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22nd International Learning and Technology conference, L and T 2025

作者： Amgad, Mahmoud Shamoon, Bahaa Mekhemar, Hagar Mostafa, Rodeen Tamer, Toqa Mohamed, Habiba Fawzi, Sahar School of Information Technology and Computer Science Giza12588 Egypt

ISBN: (纸本)9798331532215

Healthcare data management faces several challenges, including errors from manual data entry, variations in data quality, and an increasing need for advanced tools to efficiently handle large datasets. This paper introduces Data-Care, an innovative application designed to automate medical record digitization by leveraging advanced technologies such as Large Language Models (LLMs), Optical Character Recognition (OCR), and NoSQL databases. Together, these technologies significantly reduce the need for manual input, minimizing errors and enhancing data accuracy. A key feature of Data-Care is its data cleansing module, which resolves issues like data inconsistencies and missing values, ensuring the integrity and accuracy of the dataset. Additionally, the application provides a powerful offline visualization tool, enabling healthcare professionals to generate comprehensive reports that support informed decision-making and streamline workflows across various healthcare processes. Extensive performance tests demonstrated that the integration of LLMs and OCR in Data-Care achieved accuracy rates between 76% and 93%, depending on the quality of the input data. These results underscore Data-Care's capacity to improve both the speed and accuracy of data entry, offering a robust solution for the digitization and management of healthcare data. © 2025 ieee.

关键词： Data accuracy

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On Virtual Pointing Rays and Motion Artefacts 32

On Virtual Pointing Rays and Motion Artefacts

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32nd ieee conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Rendle, Gareth Kreskowski, Adrian Froehlich, Bernd Bauhaus-Universität Weimar Virtual Reality and Visualization Research Group Weimar Germany

ISBN: (纸本)9798331536459

Virtual pointing rays are a commonly employed interaction metaphor allowing users of mixed reality applications to select out-of-reach virtual objects. However, fast hand movements can cause the visual representation of the ray to move quickly across the visual field, leading to motion artefacts that cause multiple copies of the ray to appear in discrete locations. In this work, we conduct a user study to investigate whether the conditions for motion artefact occurrence arise during a selection task that participants undertake with a pointing ray. We analyse the recorded gaze and ray movement data from the study to show that pointing actions create the conditions necessary for motion artefacts to appear. In addition, we propose a motion blur effect that we apply to the pointing ray with the aim of mitigating motion artefacts. The blur effect is evaluated in the aforementioned user study and is found to improve the perceived smoothness of ray selection, without affecting selection performance. © 2025 ieee.

关键词： Virtual environments

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Neural Network Interpretability with Layer-Wise Relevance Propagation: Novel Techniques for Neuron Selection and visualization 15

Neural Network Interpretability with Layer-Wise Relevance Pr...

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15th ieee Annual Computing and Communication Workshop and conference, CCWC 2025

作者： Bhati, Deepshikha Neha, Fnu Amiruzzaman, Md. Guercio, Angela Shukla, Deepak Kumar Ward, Ben Kent State University KentOH United States West Chester University West ChesterPA United States Rutgers University NewarkNJ United States

ISBN: (纸本)9798331507695

Interpreting complex neural networks is crucial for understanding their decision-making processes, particularly in applications where transparency and accountability are essential. This proposed method addresses this need by focusing on Layer-wise Relevance Propagation (LRP), a technique used in explain-able artificial intelligence (XAI) to attribute neural network outputs to input features through backpropagated relevance scores. Existing LRP methods often struggle with precision in evaluating individual neuron contributions. To overcome this limitation, we present a novel approach that improves the parsing of selected neurons during LRP backward propagation, using the Visual Geometry Group 16 (VGG16) architecture as a case study. Our method creates neural network graphs to highlight critical paths and visualizes these paths with heatmaps, optimizing neuron selection through accuracy metrics like Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE). Additionally, we utilize a deconvolutional visualization technique to reconstruct feature maps, offering a comprehensive view of the network's inner workings. Extensive experiments demonstrate that our approach enhances interpretability and supports the development of more transparent artificial in-telligence (AI) systems for computer vision applications. This advancement has the potential to improve the trustworthiness of AI models in real-world machine vision applications, thereby increasing their reliability and effectiveness. © 2025 ieee.

关键词： Mean square error

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InfoTrace: A System for information Campaign Source Tracing and Analysis on Social Media 24

InfoTrace: A System for Information Campaign Source Tracing ...

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24th ACM/ieee Joint conference on Digital Libraries, JCDL 2024

作者： Cheng, Linus Xin Wei Chin, Daniel Wai Kit Toh, Shaun Mu, Wenchuan Ong, James Kay Liang Choo, Kenny Tsu Wei Lee, Roy Ka-Wei Lim, Kwan Hui Singapore University of Technology and Design Singapore Singapore

ISBN: (纸本)9798400710933

Social media platforms have become integral to daily life and serve as powerful channels for influencing public opinion, spanning applications from viral marketing to disinformation campaigns. While these platforms amplify the reach of marketing efforts, they also present significant risks when used for spreading misinformation via campaigns. Thus, it is crucial to understand such information campaigns by identifying the source, the different discussion topics within this campaign and how they change over time. Towards addressing this problem, we develop and present an interactive system for information campaign source tracing and analysis. This includes a demonstration and visualization of the main components of a information campaign, including the source of the campaign via explicit and implicit links, the discussion topics/clusters, their content, and how these evolve over different time periods. © 2024 Copyright held by the owner/author(s).

关键词： Tweets

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Design and Development of a Dashboard System to Enhance Monitoring in People's Business Credit

Design and Development of a Dashboard System to Enhance Moni...

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2025 International conference on Advancement in Data Science, E-learning and information System, ICADEIS 2025

作者： Kusumayuda, Made Darma Arin, Ikrar Adinata Endrianto, Wendy School of Information Systems and Accounting Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia School of Accounting Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331513320

The Micro, Small, and Medium Enterprises (MSME) sector is perpetually evolving through the application of information technology. Nonetheless, significant obstacles in overseeing individuals' business credit encompass disparate data reporting formats among distributors, manual processing impediments, and restricted real-time monitoring functionalities. Current dashboard solutions provide fundamental visualization tools but are deficient in standardized data processing and real-time monitoring capabilities for credit distribution. This study tackles these problems by creating a prototype dashboard on a desktop platform that facilitates automated data processing and standardized reporting formats. The dashboard is a visualization tool that succinctly describes intricate datasets through interactive graphics. This technology is explicitly crafted to aid state institutions in overseeing and evaluating the business credit allocation process executed by each active distributor, as well as to analyze the conditions and scenarios displayed on the dashboard for timely decisionmaking. The indicator variables comprise total ceiling, the quantity of new borrowers per scheme, and national debtors. This research employs the double diamond design technique to create a prototype dashboard that fulfills the needs of three key stakeholders: policy analysis experts who intend to utilize the dashboard as a tool. A comparative examination with current manual systems indicates that the dashboard attained a 90% decrease in data processing time and 98% accuracy in automated data translation. The results indicated increased oversight of individuals' company credit allocation via standardized data processing, augmented decision-making abilities, and a substantial decrease in the manual handling of raw data provided by the distributors. © 2025 ieee.

关键词： Decision making

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Design of Farmland Meteorological visualization Management System Based on Digital Twins

Design of Farmland Meteorological Visualization Management S...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Xu, Yong Lv, Zhenqiang Chen, Zhiling Chan, Choon Kit Luo, Kun School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Inti International University Faculty of Engineering and Quantity Surveying Negeri Sembilan Nilai Malaysia Inti International University Faculty of Education and Liberal Arts Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9798331504205

To precisely monitor farmland conditions, we need to mitigate the risk of crop yield reduction due to natural disasters and other factors, and enhance the accuracy of agricultural production decisions, an agricultural meteorological environment monitoring system based on digital twin technology has been developed. Unlike traditional meteorological monitoring systems, this design employs digital twin technology to construct a twin model that virtually replicates the physical farmland environment. Meteorological monitoring equipment collects farmland data, which is then uploaded to a virtual visualization interface where real-time remote monitoring, anomaly alerts, and weather forecasts are provided. Experimental results indicate that this system effectively collects and uploads farmland data, enables real-time remote monitoring, and issues timely alerts for abnormal meteorological conditions, thereby offering theoretical guidance and technical support for the advancement of smart agriculture. © 2025 ieee.

关键词： data collection Digital twin visualization weather forecast weather monitoring

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Silent Speech Interfaces: Non-Invasive Neuromuscular Signal Processing for Assistive Communication 4

Silent Speech Interfaces: Non-Invasive Neuromuscular Signal ...

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4th ieee International conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Khan, Sallar Ali, Khawaja Usman Qadri, Ali Raza Ali Rizvi, Maisam Technological University Dublin School of Informatics and Cybersecurity Dublin Ireland Sir Syed University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan

ISBN: (纸本)9798331518882

Our research demonstrates a discrete speech system that empowers those afflicted with speech impairments to convey ideas by internally articulating words without producing audible sound. This method harnesses the minimal movements involved in articulation, making it less arduous than conventional vocalization, especially for individuals facing communication challenges. Silent Speech Interfaces (SSIs) form the foundation of our research, enabling the decoding of neuromuscular signals associated with internal speech articulation to facilitate non-invasive and effective communication for individuals with speech impairments. This research entails the utilization of a multi-electrode configuration to capture subtle, low-voltage electrical signals positioned on the surface area of the subject's neck. These signals emerge as a consequence of neural activation within the speech articulator muscles, signifying the internal articulation of words within the oral cavity, accompanied by minimal physical movement. Subsequent to signal acquisition, a comprehensive analysis of the data is conducted using a biosensing board (Cyton), facilitated by the implementation of the OpenBCI GUI interface and visualization via Python plotting tools. © 2025 ieee.

关键词： Digital signal processing

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Defect Detection in Industrial Soldering Processes Using Machine Learning: A Critical Literature Review

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ieee ACCESS 2025年 13卷 41533-41558页

作者： Eisentraut, Luca Hosch, Johanna Roytenberg, Maksym Benecke, Amelie Penava, Pascal Buettner, Ricardo Helmut Schmidt Univ Univ Fed Armed Forces Hamburg Chair Hybrid Intelligence D-22043 Hamburg Germany

As electrical devices take on more life-critical roles, such as in autonomous driving, ensuring the quality of solder joints during production becomes increasingly important. Recently, there has been a growing interest in using machine learning techniques for this purpose. However, current research lacks a comprehensive overview that categorizes and analyzes relevant studies based on their specific intervention points within the production process. This literature review aims to examine and evaluate research coverage along three dimensions: intervention points in the process, non-destructive testing methods, and machine learning techniques employed. For this review, 112 conference papers and journal articles published since 2010 were selected from three databases using the PRISMA methodology. These publications were classified into the three dimensions previously mentioned, summarized, and analyzed. Furthermore, the literature core is critically evaluated to identify research gaps and limitations. The analysis shows that most studies focus on solder joint control, with few addressing intervention points in solder paste and component placement. Visual imaging and neural networks are the dominant techniques for non-destructive testing and machine learning, respectively. Despite a variety of literature that uses high-performance neural networks, meeting industrial detection standards often requires tolerating high false alarm rates. The findings contribute to structuring existing research and identifying research needs, particularly in validating these systems and integrating data from various testing methods and intervention points.

关键词： Systematic literature review Soldering Defect detection Testing Quality control visualization Production Process control Inspection Databases Critical literature review defect detection machine learning solder joint

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Enhanced Indoor Pathfinding Navigation in Multistoried Buildings Using a Hybrid of RRT-Connect and Dijkstra's Algorithm

Enhanced Indoor Pathfinding Navigation in Multistoried Build...

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2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramamoorthy, S. Yadav, Abhishek Pal, Subha Bal School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331509828

This paper presents a hybrid approach to enhance indoor pathfinding and navigation within complex multistory environments by integrating RRT-Connect and Dijkstra's algorithm. The objective is to address the limitations of traditional algorithms like Dijkstra and A*, which struggle with computational inefficiency and adaptability in dynamic, high-dimensional spaces. The proposed solution leverages the rapid path generation capabilities of RRT-Connect, combined with Dijkstra's algorithm for refining and optimizing the final route to ensure accuracy and optimality. To further improve efficiency, parallel processing techniques utilizing multi-threaded CPU operations are incorporated, enabling real-time computation even in dynamic environments. Additionally, a frontend visualization system is developed using React, which provides users with real-time rendering of path nodes on multistory floor plan images, enhancing usability and system interactivity. Experimental findings reveal that the hybrid approach achieves a significant reduction in computation time - up to 25% compared to traditional methods - while maintaining high pathfinding accuracy. This novel combination of rapid exploration, optimal path refinement, and real-time visualization offers a robust and scalable solution for navigating complex multistory environments, making it a significant advancement in the field of indoor navigation. © 2025 ieee.

关键词： visualization

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Bringing Intelligence to SAR Missions: A Comprehensive Dataset and Evaluation of YOLO for Human Detection in TIR Images

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ieee ACCESS 2025年 13卷 17208-17235页

作者： Rizk, Mostafa Bayad, Israa Lebanese Int Univ Sch Engn Beirut 146404 Lebanon Lebanese Univ Fac Sci Beirut 90656 Lebanon

Effective search and rescue (SAR) missions are critical for locating and assisting injured or missing individuals while optimizing resource allocation and minimizing costs. This work aims to enhance the efficiency of these missions by exploring advanced deep learning techniques for precise and efficient human detection in thermal images. The primary focus of this work is on YOLOv8, the latest version of the You Only Look Once (YOLO) object detection method. The paper also evaluates YOLOv7-Tiny, which is the most streamlined variant derived from YOLOv7. To support the investigation, a novel dataset comprising 17,148 thermal images with 90,882 instances of human subjects representing various conditions and scenarios has been carefully curated. This dataset is used for training and evaluating different variants of YOLOv8 and YOLOv7. The evaluation of the trained models reveals the efficacy of YOLOv8 in detecting humans in thermal images, achieving an average precision rate of 95% with the largest model, YOLOv8x, and an average precision rate of 91% with the smallest model, YOLOv8n. The evaluation of YOLOv7-Tiny shows that it achieves an average precision similar to YOLOv8n, which is 48% lighter in size and more practical choice for real-world deployment. Also, the trained models are deployed on graphical processing units. The tiniest trained model, YOLOv8n, achieves an inference rate of 273.6 frames per second (FPS) while the largest model, YOLOv8, achieves an inference rate of 100.29 FPS. The achieved inference rates along with the achieved detection performances meet with the requirement of fast detection of humans in SAR missions.

关键词： Cameras Imaging YOLO visualization Accuracy Temperature sensors Deep learning Temperature measurement Temperature Reflection Thermal images object detection deep learning convolutional neural networks human detection search and rescue missions

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