska Utari, Ratu Ilma Indra Putri, Zulkardi Zulkardi, Hapizah Hapizah

“…The results indicate that the designed LT guided students through five activities that support statistical literacy: reading and interpreting data tables using statistical situations with local wisdom from South Sumatra as context, interpreting graphs, analyzing and reflecting, exploring outliers, and making conclusions and presenting findings. …”
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Intelligent Infrastructure Facilitating Sequence Recommendation for Cybersecurity Education Systems by Eric Brown, Douglas Talbert

“…In this poster, we present the early building blocks of the system involving the use of federated knowledge graphs as a trusted knowledge source capable of learning from “less restricted” models such as large language models. …”
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Reliability and Quality of Complex Systems by E.A. Voronin

Machine learning message-passing for the scalable decoding of QLDPC codes by Arshpreet Singh Maan, Alexandru Paler

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A compression method of educational material for special disciplines of technical University by Konstantin P. Baslyk

“…This principle makes it possible to implement an “open architecture” of the method: there is the possibility of prompt correction or expansion of the content of discipline without significant increase in time costs for the presentation. An interpretation of the proposed method as the procedure of graphs theory is given. …”
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Learning Cohesive Behaviors Across Scales for Semi-Cooperative Agents by Reid Sawtell, Sarah Kitchen, Timothy Aris, Christopher McGroarty

“…Scalability is of particular importance when many individual opponents are required to act cohesively over long distances, but this makes learning more difficult. This paper presents a novel architecture applying graph convolutional layers in a U-net with custom pooling operators in order to achieve learning across scales. …”
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A meta-learning approach for selectivity prediction in asymmetric catalysis by Sukriti Singh, José Miguel Hernández-Lobato

“…This meta-learning model consistently provides significant performance improvement over other popular ML methods such as random forests and graph neural networks. …”
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User preference modeling for movie recommendations based on deep learning by Yang Gao, Hong Zheng, Haonan Cui

“…In this study, we employ Artificial Intelligence (AI), graph-based techniques, and text mining to accurately estimate user preferences. …”
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Optical quantum sensing for agnostic environments via deep learning by Zeqiao Zhou, Yuxuan Du, Xu-Fei Yin, Shanshan Zhao, Xinmei Tian, Dacheng Tao

“…However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical applications. Addressing this limitation, we introduce an innovative deep-learning-based quantum sensing scheme (DQS), enabling optical quantum sensors to attain HL in agnostic environments. …”
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Multi-target property prediction and optimization using latent spaces of generative model by Anirudh Jain, Markus Heinonen, Heikki Käsnänen, Julius Sipilä, Samuel Kaski

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Clinically validated graphical approaches identify hepatosplenic multimorbidity in individuals at risk of schistosomiasis by Yin-Cong Zhi, Simon Mpooya, Narcis B. Kabatereine, Betty Nabatte, Christopher K. Opio, Goylette F. Chami

“…Graph learning algorithms with statistical assumptions, e.g. graphical lasso, enabled accurate and clinically valid multimorbidity representations. …”
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Identification of source and sink points of population flow based on POI-KG embedding by Yanhao Li, Rui Li, Xinrui Liu, Bosen Li

“…Weakly supervised learning is applied using ISS-KGE to identify these types within the urban grid. …”
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Smartphone-Based Application “quizizz†as a Learning Media by Ramadhan Prasetya Wibawa, Rohana Intan Astuti, Bayu Aji Pangestu

“…Data analysis technique used is an interactive method, including the process of collecting data, reducing data (compiling data in patterns, categories and specific issues), presenting data (compiling data in the form of matrices, graphs, networks, certain charts) and drawing conclusions. …”
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Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence by Na Song, Jing Yang, Xuemei Fu, Xiangli Yang, Ying Xie, Shiping Wang

“…To tackle the challenges in CPSI, we present a multi-level feature learning framework for semi-supervised classification tasks. …”
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Point Cloud Deep Learning Methods for Particle Shower Reconstruction in the DHCAL by Borysova Maryna, Bressler Shikma, Gross Eilam, Kakati Nilotpal, Zavazieva Darina

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Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics by Pankaj Lal Sahu, Sukumaran Sandeep, Hariprasad Kodamana

“…However, thorough evaluations are essential before considering MLWP models as replacements for NWP models. This study presents a comprehensive evaluation of four leading MLWP models—GraphCast, PanguWeather, Aurora, and FourCastNet—against observations and three state‐of‐the‐art NWP models in predicting tropical cyclones (TCs) across all tropical ocean basins. …”
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Scattering-Based Machine Learning Algorithms for Momentum Estimation in Muon Tomography by Florian Bury, Maxime Lagrange

“…An alternative consists of leveraging information on scattering withstood through a known medium. We present a comprehensive study of diverse machine-learning algorithms for this regression task, from classical feature engineering with a fully connected network to more advanced architectures such as recurrent and graph neural networks and transformers. …”
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Weirdnodes: centrality based anomaly detection on temporal networks for the anti-financial crime domain by Salvatore Vilella, Arthur Capozzi, Marco Fornasiero, Dario Moncalvo, Valeria Ricci, Silvia Ronchiadin, Giancarlo Ruffo

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Applications of density functional theory and machine learning in nanomaterials: A review by Nangamso Nathaniel Nyangiwe

“…The review concludes by discussing key advancements, such as those of machine learning interatomic potentials, graph-based models for structure property mapping and generative AI for materials design.…”
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