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YOLOv5s-DNF: A lighter and real-time method for detecting surface defects in steel 4

YOLOv5s-DNF: A lighter and real-time method for detecting su...

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4th international conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Ding, Hao Xia, Bo School of Information Science and Engineering Linyi University Shandong Linyi China

ISBN: (纸本)9798350326444

Defect detection plays a crucial role in ensuring the surface quality of steel, as it impacts both downstream production and the overall quality of the final product. However, recognizing surface defects in steel has always been challenging due to the small occurrence area, diverse types of deformations, and various types of defects. In this article, we propose a novel lightweight defect detection model named YOLOv5s-DNF, which achieves high detection performance while maintaining a lightweight architecture. Specifically, we enhance the C3 structure in YOLOv5 by incorporating the Deformable Convolution Network v3 (DCNv3) operator, referred to as C3-Dcnv3. this modification effectively addresses the limitations of fixed rectangle structure sampling, enhancing the network's ability to model object deformations. Furthermore, we introduce the Normalized Wasserstein Distance (NWD) to the loss function, improving the model's feature representation and regularization, thereby mitigating overfitting and enhancing generalization ability. In addition, we propose the C3-Faster structure by integrating partial convolution (PConv) into the C3 structure, which reduces the model's parameter count and computational complexity while minimizing precision loss. the proposed YOLOv5s-DNF model is evaluated on the NEU-DET dataset, and the experimental results demonstrate that it achieves a desirable balance between accuracy and parameter efficiency, outperforming other state-of-the-art methods. Furthermore, we conduct additional experiments on the publicly available Wood Surface Defects dataset, further validating the effectiveness of our proposed model. Various experiments have shown that YOLOv5s-DNF achieves a better trade-off between running speed and detection accuracy. © 2023 IEEE.

关键词： Surface defects

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Flow Is Best, Fast and Scalable: the Incremental Parametric Cut for Maximum Density and Other Ratio Subgraph Problems 16

Flow Is Best, Fast and Scalable: The Incremental Parametric ...

引用

16th international conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2024

作者： Hochbaum, Dorit S. Department of Industrial Engineering and Operations Research University of California Berkeley United States

ISBN: (纸本)9789897587160

the maximum density subgraph, or densest subgraph, problem has numerous applications in analyzing graph and community structures in social networks, DNA networks and financial networks. the densest subgraph problem has been the subject of study since the early 80s and polynomial time flow-based algorithms are known, yet research in the last couple of decades has been focused on developing heuristic methods for solving the problem claiming that flow computations are computationally prohibitive. We introduce here a new polynomial time algorithm, the incremental parametric cut algorithm (IPC) that solves the maximum density subgraph problem and many other max or min ratio problems in the complexity of a single minimum-cut. A characterization of all these efficiently solvable ratio problems is given here as problems with monotone integer programming formulations. IPC is much more efficient than the parametric cut algorithm since instead of generating all breakpoints it explores only a tiny fraction of those breakpoints. Compared to the heuristic methods, IPC not only guarantees optimality, but also runs orders of magnitude faster than the heuristic methods, as shown in an accompanying experimental study. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Heuristic methods

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Using an HCI Perspective to Encourage Young Students to Pursue Computer science and engineering Careers: the "Envisioning the Digital City" Workshop 19th

Using an HCI Perspective to Encourage Young Students to Purs...

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19th international-Federation-for-Information-Processing-Technical-Committee-13 (IFIP TC13) international conference on Human-Computer Interaction (INTERACT)

作者： Diaz, Paloma Onorati, Teresa Montero, Alvaro Univ Carlos III Madrid Comp Sci Dept Madrid Spain

ISBN: (纸本)9783031616877;9783031616884

In recent decades, the demand for STEM professionals has increased, and consequently, the effort of educational institutions to sponsor their STEM degrees. In the context of a university program promoting STEM careers among high-school students, we designed a workshop for middle and high-school students to show them a different perspective of computing as a pragmatic, social, and people-centered profession. We focus on teaching basic concepts of Human-Computer Interaction (HCI) as part of a problem-solving strategy to offer creative solutions to real problems of real people. the workshop proposes a list of well-known problems in urban environments, like social integration, elderly loneliness, or recycling, and asks the students to solve them envisioning an innovative mobile app by following a design thinking approach and applying several design artifacts, like scenario boards, user personas, thematic cards, and paper-based sketches. the results show that with little or no experience in designing and programming, participants engage in fruitful brainstorming sessions, offer creative solutions, and even implement low-level prototypes.

关键词： Design thinking Design Workshop STEM Computer science & engineering

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Automatic programming and Education 22

Automatic Programming and Education

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6th international conference on the art, science, and engineering of programming, programming 2022

作者： Lewis, Clayton University of Colorado Boulder BoulderCO United States

ISBN: (纸本)9781450396561

Automatic programming, as supported by recent language-model based AI systems, potentially allows a new approach to making computation a useful tool for learning, a goal of the Boxer project. this paper shows that the Codex system can be used to support some of the explorations in mathematics for which Boxer has been used. Virtually no knowledge of programming is required. Reflecting on the lessons from this exploration may sharpen the goals we bring to educational computing. What knowledge about computing, as distinct from the ability to creatively use computing, should learners gain? © 2022 Owner/Author.

关键词： Automatic programming

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Comparative Analysis of Air Quality Index Using Large Language Models and Machine Learning 4

Comparative Analysis of Air Quality Index Using Large Langua...

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4th international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2024

作者： Sundaramurthy, Shanmugam Sai Harish, G. Reddy, Chandra Shekar V. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9781643685519

Accurate air quality prediction is crucial for environmental monitoring and public health. this study explores a novel approach using machine learning algorithms and large language models to predict the Air Quality Index (AQI). While traditional AQI prediction relies on complex models and extensive data on pollutants and meteorological factors, this research investigates the use of readily available fuel consumption data as an alternative predictor, given its close link to emissions. the study employs supervised machine learning algorithms, including Random Forest and Gradient Boosting, utilizing fuel consumption data as input features to build predictive models. Additionally, a state-of-the-art large language model, GPT-3.5-turbo-instruct, is fine-tuned on historical AQI data and evaluated for its predictive capabilities. the performance of both machine learning models and the language model is compared using various metrics, and the results demonstrate that both approaches achieve high AQI prediction accuracy, outperforming traditional methods based on pollutant concentration data. Notably, the fine-tuned language model exhibits superior performance, potentially due to its ability to capture complex dependencies and contextual information from the training *** work highlights the potential of leveraging readily available fuel consumption data and advanced language models for accurate and cost-effective AQI prediction. the findings have significant implications for developing scalable air quality monitoring systems, enabling timely interventions and informed decision-making to mitigate the adverse effects of air pollution. © 2024 the Authors.

关键词： Prediction models

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CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID-19 Detection in CT-Scans 4th

CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Das, Ankit Banik, Debapriya Roy, Kaushiki Chan, Gordon K. Bhattacharjee, Debotosh Department of Computer Science and Engineering Amity University Kolkata700135 India Department of Computer Science and Engineering ICFAI University Tripura799210 India Institute of Engineering and Management Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Oncology and the Department of Experimental Oncology Cross Cancer Institute University of Alberta EdmontonABT6G 1Z2 Canada Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

ISBN: (纸本)9789819726103

the coronavirus disease, COVID-19, that grasped the earth since 2019, has taken a serious toll on humanity. COVID-19 cases were spiking exponentially in densely populated regions, and the health infrastructure was challenged daily. As such, artificial Intelligence (AI) had to step in to improvise medical imaging applications. One such use case was the classification of Computerized Tomography (CT) Scan images into COVID-infected and un-infected categories. thus it would effectively classify a patient being either COVID Positive or Negative. Computer Vision (CV) algorithms manifest the potential to solve such image classification problems. In retrospect, deep learning-based architectures have been very productive. However, Vision Transformers (ViT) have taken over CV since its inception. In this study, we propose CoViT-Net: A pre-trained hybrid Vision Transformer architecture for detecting COVID-19 in CT scan images. Our proposed CoViT-Net has a unique architecture incorporating a ViT-S/16 feature extractor, pre-trained on the ImageNet21k dataset, and a modified MLP block with dense layers on top. CoViT-Net has thereby achieved a state-of-the-art accuracy score of 0.9638. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Maatran: Revolutionizing Maternal Care through Remote Monitoring and Risk Prediction 4th

Maatran: Revolutionizing Maternal Care Through Remote Monito...

引用

4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Kamal, Kulsum Das, Niladri Shekhar Rakshit, Subroto Dutta, Rudraneel Saha, Sovan Department of Computer Science and Engineering Institute of Engineering and Management Sector V Kolkata India Techno Main Salt Lake Sector V Kolkata India

ISBN: (纸本)9789819726134

Maternal mortality due to pregnancy complications remains a significant global challenge, necessitating the development of innovative approaches to safeguard pregnant women from potential threats. this paper introduces Maatran, a technological solution that leverages IoT, Cloud technologies, and Machine Learning solutions to address maternal health issues. Maatran utilizes Arduino-based wearable sensor devices to remotely collect health data, which is then transmitted to an Android app interface for data processing and storage in the cloud. A Random Forest model is employed to accurately predict maternal health risks. the system’s features provide a holistic solution for remote health monitoring of pregnant women and facilitate direct communication with healthcare professionals. through rigorous machine learning model training, Maatran achieved accuracy, ROC-AUC, and log loss of 0.93, 0.91, and 0.93 respectively, surpassing existing state-of-the-art models. the implementation of Maatran demonstrates its potential to significantly improve maternal health outcomes by enabling early risk detection and timely interventions. the supplementary document can be found at https://***/kulsumkamal/Maatran-Supplementary. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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PneumoAid: A Human-Centered AI Platform for Pneumonia Detection and Patient Support 5

PneumoAid: A Human-Centered AI Platform for Pneumonia Detect...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shajan, Rishil Santhosh, Maria Joseph, Honey Arjunan, Sajin Roy, Shaun Jacob Muhammed Yaseen, H. Amal Jyothi College of Engineering Department of Computer Science Kottayam India

ISBN: (纸本)9798350356236

PneumoAid is a digital healthcare solution designed to enhance the diagnosis and management of pneumonia by leveraging advanced machine learning algorithms. Using state-of-the-art models like ResNet50, VGGI9, DenseNetl21, and Xception, the system classifies chest X-ray images into bacterial, viral, or normal categories. this paper focuses on comparing the performance of these models using a Kaggle dataset containing 5216 images, with training, validation, and testing splits. the experiments were conducted on Google Colab with a T4 GPU for efficient processing. Beyond diagnosis, PneumoAid includes a voice-enabled chatbot to guide patients on medication, nutrition, and wellness while scheduling appointments and ensuring treatment adherence. By integrating accurate diagnostics with patient-centered features, PneumoAid aims to improve pneumonia care and streamline healthcare delivery. © 2025 IEEE.

关键词： Pulmonary diseases

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Improving Motor Imagery Classification in BCI Systems Using EMD and Multi-Layer CNNs 14

Improving Motor Imagery Classification in BCI Systems Using ...

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14th international conference on Computer and Knowledge engineering, ICCKE 2024

作者： Arghand, Reza Chaibakhsh, Ali Radman, Moein University of Guilan Intelligent Systems and Advanced Control Lab Faculty of Mechanical Engineering Rasht Guilan41996-13776 Iran University of Essex Brain-Computer Interfacing and Neural Engineering Laboratory School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)9798331511272

Brain-Computer Interface (BCI) systems are relatively new technologies that could play a significant role in aiding the recovery of impaired activities resulting from neuromuscular disabilities in affected individuals. Accurate recognition and classification of motor imagery in BCI systems present a challenge, leading to extensive research in recent years aimed at improving the accuracy of these systems. In this study, a combination of the Empirical Mode Decomposition (EMD) method and a multi-layer Convolutional Neural Network (CNN) is employed. Initially, the signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and all IMFs across all trials are analyzed to select the best ones, which are then fed into the CNN as inputs. Additionally, the study incorporates the fusion of three CNN networks, each corresponding to a different IMF. the features extracted from these networks are combined and used to train an SVM classifier. the proposed method achieved an accuracy of 86.1% on the BCI-2a 2008 dataset, outperforming other state-of-the-art approaches. ©2024 IEEE.

关键词： Brain computer interface

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Delivery Drone Routing under Load dependent Flight Speed based on Integer Quadratic programming 4

Delivery Drone Routing under Load dependent Flight Speed bas...

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4th international Symposium on Advanced Technologies and Applications in the Internet of things, ATAIT 2022

作者： Nishira, Mao Nishikawa, Hiroki Kong, Xiangbo Tomiyama, Hiroyuki Graduate School of Science and Engineering Ritsumeikan University Shiga Japan Graduate School of Information Science and Technology Osaka University Osaka Japan

Drone delivery has been attracting attention, and many companies are focusing on this as a means to solve logistics issues. In order to deliver packages by drone, it is necessary to optimize the route in advance. So far, drone routing problems that consider changes in flight speed due to load have been proposed. this routing problem is called the FSVRP. An Integer Linear programming (ILP) approach to the FSVRP was proposed in the past. In this paper, to obtain a more accurate solution than the ILP approach, we propose an Integer Quadratic programming (IQP) approach. Experiments show that the proposed method is effective compared to stateof- the-art methods. © 2022 Copyright for this paper by its authors.

关键词： Drones

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