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27th International Conference on Pattern Recognition, ICPR 2024

作者： Siddhad, Gourav Dey, Sayantan Roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Osaka Sakai599-8531 Japan

ISBN: (纸本)9783031781940

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Unlocking Domain Specificity: Fine-Tuning Llama 2 for Enhanced Performance on Custom Datasets

Unlocking Domain Specificity: Fine-Tuning Llama 2 for Enhanc...

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International IoT, Electronics and Mechatronics Conference, IEMTRONICS 2024

作者： Bhattacharya, Swarnadwip Bhattacharjee, Anindita Das Roy, Pranab Singha Samanta, Tapas Department of Computer Science and Engineering Institute of Engineering and Management Kolkata700091 India Department of CSE IEM Centre of Excellence for InnovAI Institute of Engineering and Management Kolkata700091 India Department of Computer and Informatics Variable Energy Cyclotron Centre Kolkata700064 India

ISBN: (纸本)9789819747795

This paper aims to improve Llama 2’s performance by using personalized and modified datasets. Despite the impressive capabilities of large language models (LLMs) such as Llama 2, their effectiveness may be limited in specialized domains. The proposed method entails fine-tuning Llama 2 on custom datasets to optimize performance efficiently. The study focuses on the impact of quantization-aware low-rank adapter layers (QLoRA) on a single GPU’s resource-efficient fine-tuning performance. Furthermore, the study looks into the design of instruction datasets to guide the model toward desired behaviors. When Llama 2 is fine-tuned with QLoRA, performance improves significantly across tasks such as text summarization, question answering, and natural language generation in a variety of domains. The paper concludes by highlighting the broader implications of the findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Custom datasets Fine-tuning Instruction datasets Large language models (LLMs) Llama 2 Personalized datasets QLoRA Quantization-aware low-rank adapter layers (QLoRA) Resource-efficient fine-tuning Specialized domains

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Fog Computing-based Real-Time Emotion Recognition using Physiological Signals 27

Fog Computing-based Real-Time Emotion Recognition using Phys...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Erzurumluoglu, Omur Fatmanur Ozlem, Kadir Atalay, Asli Tuncay Atalay, Ozgur Ince, Gokhan Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Faculty of Textile Technologies and Design Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9791188428137

Emotion recognition based on physiological signals has become a crucial area of research in affective computing and human-computer interaction, with applications in smart homes, workplaces, educational institutions, healthcare, and entertainment. In this study, a real-time emotion recognition system utilizing fog computing architecture was developed by considering the challenges of latency, total response time, resource usage, and security in IoT environments. The random forest machine learning model was trained with time-based statistical features by using the DREAMER dataset. Even though the model achieved an accuracy of 84.21% with 104 features, to meet real-time performance requirements, the system was optimized to calculate 24 features, maintaining a commendable accuracy of 79.70%. Extensive experiments demonstrated the superior performance of fog computing compared to edge and cloud computing in terms of latency, queuing delay, jitter, and most importantly total response time. The results highlight the proposed system's ability to support multiple users simultaneously. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fog computing

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Sampling-Based Motion Planning for Guide Robots Considering User Pose Uncertainty 25th

Sampling-Based Motion Planning for Guide Robots Considering...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Mosquera-Maturana, Juan Sebastian Hernández Vega, Juan David Romero Cano, Victor Faculty of Engineering Universidad Autónoma de Occidente Cali Colombia School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783031720581

In this paper, we propose a framework to address the problem of guiding a person within a semi-structured environment in a socially acceptable manner that prioritises safety and comfort. We propose an algorithm based on the optimal Rapidly exploring Random Tree (RRT*) algorithm for path planning. Our proposal utilises Dubins curves and takes into account the user during path planning to generate a navigation path that allows the robot to follow a feasible path that can also be navigated by the user. A comparative analysis against standard path planning based on the RRT* algorithm and the Social Force Model validates the efficacy of our proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Microrobots

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Direction-of-Arrival Estimation with Arbitrary Linear Array: A Turbo-HS Approach 5

Direction-of-Arrival Estimation with Arbitrary Linear Array:...

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5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Chen, Zhendong Huang, Yongfeng Li, Dingzhao Lu, Xicheng Sun, Haixin School of Informatics Xiamen University Xiamen China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9798331533694

Conventional subspace-based direction-of-arrival (DOA) estimation algorithms require optimal environments to achieve satisfactory estimation accuracy. With the advancement of sparse signal recovery theory, sparse optimization-based DOA estimation algorithms have exhibited commendable performance. One of the popular sparse optimization method is sparse Bayesian learning (SBL), due to the fact that it does not require regularization parameters. However, existing SBL algorithms do not adequately address the issue of noise modeling. In order to more accurately model the received signal covariance matrix under finite snapshots, we transform the signal covariance matrix into the form of sparse representation, normalize the variance of the observation matrix and vectorize it. Based on this, we introduce the Turbo-CS framework, which divides the DOA estimation problem into two subproblems: LMMSE estimation for external noise separation and a novel prior for internal SBL DOA estimation. As demonstrated by the experimental results, the proposed algorithm performs optimally in a variety of scenarios. © 2025 IEEE.

关键词： Direction of arrival

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No-Reference Point Cloud Quality Assessment via Graph Convolutional Network

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IEEE Transactions on Multimedia 2025年 27卷 2489-2502页

作者： Chen, Wu Jiang, Qiuping Zhou, Wei Shao, Feng Zhai, Guangtao Lin, Weisi Ningbo University School of Information Science and Engineering Ningbo315211 China Cardiff University School of Computer Science and Informatics CardiffCF10 3AT United Kingdom Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Three-dimensional (3D) point cloud, as an emerging visual media format, is increasingly favored by consumers as it can provide more realistic visual information than two-dimensional (2D) data. Similar to 2D plane images and videos, point clouds inevitably suffer from quality degradation and information loss through multimedia communication systems. Therefore, automatic point cloud quality assessment (PCQA) is of critical importance. In this work, we propose a novel no-reference PCQA method by using a graph convolutional network (GCN) to characterize the mutual dependencies of multi-view 2D projected image contents. The proposed GCN-based PCQA (GC-PCQA) method contains three modules, i.e., multi-view projection, graph construction, and GCN-based quality prediction. First, multi-view projection is performed on the test point cloud to obtain a set of horizontally and vertically projected images. Then, a perception-consistent graph is constructed based on the spatial relations among different projected images. Finally, reasoning on the constructed graph is performed by GCN to characterize the mutual dependencies and interactions between different projected images, and aggregate feature information of multi-view projected images for final quality prediction. Experimental results on two publicly available benchmark databases show that our proposed GC-PCQA can achieve superior performance than state-of-the-art quality assessment metrics. © 1999-2012 IEEE.

关键词： Cloud platforms

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A Social Robot Companion for Individuals Eating Alone 25

A Social Robot Companion for Individuals Eating Alone

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30th International Conference on Intelligent User Interfaces Companion, IUI 2025

作者： Soufargi, Selim Niewiadomski, Radoslaw Fong, Hunter Mancini, Maurizio Department of Computer Science Sapienza University of Rome Rome Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

ISBN: (纸本)9798400714092

In this paper, we present the first implementation of a social robot acting as a companion for individuals eating alone. Our system can engage in multimodal interactions with the user during meals. It conducts conversations on various topics, complemented by gestures and other nonverbal behaviors. The system comprises a NAO robot, human activity recognition modules, a speech recognition module, and a dialog management system integrated with an LLM model. In the paper, we present a technical description of the system and the interaction flow, as well as discuss the possible applications and planned validation. © 2025 Copyright held by the owner/author(s).

关键词： Social robots

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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising

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IEEE Access 2025年 13卷 71880-71907页

作者： El-Nabi, Samy Abd Ibrahim, Ahmed F. El-Rabaie, El-Sayed M. Hassan, Osama F. Soliman, Naglaa F. Ramadan, Khalil F. El-Shafai, Walid Faculty of Computer Science and Engineering Department of Artificial Intelligence Engineering South Sinai 46511 Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Suez Canal University Faculty of Computers and Informatics Information Systems Department Ismailia41522 Egypt Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Computer Science Department Riyadh11586 Saudi Arabia

With the rapid development of intelligent transportation systems and growing emphasis on driver safety, real-time detection of driver drowsiness has become a critical area of research. This study presents a robust and scalable driver drowsiness detection framework that integrates a Swin Transformer-based deep learning model with a diffusion model for image denoising. While conventional convolutional neural networks (CNNs) are effective in standard vision tasks, they often suffer performance degradation in real-world driving scenarios due to noise, poor lighting, motion blur, and adversarial attacks. To address these challenges, the proposed model focuses on eye-state detection, specifically, prolonged eye closure, as a primary indicator of driver disengagement and fatigue. Our system introduces a novel preprocessing stage using a denoising diffusion model built on a U-Net encoder-decoder architecture, effectively mitigating the impact of Gaussian noise and adversarial perturbations. Additionally, we incorporate adversarial training with Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks, demonstrating significant improvements in classification accuracy and resilience. Evaluations are conducted on two benchmark datasets, Eye-Blink and Closed Eyes in the Wild (CEW), under both clean and noisy conditions. Comparative experiments show that the proposed system outperforms several state-of-the-art models, including ViT, ResNet50V2, InceptionV3, MobileNet, DenseNet169, and VGG19, in terms of accuracy (up to 99.82%), PSNR (up to 41.61 dB), and SSIM (up to 0.984), while maintaining competitive inference times suitable for practical deployment. Moreover, a detailed sensitivity analysis of data augmentation strategies reveals that techniques such as rotation and horizontal flip substantially enhance the model’s generalization across variable visual inputs. The system also demonstrates improved robustness under real-world black-box scenarios and adver

关键词： Sensitivity analysis

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Optimal control for unreliable manufacturing and remanufacturing system under environmental subsidy and overrun penalty mechanism

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IEEE Transactions on engineering Management 2025年 72卷 1983-2002页

作者： Turki, Sadok Jemmali, Mahdi Hidri, Lotfi University of Lorraine Department of Logistic and Maintenance UFR MIM at Metz Metz17 France University of Sharjah Computer Science Department College of Computing and Informatics Sharjah United Arab Emirates King Saud University Industrial Engineering Department College of Engineering Riyadh11421 Saudi Arabia

Because of environmental concerns, remanufacturing has become an integral process for many production companies. Most published papers dealing with manufacturing and remanufacturing systems (MRSs) overlook some industrial features. These include machine unreliability, the distinction between newly manufactured and remanufactured products, and uncertainty about the returned used products (RUPs). As a result, these models do not reflect the reality of industrial systems. This study aims to bridge the gap in research on MRSs by elaborating an optimal and dynamic control of manufacturing, remanufacturing, and recovery of RUPs. The goal is to develop a model based on manufacturing remanufacturing emissions hedging point control policy (MREHP) to adjust the production of new and remanufactured products, and recovery of RUPs according to economic and environmental thresholds. To reduce carbon emissions, a new method is proposed based on the environmental subsidy and overrun penalty mechanism (ESOPM), and that is combined with MREHP to control in parallel the MRS and generated emissions. A robust optimization algorithm is developed to determine the optimal economic and environmental thresholds in MREHP and ESOPM. A comparative study is provided to evaluate our proposed ESOPM in comparison with traditional reduction policies: carbon cap and trade (CCT) and carbon tax regulation (CTR). The obtained results reveal that machines' reliability, rate of RUPs, emissions subsidy, and carbon emissions limit have notable impacts on economic and environmental thresholds. Additionally, results show that the proposed ESOPM performs higher profit than CTR and CCT while reducing emissions when the carbon price is below a certain value. © 1988-2012 IEEE.

关键词： Fabrication

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