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Measurement of Imaging Indexes of Distal Radius based on deep Learning Key Point Detection 23

Measurement of Imaging Indexes of Distal Radius based on dee...

引用

9th international conference on computing and Artificial Intelligence, ICCAI 2023

作者： Jiachao, Niu Xin, Zhang Xufeng, Ling Yang, Jie Yin, Yong Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road China Department of Orthopedics Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences No.1 Chengbei Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

the measurement of imaging indexes of distal radius is the basic work of diagnosis and recovery evaluation. Due to the problems of large morphological differences and insufficient clarity in radial images, the accurate measurement of indexes relies heavily on the clinical experience of doctors, with defects such as low accuracy and poor consistency. In order to solve the above problems, this paper proposes an intelligent technology to achieve accurate measurement of imaging indicators of the distal radius. Firstly, 12 key points needed for distal imaging indexes were determined: radius left vertex, radius right vertex, radius left neck point, radius right neck point, radius left lower point, radius right lower point, ulna left vertex, ulna right vertex, ulna left neck point, right neck point, ulna left lower point, ulna right lower point. then, the deep convolutional neural network was used to estimate the geometric transformation features and regress the thermal map of the feature points, so as to achieve accurate positioning of the 12 feature points. Finally, each imaging index was calculated accurately by the preset computational method. the experimental results show that the proposed measurement method has advantages of high accuracy, good consistency and fast speed. the test accuracy is higher than the manual measurement results of inexperienced doctors, and the network model has good robustness and generalization ability. this method has practical value and can be applied in the field of medical imaging to help doctors improve the efficiency of diagnosis. © 2023 Copyright held by the owner/author(s).

关键词： Diagnosis

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Automatic Route Selection of Railway Station Based on Genetic Algorithm 12

Automatic Route Selection of Railway Station Based on Geneti...

引用

12th international conference on Transportation and Traffic Engineering, ICTTE 2023

作者： Yang, Kun Ma, Liang Guo, Jin Yang, Yang Yang, Ming School of Information Science and Technology Southwest Jiaotong University Chengdu China The Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing China Institute of Signal & Communication Design CRSC Research & Design Institute Group Co. Ltd Beijing China

ISBN: (纸本)9781839539954

To realize the automatic execution of the train station operation plan and ensure the safety, efficiency and orderliness of the station operations, the key is to realize the automatic selection of the routes required by the station operation plan. the operation routes corresponding to the station operation plans were analysed and the network topology of the station yard through a meta-mode was built taking a certain station as the background. A mathematical model of automatic route selection of station operations, with the objective of minimizing the total delay time of all operations and the total travel time of the routes, considering resource conflicts between operation routes and the constraint conditions of the operation plans. the route selection problem was abstracted as a 0-1 integer model, and a heuristic optimization algorithm suitable for solving the model was proposed based on a genetic algorithm. the results through example analysis show that the algorithm can get the optimal solution in a short time, which meets the real-time requirements of route selection;Different delay factors and travel coefficients will get different optimal route schemes, and the route scheme obtained by focusing on delay factors is better. © the Institution of Engineering & Technology 2023.

关键词： Genetic algorithms

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Research on Improved Faster R-CNN in Stacked Artifact recognition

Research on Improved Faster R-CNN in Stacked Artifact Recogn...

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Information and Communication Technology (ICTech), international conference of

作者： Weiguang Han Xuesong Han University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

In order to improve the sorting efficiency of workpieces, improve the sorting quality, reduce the labor intensity of workers and improve the working environment, and at the same time meet the needs of small batch, multi-variety and mixed production, this paper studies the improvement of Faster R-CNN for the field of robotic sorting. Application in stacked workpiece recognition. this paper improves the Faster R-CNN and proposes the Bin-Soft-NMS algorithm based on the Soft-NMS algorithm. the dynamic threshold is used to improve the low recall rate of the model and the low score of the adjacent stacked workpiece bounding box caused by the inaccurate threshold. the problem of inhibition. A stacked workpiece dataset was made by using four kinds of workpieces, and the model was trained on the dataset to complete the experiment. the experimental results show that the average detection accuracy of the improved model in the stacked workpiece scene is 86.26%, and the improvement in mild, moderate and severe scenarios is improved respectively. increased by 1.0%, 3.2% and 4.825%, with an average increase of 3%.

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Research on Power Voice Based on WaveNet+LSTM Improvement

Research on Power Voice Based on WaveNet+LSTM Improvement

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Information and Communication Technology (ICTech), international conference of

作者： HanJun Wang MingYun Zhu Qun Wang Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

With the rapid development of speech recognition [1] in recent years, the WaveNet model has also been discovered by more people and applied to speech recognition. In the open source LJSpeech-1.0, AISHELL and thch-30 and other general speech datasets, the model has achieved relatively good recognition results, but in the face of a large number of power voice data, because of the uniqueness and complexity of industry speech, the model is generally performed, so in this paper we have made some improvements to the model, aiming to improve the recognition of power-related speech data. First of all, in view of the shortcomings of the WaveNet network model in power speech recognition, we have improved and optimized it. the output gate structure of the LSTM network model is used to increase the nonlinearity of the model, so as to optimize the structural composition of the new model. We then introduced the core structure of the Reduced Convolution in WaveNet. According to the length of speech in the electric speech dataset, the receptive field of this structure is adjusted, and the sensory field is theoretically analyzed in order to better analyze the speech data. Finally, we experimentally verify the feasibility of applying the improved WaveNet model on the thch-30 dataset and the power-related dataset, and evaluate the trained model.

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Distributed Edge computing System for Vehicle Communication

Distributed Edge Computing System for Vehicle Communication

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12th international conference on Data Science, Technology and Applications, DATA 2023

作者： Pakala, Rinith Kathiriya, Niket Haeri, Hossein Maddipatla, Satya Prasad Jerath, Kshitij Beal, Craig Brennan, Sean Chen, Cindy Computer Science Department University of Massachusetts Lowell 220 Pawtucket St Lowell United States Mechanical Engineering Department University of Massachusetts Lowell Lowell United States Mechanical Engineering Department The Pennsylvania State University University Park United States Mechanical Engineering Department Bucknell University Lewisburg United States

ISBN: (纸本)9789897586644

the development of communication technologies in edge computing has fostered progress across various applications, particularly those involving vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Enhanced infrastructure has improved data transmission network availability, promoting better connectivity and data collection from IoT devices. A notable IoT application is with the Intelligent Transportation System (ITS). IoT technology integration enables ITS to access a variety of data sources, including those pertaining to weather and road conditions. Real-time data on factors like temperature, humidity, precipitation, and friction contribute to improved decision-making models. Traditionally, these models are trained at the cloud level, which can lead to communication and computational delays. However, substantial advancements in cloud-to-edge computing have decreased communication relays and increased computational distribution, resulting in faster response times. Despite these benefits, the developments still largely depend on central cloud sources for computation due to restrictions in computational and storage capacity at the edge. this reliance leads to duplicated data transfers between edge servers and cloud application servers. Additionally, edge computing is further complicated by data models predominantly based on data heuristics. In this paper, we propose a system that streamlines edge computing by allowing computation at the edge, thus reducing latency in responding to requests across distributed networks. Our system is also designed to facilitate quick updates of predictions, ensuring vehicles receive more pertinent safety-critical model predictions. We will demonstrate the construction of our system for V2V and V2I applications, incorporating cloud-ware, middleware, and vehicle-ware levels. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Middleware

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DoppelSearch: A Novel Approach to Content-Based Video Retrieval for AI Challenge HCMC 2023 23

DoppelSearch: A Novel Approach to Content-Based Video Retrie...

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12th international Symposium on Information and Communication Technology, SOICT 2023

作者： Phan Nguyen Huu, Phong Tran Dinh, Khoa Tran Kim Ngoc, Ngan Tran Duc, Luong Le Hoang, Phuc Nguyen Huu, Quyen Phan the, Duy Pham, Van-Hau Information Security Laboratory University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam Faculty of Information Science and Engineering University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam

ISBN: (纸本)9798400708916

Video retrieval, which has been considered as a critical task in the field of computer vision and pattern recognition recently, finds extensive applications in several aspects such as education, entertainment, security, and healthcare. However, it faces challenges due to the complexity of video data, the instability in feature extraction methods, or the semantic disparities between videos and text. In this paper, we present a novel approach for content-based video retrieval, named DoppelSearch, leveraging the CLIP (Contrastive Language-Image Pre-training) model architecture to classify and label video segments, offering users the ability to search for videos based on specific content. Our method capitalizes on the ViT-b/32 model for feature extraction and employs feature embedding, in conjunction with the Faiss library, to enhance search efficiency. Experimental results demonstrate our model's high accuracy and swift retrieval times, promising new opportunities in content-based video retrieval for researchers, developers, and end-users. this paper not only introduces the application of the CLIP and ViT-b/32 models, but also elaborates on the specific feature extraction process and the utilization of Faiss for optimizing video retrieval. the DoppelSearch method represents a significant stride in the field of video retrieval and holds promise for diverse applications across various industries. © 2023 ACM.

关键词： Feature extraction

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A New Multimodal Video Detection Model and Dataset

A New Multimodal Video Detection Model and Dataset

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Information and Communication Technology (ICTech), international conference of

作者： Bihui Yu Jingxuan Wei Shaojie He Shuyue Tan Linzhuang Sun Chang Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

In this work, we discuss the issue of multimodal detection of long and short video violations on Internet environment. We collect and annotate a dataset consisting of 6000 videos from different platforms, and offer a joint model of video frame sequences and text sequences to analyze video and text information for violation detection. We also do a comparison with the unimodal model. As far as we know, this is the first multimodal dataset proposed for the detection of violations involving “pornography and violence”. And the model adapts to the conversion of video unimodal data to multimodal data. Experiments found that although text unimodal recognition results are limited, the complementarity of the modalities in the multimodal case gives a large improvement in the results. We discuss the reasons for the results and open the codes and dataset in this domain for further study. the address is https://***/weijingxuan/multivideo.

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Efficient Regression with Feature Selection Based on L12-Norm

Efficient Regression with Feature Selection Based on L12-Nor...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Yu Lei Qin Li Jane You School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China School of Telecommunications Engineering Xidian University Xi’an China Department of Computing The Hong Kong Polytechnic University Hong Kong China

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with ℓ 12 -norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by ℓ 12 -norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the ℓ 12 -norm regularization iteratively. Experimental results demonstrate the effectiveness of our method.

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Semantic Matching Algorithm for Legal Issues Based on BERT and Graph Convolution with Multi-granularity Features 23

Semantic Matching Algorithm for Legal Issues Based on BERT a...

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proceedings of the 2023 12th international conference on computing and pattern recognition

作者： Baohua Qiang Shijie Pang Keliang Teng Zhiguang Liu Weigang Liu Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9798400707988

In recent years, pre-training models represented by BERT have shown amazing text semantic representation capabilities supported by large-scale corpus, and now have become one of the mainstream solutions for semantic matching tasks in legal Q&A systems. However, pre-training models generally do not set specific pre-training tasks in terms of syntactic information, but implicitly and incidentally learn in large-scale unsupervised, which does not make good use of syntactic information as an important factor for representing semantics. To address this problem, this paper proposes a multi-granularity feature semantic matching model based on BERT and graph convolution. the model first obtains syntactic features of sentences in the form of dependent syntactic trees using existing tools, and then fuses syntactic features with word features and sentence features of BERT using an attention mechanism and graph convolutional neural network to enhance the characterization of text semantics and make the model better for semantic matching tasks. Experimental results show that the algorithm achieves significant improvements in the semantic matching task and outperforms the five comparison methods in the DIAC2019 dataset. Compared to the baseline model Sentenct-BERT, the accuracy, precision, recall and F1 values are improved by 0.33%, 1.18%, 1.03% and 1.05% improvement.

关键词： BERT Graph Convolutional Neural Network Legal Q&A Semantic matching

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Integrating Autoencoder-Based Hybrid Models into Cervical Carcinoma Prediction from Liquid-Based Cytology

Integrating Autoencoder-Based Hybrid Models into Cervical Ca...

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12th international conference on Data Science, Technology and Applications, DATA 2023

作者： Idlahcen, Ferdaous Idri, Ali Zerouaoui, Hasnae Al Khwarizmi College of Computing Mohammed VI Polytechnic University Ben Guerir43150 Morocco Software Project Management Research Team ENSIAS Mohammed V University Rabat10000 Morocco

ISBN: (纸本)9789897586644

Artificial intelligence (AI)-assisted cervical cytology is poised to enhance sensitivity whilst lessening bias, labor, and time expenses. It typically involves image processing and deep learning to automatically recognize pre-cancerous lesions on a given whole-slide image (WSI) prior to lethal invasive cancer development. Here, we introduce autoencoder (AE)-based hybrid models for cervical carcinoma prediction on the Mendeleyliquid-based cytology dataset. this is built on fourteen combinations of AE, DenseNet-201, and six state-ofthe-art classifiers: adaptive boosting (AdaBoost), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), k-nearest neighbors (k-NN), and random forest (RF). As empirical evaluations, four performance metrics, Scott-Knott (SK), and Borda count voting scheme, were performed. the AE-based hybrid models integrating AdaBoost, MLP, and RF as classifiers are among the top-ranked architectures, with respective accuracy values of 99.30, 99.20, and 98.48%. Yet, DenseNet-201 remains a solid option when adopting an end-to-end training strategy. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Support vector machines

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