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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Khokhar, Maham Bakir-Gungor, Burcu Yousef, Malik Department of Data Science Social Sciences Institute Abdullah Gul University Kayseri38080 Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat13206 Israel

The advent of high-throughput transcriptomic technologies has generated vast transcriptomic datasets, challenging current analytical methodologies with their sheer volume and complexity. The Grouping-Scoring Modeling (G-S-M) approach is one of the recent approaches that treat groups of genes (or clusters of genes) by embedding prior biological knowledge with machine learning in order to detect the most significant groups for classification tasks. The G-S-M might need to treat thousand ten thousand of groups (scoring those groups) which might affect the speed and performance of the algorithm. In response, this study introduces the Pre Scoring G-S-M model, an enhancement of the established Grouping-Scoring-Modeling (G-S-M) framework. This approach incorporates a Pre-Scoring component that leverages the Limma package for its empirical Bayes methods to optimize initial transcriptomic data evaluation through a percentage-based selection of statistically significant gene groups. Aimed at reducing computational demand and streamlining feature selection, the model also addresses data redundancy by eliminating duplicate gene-disease associations. Application to nine human gene expression datasets from the GEO database showed promising results. It demonstrated improvements in computational efficiency and analytical precision while reducing the number of features selected per dataset compared to the traditional G-S-M approach, without compromising accuracy. These initial findings highlight the Pre-Scoring G-S-M model's potential to enhance transcriptomic data analysis, indicating a promising direction for future bioinformatics research. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Data accuracy

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Parallel Path Planning for Multi-Robot Coordination

Parallel Path Planning for Multi-Robot Coordination

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Jorge Ribeiro Marina Brilhante Diogo Miguel Matos Carlos A. Silva Héber Sobreira Pedro Costa CRIIS - Centre of Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Flowbotic Mobile Systems S.A Viseu Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

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Classification of benign and malignant breast lesions in mammograms using dense-unified multiscale attention network and data-efficient image transformers

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The European Physical Journal Special Topics 2025年 1-19页

作者： Paavankumar, S. Karthik, R. Idayachandiran, Gangatharan Sri, Pulaparthi Penchala Deepthi Illakiya, T. School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai India

Breast cancer is a common and a serious health problem and it is the major cause of morbidity and mortality for women. Early detection of the disease is particularly challenging because abnormalities such as masses and microcalcifications exhibit subtle and diverse characteristics that are often difficult to identify in mammograms. In recent years, advancement in artificial intelligence, particularly deep learning (DL), has shown to improve diagnostic accuracy and early-stage tumor detection. This study aims to improve performance of DL models by considering both masses and microcalcifications in the proposed work to classify breast cancer abnormalities. The proposed work introduces a novel dual-track network that employs a combination of dense-unified multiscale attention fusion (UMAF) track and data-efficient image transformer (DeiT). The DeiT track processes the entire image simultaneously using patch embeddings, enabling them to capture multiscale representations and dependencies across the entire image. Simultaneously, the Dense-UMAF track focuses on extracting localized features while utilizing connectivity of DenseNet architecture to enable effective feature reuse. This approach generates relevant input features through residual connections of varying lengths, thereby effectively addressing the vanishing gradient problem. The UMAF improves feature extraction by capturing multiscale information, resulting in a better representation of the input data. This dual-track architecture is specifically designed to capture the characteristics of mass and calcification abnormalities in mammograms, which display both localized features and global contextual patterns. The proposed network was evaluated on the Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset, obtaining a classification accuracy of 88.69%.

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What Drives the Use of Generative Artificial Intelligence to Promote Educational Sustainability? Evidence from SEM-ANN Approach

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TechTrends 2025年 1-15页

作者： Arpaci, Ibrahim Al-Emran, Mostafa Al-Qaysi, Noor Al-Sharafi, Mohammed A. Management Information Systems College of Business Administration Gulf University for Science and Technology Mishref Kuwait Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emirates Engineering Technical College Al-Bayan University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq IRC for Finance and Digital Economy KFUPM Business School King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. However, there is a notable absence of empirical research concerning the holistic use of generative AI within educational institutions and its long-term impact on educational sustainability. This study investigates the factors that predict the use of generative AI and its subsequent influence on educational sustainability. This study enhanced the extended “Unified Theory of Acceptance and Use of Technology” (UTAUT-2) by including personal innovativeness as an external variable and educational sustainability as an outcome. A hybrid methodology, integrating “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN), was used to evaluate the research model using data collected from 1,011 university students. The SEM analysis confirmed all hypotheses. Specifically, a positive relationship was found between use behavior and educational sustainability. These results show that the combined variables collectively account for 63% and 66% of the variance in use behavior and educational sustainability, respectively. The sensitivity analysis identified habit as the most important predictor of use behavior. The findings offer both theoretically important and practically valuable implications for designing user-centered AI integration in education. © Association for Educational Communications & Technology 2025.

关键词： Artificial intelligence Educational sustainability SEM-ANN UTAUT-2

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A Study on Transfer Learning TinyML-Based Intrusion Detection Framework on IoT Devices

A Study on Transfer Learning TinyML-Based Intrusion Detectio...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jedidah Mwaura Shunsuke Araki Muhammad Bisri Musthafa Samsul Huda Yasuyuki Nogami Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Graduate School of Environmental Life Natural Science and Technology Okayama University Okayama Japan Green Innovation Center Okayama University Okayama Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Currently, there are many ongoing attempts to incorporate transfer learning into Intrusion Detection systems. Transfer learning leverages on the knowledge of previous models thus reducing training time, improving accuracy and making it possible to train new models with limited data. However, it is difficult to incorporate such a technique into IoT devices with limited computational power. In this paper, we propose a Transfer Learning TinyML-based IDS framework with feature selection to be implemented on a microcontroller for detecting attacks on IoT devices. To implement the proposal, we leveraged knowledge achieved from our previous DDoS attack detection model to create a new transfer learning framework that could detect various IoT attacks such as malware attacks and zero-day attacks. To test the transfer learning framework, we trained it using two separate sets of datasets each containing different attacks. After each training, we evaluated the original model, converted it into a format compatible with a microcontroller and evaluated it further. The TL TinyML-based framework was evaluated based on its accuracy, model sizes and average inference times. As the results confirmed, the framework performed well and can successfully be implemented on a microcontroller without depleting its resources.

关键词： Training Accuracy Microcontrollers Computational modeling Transfer learning Intrusion detection Feature extraction Malware Data models Internet of Things

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One-bit Compressed Sensing using Generative Models

arXiv

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arXiv 2025年

作者： Kafle, Swatantra Joseph, Geethu Varshney, Pramod K. ANDRO Computational Solutions NY United States Electrical Engineering and Computer Science Department Syracuse University NY United States Signal Processing Systems group Electrical Engineering Mathematics and Computer Science faculty the Delft University of Technology Netherlands

This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pretrained neural network, learns to map from a low-dimensional latent space to a higher-dimensional set of sparse vectors. This generator is then used to reconstruct sparse vectors from their one-bit measurements by searching over its range. The presented algorithm provides an excellent reconstruction performance because the generative model can learn additional structural information about the signal beyond sparsity. Furthermore, we provide theoretical guarantees on the reconstruction accuracy and sample complexity of the algorithm. Through numerical experiments using three publicly available image datasets, MNIST, Fashion-MNIST, and Omniglot, we demonstrate the superior performance of the algorithm compared to other existing algorithms and show that our algorithm can recover both the amplitude and the direction of the signal from one-bit measurements. © 2025, CC BY.

关键词： Compressed sensing

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RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition

arXiv

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arXiv 2025年

作者： Zheng, Jinhui Liu, Zhiquan Si, Yain-Whar Li, Jianqing Zhang, Xinyuan Li, Xiaofan Huang, Haozhi Gong, Xueyuan School of Intelligent Systems Science and Engineering Jinan University Guangdong Province China College of Cyber Security Jinan University Guangdong Province China Department of Computer and Information Science Faculty of Science and Technology University of Macau China School of Computer Science and Engineering Macau University of Science and Technology China

Handwritten Paragraph Text Recognition (HPTR) is a challenging task in computer Vision, requiring the transformation of a paragraph text image, rich in handwritten text, into text encoding sequences. One of the most advanced models for this task is Vertical Attention Network (VAN), which utilizes a Vertical Attention Module (VAM) to implicitly segment paragraph text images into text lines, thereby reducing the difficulty of the recognition task. However, from a network structure perspective, VAM is a single-branch module, which is less effective in learning compared to multi-branch modules. In this paper, we propose a new module, named Re-parameterizing Vertical Attention Fusion Module (RVAFM), which incorporates structural re-parameterization techniques. RVAFM decouples the structure of the module during training and inference stages. During training, it uses a multi-branch structure for more effective learning, and during inference, it uses a single-branch structure for faster processing. The features learned by the multi-branch structure are fused into the single-branch structure through a special fusion method named Re-parameterization Fusion (RF) without any loss of information. As a result, we achieve a Character Error Rate (CER) of 4.44% and a Word Error Rate (WER) of 14.37% on the IAM paragraph-level test set. Additionally, the inference speed is slightly faster than VAN. © 2025, CC BY.

关键词： Parameterization

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Integrating Multimodal Perception into Ground Mobile Robots

Integrating Multimodal Perception into Ground Mobile Robots

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Ricardo B. Sousa Héber Miguel Sobreira João G. Martins Paulo G. Costa Manuel F. Silva António Paulo Moreira Faculty of Engineering University of Porto (FEUP) Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal ISEP Polytechnic of Porto Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multimodal perception systems enhance the robustness and adaptability of autonomous mobile robots by integrating heterogeneous sensor modalities, improving long-term localisation and mapping in dynamic environments and human-robot interaction. Current mobile platforms often focus on specific sensor configurations and prioritise cost-effectiveness, possibly limiting the flexibility of the user to extend the original robots further. This paper presents a methodology to integrate multimodal perception into a ground mobile platform, incorporating wheel odometry, 2D laser scanners, 3D Light Detection and Ranging (LiDAR), and RGBD cameras. The methodology describes the electronics design to power devices, firmware, computation and networking architecture aspects, and mechanical mounting for the sensory system based on 3D printing, laser cutting, and bending metal sheet processes. Experiments demonstrate the usage of the revised platform in 2D and 3D localisation and mapping and pallet pocket estimation applications. All the documentation and designs are accessible in a public repository.

关键词： Laser radar Robot vision systems Wheels Three-dimensional printing Cameras Distance measurement Robustness Mobile robots Laser beam cutting Autonomous robots

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Architecting Digital Twins for Intelligent Transportation systems

Architecting Digital Twins for Intelligent Transportation Sy...

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models Software architecture Transportation computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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