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检索条件"主题词=Visual process modeling"

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Application of visual recognition technology in machine learning-driven waste sorting

Application of visual recognition technology in machine lear...

引用

2024 International Conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Zhang, Jihaolei School of Maynooth Fuzhou University Fujian Fuzhou China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

In today's society, the output of garbage is more and more, and people are indispensable for the management and classification of garbage. The recycling of garbage is also a hot topic today. The project will propose an artificial intelligence garbage classification system based on machine learning, the data set contains many photo sets, the data set has been divided into many detailed categories, such as battery class, clothing class, cardboard class, etc. The machine learning model will learn classification through the huge data set, the model will also introduce cameras, the identified garbage is photographed and stored. The model will then call the photos that have been stored, and then identify and classify the output. Our goal is to accurately identify garbage and classify them, and people can quickly divide garbage through this model, which can be better and more targeted to take garbage for reuse, and has better help for the preparation work in the field of garbage reuse and garbage classification. © 2025 SPIE.

关键词： Data modeling Machine learning Education and training Mathematical optimization Performance modeling Deep learning Systems modeling visualization Image processing visual process modeling

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Comparative study on green logistics based on neural network word embedding 4

Comparative study on green logistics based on neural network...

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2024 4th International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Chen, Meng School of Management Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9781510686304

Green logistics present rapid development trend in China, this study combined with academic platforms and media platforms, using neural network word2vec word embedded algorithm to carry out comparative study of green logistics, choose China network and China logistics and purchasing network as a research object, to collect "green logistics" corpus, build academic platforms and media platforms two corpus and word2vec modeling. On this basis, compare the top 20 words with the top 20 words most similar to "green logistics" with the attention topics of the two platforms. Then, using the t-SNE algorithm, visually displaying and comparing through the *** package, and the results show that there are differences between green logistics in academic and media platforms. This study innovatively uses the word2vec algorithm to compare the differences between academic platforms and media platforms in green logistics from the semantic level, thus providing a new perspective for green logistics research. © 2025 SPIE.

关键词： Education and training Carbon Neural networks visualization Industry Data modeling Evolutionary algorithms Reflection visual process modeling Transportation

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Attention-mechanism-based model for micro-defect detection on cigarette packages with performance optimization 2

Attention-mechanism-based model for micro-defect detection o...

引用

2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Ban, Cuiren Li, Qingqi Zhong, Shile China Tobacco Guangxi Industrial Co. Ltd. Guangxi Nanning530000 China

ISBN: (纸本)9781510689053

Micro-defect detection in cigarette packaging is critical for ensuring product quality and maintaining brand reputation. However, traditional defect detection methods are limited in their ability to efficiently and accurately identify complex defects, especially under diverse operational conditions. This study presents an attention-mechanism-based model designed to enhance micro-defect detection in cigarette packages. The proposed method utilizes advanced convolutional neural networks (CNNs) combined with attention mechanisms to enhance feature extraction and defect localization. Experimental results confirm the model's superior performance in detection accuracy, processing speed, and robustness, surpassing existing state-of-the-art techniques. These findings demonstrate the approach's potential for industrial applications and its significant contribution to advancing defect detection technologies. © 2025 SPIE.

关键词： Object detection Defect detection Performance modeling Feature fusion Packaging Mathematical modeling Education and training Mathematical optimization visual process modeling Convolutional neural networks

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Use neural Networks to Recognize Students' Handwritten Letters and Incorrect Symbols

Use neural Networks to Recognize Students' Handwritten Lette...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the four, some students may write incorrect symbols or options that do not exist. In this paper, five classifications were set up - four for possible correct options and one for other incorrect writing. This approach takes into account the possibility of non-standard writing options. © 2025 SPIE.

关键词： Education and training Machine learning Data modeling Neural networks Image classification visual process modeling Computer vision technology Deep learning Performance modeling Transformers

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Markerless Feature-Registration-Based Image processing Framework for Oil Palm Fresh Fruit Bunch Robotic Harvester

Markerless Feature-Registration-Based Image Processing Frame...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Tang, Tiong Yew Ho, Chian C. Nadarajan, Geetha School of Engineering and Technology Sunway University Jalan Universiti No.5 Bandar Sunway Selangor Darul Ehsan 47500 Malaysia Dept.of Electrical Engineering National Yunlin University of Science and Technology Taiwan Department of Marketing Strategy & Innovation Sunway Business School Sunway University Jalan Universiti No.5 Bandar Sunway Selangor Darul Ehsan 47500 Malaysia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

Malaysia's oil palm industry faces many challenges in sustaining manual oil palm harvesting *** work investigates an effective oil palm Fresh Fruit Bunch (FFB) image processing method for robot harvesting *** research explores the proposed image processing method that first detects the Fresh Fruit Bunch (FFB) category which involves 6 different categories of FFB growth stages and then detects its 6D pose estimation for ***, this research proposes a novel image processing framework that utilises the convolutional neural network deep learning classification and is followed by markerless feature-registration-based oil palm FFB for 6D pose estimation with the public FFB ***, this work introduced view obstruction to the public FFB dataset as noise for practical robot harvester applications in plantation field ***, the experiment results show the proposed model can maintain a high F1 score performance up until 70% of view obstruction before the F1 score performance is reduced. © 2024 SPIE.

关键词： RGB color model Pose estimation 3D modeling Image processing visual process modeling Education and training 3D image processing Image classification visualization Robotics

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Artificial Intelligence-based disinformation discovery for social networks 5

Artificial Intelligence-based disinformation discovery for s...

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5th International Conference on Signal processing and Computer Science, SPCS 2024

作者： Zhu, Zhiying Zhujiang College South China Agricultural University Guangdong Guangzhou510900 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this era of rapid development of the Internet, disinformation, as a new public opinion phenomenon, is generated and disseminated through the Internet. The most important feature that distinguishes it from general disinformation is that the means of dissemination have changed from verbal transmission to the dissemination of textual information or multimedia information on the Internet. Its untruthful and deceptive information content has a great impact on society and people's lives. The wide range and fast speed of dissemination of false information makes it difficult to judge its dissemination channels and the scope of dissemination, and therefore it is impossible to detect false information in an effective and timely manner. In this paper, through the simulation of the BSS virtual community propagation model, the SIR model, we can discover its propagation mode in social networks. After getting a rough idea of the pattern of disinformation dissemination. From the perspective of artificial intelligence through the feature extraction model detection method and Web content filtering model method to effectively detect false information, to reduce the negative impact of false information dissemination on society. © 2025 SPIE.

关键词： Tunable filters Social networks Internet visual process modeling Artificial intelligence Feature extraction Optical filters Data transmission Information visualization Virtual reality

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Explainable deep learning for rib fracture detection in chest x-rays

Explainable deep learning for rib fracture detection in ches...

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SPIE Medical Imaging

作者： Charles Roberts Connor Rutherford Yiyang Wang Kieran Penneau Mohamad Mehdi Derek Riley Milwaukee School of Engineering (United States)

ISBN: (纸本)9781510685925

Detecting rib fractures is a crucial and specialized task in the medical field due to the complications that can arise from untreated fractures, such as symptom relief challenges, preventing further issues, increased healthcare costs, and overall patient health. Deep learning models have been developed to enhance accurate rib fracture detection, provide support towards helping radiologist efficiency, and provide as an option in situations where radiologists are not immediately available. However, the reliability of these models has often been questioned, as previous studies have reported only overall accuracy without assessing the model's focus on the correct regions, thus lacking explainability. In this study, we aim to improve overall accuracy and elucidate the model's focus when detecting rib fractures. We utilized GRAD-CAM to visualize the regions of interest identified by the model. We used the DenseNet121 architecture, performed data preprocessing, including image cropping and data augmentation, and conducted extensive experimentation and hyperparameter tuning. Our optimized model achieved 74.9% accuracy on the CheXpert testing dataset, 84.5% validation accuracy on the Medical College of Wisconsin (MCW) dataset, and an AUROC score of 0.82. Grad-CAM visualization revealed issues with the model's focus and localization abilities, highlighting current limitations and providing areas for future research. Our work demonstrates the potential of deep learning for automated rib fracture detection in chest X-rays and underscores the need for further improvements in model explainability.

关键词： visual process modeling Data modeling visualization Deep learning Performance modeling Chest imaging Education and training Lung RGB color model Medical imaging

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Zero-shot OCT segmentation for volumetric and live-streaming data

Zero-shot OCT segmentation for volumetric and live-streaming...

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BiOS

作者： Kaixiang Zhang Haoran Zhang Qi Lan Zesen Chen Shuo Yin Yongqi Wei Zehao Wang Jianlong Yang Shanghai Jiao Tong Univ. (China)

ISBN: (纸本)9781510683587

Deep-learning-based OCT segmentation methods are accurate but less flexible (need re-labeling and re-training) for unseen and varying Regions Of Interest (ROIs), which hinders their applications in many scenarios, such as surgical and navigation. Inspired by the recent emerging large vision models and their zero-shot capabilities (directly applicable to unseen ROIs), we propose a zero-shot OCT segmentation method. For volumetric and live-streaming data, it only requires the box or point prompts of ROIs at the first frame, then subsequent frames could be segmented automatically. To test the zero-shot capabilities of our method, we used data from completely different sources as the training set and test set in our experiments. Its superiority has been verified on publicly-available OCT datasets and the datasets collected via our home-built OCT systems. Along with the excellent generalization capability, our method also combines the high accuracy and real-time advantages of deep learning, which makes it promising to become a general-purpose analysis tool in the graphical user interface of OCT devices.

关键词： Optical coherence tomography Image segmentation Education and training visual process modeling Feature extraction Data modeling Algorithm development Biomedical applications Deep learning Imaging systems

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GhostTuDNet: accelerating tumor detection on CPUs with a compact and efficient model

GhostTuDNet: accelerating tumor detection on CPUs with a com...

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SPIE Medical Imaging

作者： Mingzhe Hu Xiaofeng Yang Emory Univ. (United States)

ISBN: (纸本)9781510685925

Early detection of tumors, particularly intracranial and breast tumors, is essential for improving patient outcomes. Current deep learning-based algorithms have shown remarkable success in identifying tumors but often require high computational power, limiting their use in resource-constrained environments. To address this challenge, we developed GhostTuDNet, a compact and efficient model based on GhostNetV3, specifically optimized for CPU operation. The model was trained using knowledge distillation with ViG as the teacher model and was evaluated on two datasets: breast ultrasound and brain MRI. GhostTuDNet achieved a Precision of 93.5% and a Recall of 92.7% on the Intracranial Tumor dataset, closely matching the performance of the ViG model, which recorded a Precision of 94.2% and Recall of 93.4%. Despite having only 6.1M parameters and an inference time of 8.7ms, GhostTuDNet's performance was comparable to the much larger ViG model, which has 86.8M parameters and a 231ms inference time. In contrast, MobileNetV3, another compact model, showed significantly lower performance, with a Precision of 89.8% and Recall of 88.2% on the Intracranial Tumor dataset. These results highlight GhostTuDNet's ability to balance compactness and accuracy, making it a viable solution for tumor detection in environments where computational resources are limited. Future work will explore the impact of class rebalancing techniques and evaluate GhostTuDNet across a broader range of datasets and platforms.

关键词： Tumors Cancer detection Performance modeling Data modeling Breast Brain Education and training Ultrasonography Tumor growth modeling visual process modeling

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Pediatric osteopenia prediction and interpretation in wrist x-rays

Pediatric osteopenia prediction and interpretation in wrist ...

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SPIE Medical Imaging

作者： Chelsea E. Harris Lingling Liu Sokratis Makrogiannis Delaware State Univ. (United States)

ISBN: (纸本)9781510685925

Osteopenia, a condition marked by reduced bone mineral density (BMD), affects millions globally. Patients diagnosed with osteopenia typically have a BMD valued below the normal reference measure, but not low enough to meet the diagnostic criteria for osteoporosis. Deep learning techniques, and Convolutional Neural Networks (CNNs) in particular, have revolutionized various fields, including medicine and diagnostics. This class of deep learning algorithms, excel in image analysis and pattern recognition tasks. In medical diagnostics, CNNs have shown remarkable potential for accurately detecting diseases in various medical imaging modalities, such as Xrays, MRIs, and CT scans. These networks can identify subtle patterns and anomalies that may be challenging for human observers to detect. Recently, research has increasingly focused on machine learning interpretability and explainable AI, aiming to provide insights into the decision-making processes of these algorithms. In this work, we fine-tuned deep network models on a pediatric wrist X-ray dataset to classify osteopenia versus healthy bone patterns. We also employed a decision interpretability algorithm to understand the CNNs’ predictions. Our results indicate that CNNs can effectively learn bone tissue patterns, achieving an accuracy of over 95% in classifying osteopenia in pediatric wrist X-rays. The visual explanations may offer valuable insights into the key areas within the X-ray images that contribute most to the deep network predictions.

关键词： X-rays Bone Education and training Deep learning X-ray imaging Data modeling Image classification visualization Machine learning visual process modeling

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