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3rd international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with the 25th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

3rd International Workshop on Multiscale Multimodal Medical ...

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3rd international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with the 25th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

ISBN: (纸本)9783031188138

The proceedings contain 12 papers. The special focus in this conference is on Multiscale Multimodal Medical Imaging. The topics include: Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation;Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans;visual Modalities Based Multimodal Fusion for Surgical Phase recognition;cross-Scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological Images;vessel Segmentation via Link Prediction of Graph Neural Networks;A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset;Liver Segmentation Quality Control in Multi-sequence MR Studies;pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI;gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation;Learning-Based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study.

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The Fast K Algorithm Based on a Fixed Grid Method 3

The Fast K Algorithm Based on a Fixed Grid Method

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3rd international conference on Artificial Intelligence, Robotics and Control, AIRC 2022

作者： Jan, Gene Eu Su, Kuan-Lin Hsieh, Hui-Ching Luo, Chaomin Tainan National University of the Arts Tainan City Taiwan National Taipei University Department of Electrical Engineering New Taipei City Taiwan Mississippi State University Department of Electrical and Computer Engineering Mississippi State United States

ISBN: (纸本)9781665459471

k-Nearest Neighbor (k-NN) is a well-known instance-based learning algorithm;widely used in pattern recognition. A classifier can generate highly accurate predictions if provided with sufficient training instances. Thus, it plays a pivotal role in many fields. However, though its accuracy can improve with more data, the need for computational resources increases as well. In this paper, we propose a novel approach which pre-partitions instance space into smaller cells in order to reduce computational cost and greatly reduce the time complexity. Assume every instance is mapped to a point in the d-{dimensional} Euclidean space and a training set D contains n training instances. Given a query instance, the brute force k-{NN} algorithm has the O(nd) time complexity for predicting the class of a query instance. This algorithm can improve the time complexity. © 2022 IEEE.

关键词： pattern recognition

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An Efficient Scheme for Facial Expression recognition using Random Forest and CNN 3

An Efficient Scheme for Facial Expression Recognition using ...

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Susan Jacob, Sharon Mahatma Gandhi University Faculty School of Artificial Intelligence and Robotics Kerala Kottayam India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

As humans, we use our facial expressions to communicate our thoughts and feelings without saying a word. The recognition of these facial expressions can provide a better idea of people's thoughts or opinions. At present, the high performance of deep learning procedures has influenced the high efficiency of image recognition systems. This paper proposes a Facial Expression recognition System (FER) using Convolutional Neural Network (CNN). We have made use of features such as Gabor wave coefficients, Histogram of Oriented Gradient (HOG), Speed-Up Robust Features (SURF), and Local Binary patterns (LBP). We have used the Random Forest algorithm for the selection of the best features from the extracted feature set, accordingly, the learning procedure can be faster. The widely used CK+ database can be used for the appraisal of our proposed system. We have also compared our proposed method against similar FER systems and the consequences are satisfactory. © 2022 IEEE.

关键词： Local binary pattern

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Face Mask Detection and Face recognition of Unmasked People in Organizations 3

Face Mask Detection and Face Recognition of Unmasked People ...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Bennur, Sushma Bharadwaj, Dhanyaa Anand, Piyush Premananda, B.S. Rv College of Engineering Dept. of Ete Bengaluru India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

With the onset of the global pandemic of COVID-19 and with the infection rates on the rise, face masks have become a necessity in public spaces to prevent further spread of the virus. Therefore, it is necessary to develop a system which enforces the need to wear a facemask in public places and identify the ones who are without it. This paper describes a face mask detection and face recognition system. Masked faces are detected using Convolutional Neural Networks and individuals without a face mask are recognized using the LBPH algorithm. The models use OpenCV and TensorFlow and are trained and tested. For doing so, two sets of datasets are used, where the majority of the images are obtained from the Kaggle dataset. 20% of the images are considered for training purposes and other 80% is for testing. The experimental results have confirmed an average face mask detection and face recognition rate of 99%. Accuracy and loss plots for the proposed model are compared with the existing model. Testing for mask detection and facial recognition of masked and unmasked faces are done elaborately. © 2022 IEEE.

关键词： Local binary pattern

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Bento Packaging Activity recognition with Convolutional LSTM Using Autocorrelation Function and Majority Vote 1

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3rd international conference on Activity and Behavior Computing (ABC)

作者： Fujii, Atsuhiro Yoshida, Kazuki Shirai, Kiichi Murao, Kazuya Ritsumeikan Univ Grad Sch Informat Sci & Engn 1-1-1 Nojihigashi Kusatsu Shiga 5258577 Japan

ISBN: (数字)9789811903618

ISBN: (纸本)9789811903618;9789811903601

This paper reports Bento Packaging Activity recognition Challenge by team "RitsBen" held in the international conference on Activity and Behavior Computing (ABC 2021). Our approach used an autocorrelation function in the preprocessing to isolate the data since the dataset was given with repetitive activity. We then use a model that implements convolutional layers and LSTM. The final decision is made by majority vote using sigmoid predictions output from all body parts. The loss is calculated using BCEWithLogitsLoss for each body part. The evaluation results showed that average accuracy of 0.123 was achieved among subjects 1, 2, and 3 in leave-one-subject-out manner. However, we did not achieve high accuracy as the possibility that the extraction of repetitive actions was not correct.

关键词： pattern recognition

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A recognition technology for the output pattern of likely metamorphic relation of neutron diffusion program 3

A recognition technology for the output pattern of likely me...

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3rd international conference on Electronics and Communication;Network and Computer Technology (ECNCT)

作者： Wen, Zhaoyu Yang, Xiaohua Yan, Shiyu Li, Meng Univ South China Sch Comp Sci Hengyang 421001 Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

The metamorphic test is a method to alleviate the unexpected value of the Oracle problem. The key point is the identification of the metamorphic relations. The identification of the metamorphic relations of scientific calculation programs is also a complex problem, and the likely of the metamorphic relation of the program can provide enlightening information for the identification of the metamorphic relations. The likely metamorphic relation can be regarded as the implicit expression of the input pattern and output pattern. This paper proposes an output pattern recognition technology based on the likely metamorphic relations of GEP. The technology is mainly aimed at the core neutron diffusion calculation program. The input pattern of the program, and then generate input data and run the program. Finally, in the corresponding output data results, through GEP data mining technology, the output pattern expressed in a variety of functional forms is obtained, which is further compared with analytical solutions and verified to be reliable likely metamorphic relations.

关键词： metamorphic testing metamorphic relation likely metamorphic relation GEP neutron diffusion program

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A Novel Multi-view Fuzzy Clustering Algorithm Based on Fuzzy C-Means 1

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3rd international conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Lin, Ze Chen, Yixiang National Engineering Research Center of Trustworthy Embedded Software East China Normal University Shanghai200062 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Fuzzy c-means (i.e., FCM) is a representative clustering method that is widely used in machine learning and pattern recognition. It can describe the degree of fuzziness of objects to clusters using memberships, but it cannot assess the degree of fuzziness of the clustering results. Motivated by such issues, in this paper, we propose a multi-view fuzzy clustering method based on FCM and a fuzzy assessment is introduced to assess the fuzziness of clustering results. Specifically, we constructed a multi-view FCM to obtain a reasonable membership matrix by fully integrating multi-view information. Based on this, we propose a fuzzy assessment method to evaluate the fuzziness of the clustering results and mark the objects between the different clusters (i.e., the object has the feature of multiple clusters). The experimental result shows that the proposed multi-view fuzzy clustering method achieves better performance than several related clustering methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： pattern recognition

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Virtual resurrection leveraging 3D modeling for digital preservation of an ancient educational institution 3

Virtual resurrection leveraging 3D modeling for digital pres...

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2024

作者： Anuj, Sreedharan Santheep, Wilson Suthakaran Alfred Daniel, John William Santhosh, Rajendran Parthasarathy, Velusamy Karpagam Academy of Higher Education Tamil Nadu Coimbatore641021 India

Nowadays, advanced 3D modeling software lets us see, explore and learn about historical sites like never before. Preserving our cultural heritage for future generations means documenting ancient sites accurately and enhancing user experience in virtual visits and education. This paper describes a method to digitally reconstruct and restore Nalanda University, a UNESCO World Heritage site. We collect data from historical records, old maps and photographs, then segregate the ruins based on their importance and features. We use Blender and Maya to create and optimize 3D models to get an accurate virtual representation of Nalanda. This digital model will give us valuable insights into the university's history and culture, a resource for researchers, historians and the public. © 2024 Copyright for this paper by its authors.

关键词： Digital elevation model

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An Encoder-Decoder Approach to Offline Handwritten Mathematical Expression recognition with Residual Attention 3rd

An Encoder-Decoder Approach to Offline Handwritten Mathemati...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Lin, Qiqiang Wang, Chunyi Bi, Ning Suen, Ching Y. Tan, Jun School of Mathematics Sun Yat-Sen University Guangzhou510275 China Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510275 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031090363

Unlike handwritten numeral recognition and handwritten text recognition, the recognition of handwritten mathematical expressions is more difficult because of their complex two-dimensional spatial structure. Since the "watch, attend and parse (WAP)" method was proposed in 2017, encoder-decoder models have made significant progress on handwritten mathematical expression recognition. Our model is improved based on the WAP [1] model. In this paper, the attention module is reasonably added to the encoder to make the extracted features are more informative. The new network is called Dense Attention Network (DATNet), which allows for an adequate extraction of the structural information from handwritten mathematical expressions. To prevent the model from overfitting during the training process, we use label smoothing [2] as the loss. Experiments showed that our model (DATWAP) improved WAP expression recognition from 48.4%/46.8%/45.1% to 54.72%/52.83%/52.54% on the CROHME 2014/2016/2019 test sets. © 2022, Springer Nature Switzerland AG.

关键词： Decoding

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3rd BenchCouncil international Symposium on Intelligent Computers, Algorithms, and Applications, IC 2023

3rd BenchCouncil International Symposium on Intelligent Comp...

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3rd BenchCouncil international Symposium on Intelligent Computers, Algorithms, and Applications, IC 2023

ISBN: (纸本)9789819700646

The proceedings contain 26 papers. The special focus in this conference is on Intelligent Computers, Algorithms, and Applications. The topics include: KGCN-DDA: A Knowledge Graph Based GCN Method for Drug-Disease Association Prediction;machine Learning for Time-to-Event Prediction and Survival Clustering: A Review from Statistics to Deep Neural Networks;label-Independent Information Compression for Skin Diseases recognition;3D Approach Trajectory Optimization Based on Combined Intelligence Algorithms;A-SMGCS: Innovation, Applications, and Future Prospects of Modern Aviation Ground Movement Management System;an Intelligent Image Segmentation Annotation Method Based on Segment Anything Model;ParticleNet for Jet Tagging in Particle Physics on FPGA;application of Graph Neural Networks in Dark Photon Search with Visible Decays at Future Beam Dump Experiment;second-Order Gradient Loss Guided Single-Image Super-Resolution;Neutrino Reconstruction in TRIDENT Based on Graph Neural Network;charged Particle Reconstruction for Future High Energy Colliders with Quantum Approximate Optimization Algorithm;A Levy Scheme for User-Generated-Content Platforms and Its Implication for Generative AI Providers;Moving Beyond Text: Multi-modal Expansion of the Toulmin Model for Enhanced AI Legal Reasoning;The Worldwide Contradiction of the GAI Regulatory Theory Paradigm and China’s Response: Focus on the Theories of Normative Models and Regulatory Systems;Intelligent Forecasting of Trademark Registration Appeal with TF-IDF and XGBoost;review of Big Data Evidence in Criminal Proceedings: Basis of Academic Theory, Practical pattern and Mode Selection;The Implementation and Optimization of FFT Calculation Based on the MT-3000 Chip;EDFI: Endogenous Database Fault Injection with a Fine-Grained and Controllable Method;diffusion Probabilistic Models for Underwater Image Super-Resolution;classification Method for Ship-Radiated Noise Based on Joint Feature Extraction;Forecasting the Price of Bitcoin

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