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Weave defect detection in jute manufacturing value chain using artificial intelligence and computer vision

International Journal of System Assurance Engineering and Management 2025年 1-9页

作者： Vadivel, S.M. Madhumitha, R. Nair, Yash Aniruddha, P. Manish Mithun, S.P. Sarathy, Yashwanth VIT Business School Operations and Quantitative Techniques Division Vellore Institute of Technology Chennai Vandalur-Kelambakkam Road Tamil Nadu Chennai600127 India School of Computer Science and Engineering Vellore Institute of Technology Vandalur-Kelambakkam Road Tamil Nadu Chennai600127 India

This study aims to introduce a cutting-edge method for weave defect detection using Artificial intelligence (AI) and computer vision (CV) technologies. By employing a Convolutional neural network (CNN) trained on a comprehensive dataset (Python programming codes mentioned in appendix) of textile images with annotated defects, the system automates the identification and classification of fabric irregularities, such as stains and tears. Testing in an industrial setting has shown that this AI-driven approach significantly outperforms traditional manual inspections, enhancing defect detection accuracy and production efficiency. The research highlights the potential of AI and CV to revolutionize Quality control (QC) processes in jute manufacturing industries and beyond. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The division of Operation and Maintenance, Lulea University of technology, Sweden 2025.

关键词： computer control

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Personality Traits of Individuals at Risk of Social Media Radicalisation

Personality Traits of Individuals at Risk of Social Media Ra...

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2025 IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media, CI-NLPSoMe Companion 2025

作者： Underhaug, Lars-Magnus Gamback, Bjorn Norwegian University of Science and Technology Dpt of Computer Science Trondheim Norway H5P Division at D2L Tromso Norway

ISBN: (纸本)9798331519742

This work explores personalities of individuals at risk of Islamist radicalisation. Previous studies have successfully identified radicalised individuals on social media. However, a more prophylactic approach to counter radical communities would be to detect individuals prior to getting radicalised. A manually annotated dataset of 15,195 tweets from 259 persons believed to be at risk of radicalisation was classified according to Big 5 personality traits and compared to the same number of non-radical users. The results show that risk users can successfully be identified and exhibit distributional differences compared to non-radical users for Extraversion and Openness. © 2025 IEEE.

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GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles 27th

GUARDEV: Guided Unified Adaptive Response for Defending Elec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mondal, Koustav Kumar Das, Debasis Inter-Disciplinary Research Division - IoT and Applications Indian Institute of Technology Jodhpur Rajasthan Jodhpur India Department of Computer Science Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur India

ISBN: (纸本)9783031783821

In the rapidly evolving landscape of electric vehicles (EVs), integrating the Internet of Vehicles (IoV) has opened up new dimensions in automotive technology. While enhancing vehicle performance, safety, and security, this advancement also exposes EVs to heightened cyber threats. To address this critical concern, we introduce GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles, a novel Machine Learning (ML)-enhanced conflict resolution-based ensemble Intrusion Detection System (IDS) architecture. GUARDEV leverages the power of adaptive ML techniques to provide a unified and guided response in safeguarding EVs against sophisticated cyber-attacks. Our framework evaluates the effectiveness of three prominent ML models - XGBoost, Random Forest, and Extra Trees - in detecting specific categories of cyber threats. By harnessing the prediction confidence scores from these models, GUARDEV offers a nuanced and intelligent approach to recognizing and thwarting diverse cyber-attacks. Our research, grounded in rigorous experimentation using public IoV security datasets, demonstrates GUARDEV’s exceptional accuracy of 99.47% in intrusion detection. This remarkable achievement not only underscores the potential of ML in enhancing vehicular cybersecurity but also establishes GUARDEV as a powerful tool for creating safer, more secure EVs. GUARDEV paves the way for robust protection of internet-connected electric vehicles in today’s digital age by providing a guided, unified, and adaptive response to cyber threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

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Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

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光：科学与应用（英文版） 2025年第2期14卷 411-421页

作者： Waqas W.Ahmed Haicheng Cao Changqing Xu Mohamed Farhat Muhammad Amin Xiaohang Li Xiangliang Zhang Ying Wu Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia College of Engineering Taibah UniversityMadinah 42353Saudi Arabia Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Division of Physical Science and Engineering King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Computer Science and Engineering University of Notre DameNotre DameIN 46556USA

We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon *** embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon(a-Si)layer,we achieve a significant enhancement of light *** enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes,requiring precise tuning of plasmon resonances to match the absorption region of the silicon active *** facilitate the device design and improve light absorption without increasing the thickness of the active layer,we develop a deep learning framework,which learns to map from the absorption spectra to the design *** inverse design strategy helps to tune the absorption for selective spectral *** optimized design surpasses the bare silicon planar device,exhibiting a remarkable enhancement of over 100％.Experimental validation confirms the broadband enhancement of light absorption in the proposed *** proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells,photodetectors,and optical filters.

关键词： silicon learning absorption assisted broadband enhanced films machine metascreen plasmonic

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An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks

An Intelligent Reinforcement Learning–Based Method for Thre...

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作者： Saeed, Muhammad Yousaf He, Jingsha Zhu, Nafei Farhan, Muhammad Dev, Soumyabrata Gadekallu, Thippa Reddy Almadhor, Ahmad College of Computer Science Beijing University of Technology Beijing China Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal Pakistan School of Computer Science University College Dublin Dublin Ireland Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia

Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real-time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL-based approach makes it well suited for securing mission-critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on-device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next-generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration. © 2024 John Wiley & Sons Ltd.

关键词： Reinforcement learning

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Heralded optical entanglement distribution via lossy quantum channels: a comparative study

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Optics Express 2025年第6期33卷 12459-12474页

作者： Zo, Wan Chin, Seungbeom Kim, Yong-Su Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Okinawa Institute of Science and Technology Graduate University Okinawa904-0495 Japan Division of Quantum Information Korea University of Science and Technology KIST School Seoul02792 Korea Republic of

Quantum entanglement serves as a foundational resource for various quantum technologies. In optical systems, entanglement distribution relies on the indistinguishability and spatial overlap of photons. Heralded schemes play a crucial role in ensuring the reliability of entanglement generation by detecting ancillary photons to signal the creation of desired entangled states. However, photon losses in quantum channels remain a significant challenge, limiting the distance and capacity of entanglement distributions. This study suggests three heralded schemes for distributing multipartite Greenberger-Horne-Zeilinger (GHZ) states via lossy quantum channels. These schemes differ both qualitatively in their network architecture (centralized or decentralized) and photon source requirements (Bell states or single-photons), and quantitatively in their success probabilities and heralding efficiency. Through comprehensive analysis incorporating both practical implementation considerations and theoretical performance metrics, we find that each scheme offers distinct advantages depending on the number of parties, channel distance, and security requirements. The decentralized scheme proves particularly advantageous for networks requiring balanced information distribution, while centralized schemes may offer better performance for smaller networks. This analysis provides insights into designing resilient heralded circuits for quantum information processing over lossy channels, considering both architectural constraints and performance requirements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Quantum channel

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Quantum Field Current Modelling in a Semiconductor 3rd

Quantum Field Current Modelling in a Semiconductor

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Vaish, Arti Parthasarathy, Harish Department of Computer Science and Engineering School of Engineering and Technology O P Jindal University CG Raigarh Punjipathra India Electronics and Communication Division Netaji Subhas University of Technology Dwarka New Delhi India

ISBN: (纸本)9789819719426

In this paper, we model the sea of electrons and holes within a semiconductor using the second quantized electron-positron Dirac wave operator field interacting with an applied classical electromagnetic field, a quantum photon field which can be regarded as quantum fluctuations of the electromagnetic field, and a noisy quantum photon field coming from the bath outside the semiconductor. Essentially, in this model, the Dirac wave field interacts and evolves along with the quantum photon field in the given background classical electromagnetic field and the given quantum noisy operator electromagnetic field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photons

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CASEMark: A hybrid model for robust anatomical landmark detection in multi-structure X-rays

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Journal of King Saud University - computer and Information sciences 2025年第3期37卷 1-18页

作者： Huang, Zhen Zhou, Xiaoqian He, Xu Wei, Yangbo Yang, Wenkai Wang, Suhua Sun, Xiaoxin Li, Han School of Computer Science and Technology University of Science and Technology of China Hefei 230026 China School of Information Science and Technology Eastern Institute of Technology Ningbo 315200 China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Suzhou 230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou 215123 China Shanghai Jiao Tong University Shanghai 200030 China Shanghai Tech University Shanghai 201210 China Computer Department Changchun Humanities and Sciences College No. 1488 Boshuo Road Jingyue High-Tech Industrial Development Zone Jilin Province Changchun 130117 China School of Information Science and Technology Northeast Normal University Changchun 130117 China Computer Aided Medical Procedures (CAMP) School of Computation Information and Technology Technische Universitaet Muenchen Boltzmannstraße 3 Garching bei München 85748 Germany

Anatomical landmark detection is crucial in medical image analysis, facilitating accurate diagnosis, surgical planning, and treatment evaluation. However, existing methods often struggle to simultaneously capture global context and local details while exhibiting limited generalization across diverse datasets and imaging modalities. To relieve this, we propose a hybrid model that leverages convolutional operations to capture local information and a Swin Transformer to enhance global context. Specifically, we introduce a novel U-shaped architecture, termed Convolutional Attention Swin Enhanced Landmark Detection Network (CASEMark). CASEMark integrates three key innovations: (1) a Convolutional Attention Swin Transformer module (CAST) that integrates transformer-based global context modeling with convolutional operations for local feature extraction, (2) an Enhanced Skip Attention Module (ESAM) enabling adaptive feature fusion between encoder and decoder pathways, and (3) a multi-resolution heatmap learning strategy that aggregates information across scales. This approach effectively balances global-local feature extraction with robust cross-modality generalization. Extensive experiments on four public datasets demonstrate the superiority of CASEMark. The code and datasets will be made publicly available. © The Author(s) 2025.

关键词： Anatomical landmark detection CAST ESAM Multi-resolution learning Swin transformer

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN computer science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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