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YOLO lung CT disease rapid detection classification with fused attention mechanism 7

YOLO lung CT disease rapid detection classification with fus...

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7th international conference on Electronic Information technology and Computer engineering (EITCE)

作者： Su, Qinghua Qin, Zhenbo Mu, Jianhong Wu, Hao Beijing WUZI Univ Coll Informat Beijing Peoples R China

ISBN: (纸本)9798400708305

Currently, although the use of convolutional neural networks (CNN) for detecting lung infection has improved the detection performance and efficiency, it still has certain shortcomings, low feature utilization for images or difficulty in focusing key features. An effective YOLO algorithm with fused attention mechanism is proposed for lung CT images to detect normal, common pneumonia and COVID-19 images to address the above problems. the YOLO with fused attention mechanism is mainly divided into two parts for model training and experiments: the first step performs lung segmentation of chest CT images and data cleaning of CT images based on physician diagnostic image values;the second step uses the cleaned lung CT images for training and model evaluation of the Yolov5 model with fused attention mechanism (CBAM). We use a series of operations such as binarization, expansion erosion and connected domain segmentation for initial segmentation and filtering of lung images, and incorporate the attention mechanism into the YOLO model, which enables the model to better focus on key features and avoid interference from erroneous data. the results on the COVID-19x dataset show that the YOLO model with the fused attention mechanism detects classification with an accuracy rate of 0.85 and a recall rate of 0.88. In summary, the fused attention mechanism YOLO outperforms the original YOLO model by 6.5% in accuracy and 8.8% in recall, which helps clinicians

关键词： Attentional mechanism Yolov5 CT detection classification

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Design and Computer Simulation of Original Doll Trend Brand Communication Path Based on Artificial Bee Colony Algorithm 24

Design and Computer Simulation of Original Doll Trend Brand ...

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5th international conference on Computer Information and big data Applications, CIBDA 2024

作者： Yan, Lin School of Art and Design Guangdong University of Science and Technology Guangdong Province Dongguan City523083 China

ISBN: (纸本)9798400718106

this study focuses on the design and computer simulation of communication paths for original doll trend brands using the artificial bee colony algorithm. the original doll trend market is characterized by rapid changes and intense competition, making efficient brand communication essential. Traditional methods for designing communication paths may face limitations in addressing the dynamic nature of this market. In response, this research proposes the use of the artificial bee colony algorithm, inspired by the foraging behavior of honeybees, to optimize communication paths for original doll trend brands. the algorithm iteratively explores and evaluates potential paths based on brand objectives and constraints, seeking to maximize brand exposure and audience engagement while minimizing communication costs. through computer simulations, the effectiveness of the proposed approach is evaluated, demonstrating its ability to generate optimized communication paths tailored to the unique characteristics of the original doll trend market. the findings highlight the potential of the artificial bee colony algorithm as a valuable tool for enhancing brand communication strategies in dynamic and competitive market environments. © 2024 ACM.

关键词： Design

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A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation 30

A Logical Pattern Memory Pre-trained Model for Entailment Tr...

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Joint 30th international conference on Computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yuan, Li Cai, Yi Ren, Haopeng Wang, Jiexin School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

ISBN: (纸本)9782493814104

Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibits a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models often overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. To address this limitation, we propose the logical pattern memory pre-trained model (LMPM). LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions. Furthermore, to mitigate the influence of logically irrelevant domain knowledge in the Wikipedia-based data, we introduce an entity abstraction approach to construct the dataset for pre-training LMPM. the experimental results highlight the effectiveness of our approach in improving the quality of entailment tree generation. By leveraging logical entailment patterns, our model produces more coherent and reasonable conclusions that closely align with the underlying premises. Code and data are released at https://***/YuanLi95/T5-LMPM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Domain Knowledge

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Estimating the Cost-Effectiveness of Communications Software Design for Networks 5th

Estimating the Cost-Effectiveness of Communications Software...

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5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Jain, Garima Mehta, Deepak Yadav, Rakesh Kumar Shankar Shankar, Shiv Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mathematics Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Communications software layout for networks is essential to keeping green and robust operations. Within the manner of designing new or advanced software, a couple of cost elements are taken into consideration. Estimating the fee-effectiveness of communications software program layout for networks calls for an in-depth analysis of the charges related to improvement, implementation, and maintenance of the software program, in addition to the predicted gain of the software in phrases of stepped forward network overall performance and decreased working prices. this evaluation must recollect the improvement method, hardware charges, support required, and any relevant taxes or other prices. Moreover, the value of training users well must be included in the evaluation. Estimating the value-effectiveness of communications software program design for networks requires a comprehensive technique considering capacity alternate-offs among fees and advantages. the evaluation must be a complete assessment of the software program development method to ensure proper and cost-effective software layout. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Analysis Communications Estimating Operations Performance

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A Novel Knowledge Semantic Video game Prototype to treat patients with Absence Epilepsy 5

A Novel Knowledge Semantic Video game Prototype to treat pat...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Kumar, Srikoti Rishi Reshmitha, Uppala Sarath, Tenneti Sai Challa, Tanmayi Akhil, Karnam Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Science And Business Systems Hyderabad India Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Sience And Engineering Hyderabad India

ISBN: (纸本)9798350330861

In recent times, children are increasingly experiencing anxiety and depression, nervousness, emotional distress, and a susceptibility to conditions such as Absence Epilepsy, Juvenile Myoclonic Epilepsy (JME), and Migraine. Absence Epilepsy is one of the major challenging issues of current neurological research. Absence Epilepsy is a common form of childhood epilepsy that can have significant impact on children, leading problems to memory loss, lack of cognitive ability, attention deficit hyperactivity disorder (ADHD). Recent studies of neurological research highlight that specific video games have positive effects on cognitive and motor functions in children. the proposed research presents a novel approach to create a video game prototype exclusively for children with Absence Epilepsy. Concepts of semantic web are used to build the various relations among depression, anxiety, Absence Epilepsy to understand its cause, impact on human brain and memory. Additionally, the prototype aims to assess the game's usability to ensure it is accessible and effective for its intended audience. the research methodology involves a multidisciplinary approach, drawing from neurology, pediatrics, Game designers, and Semantic Web technologies. the initial phase involves an in-depth analysis of the cognitive and motor challenges faced by children with Absence Epilepsy, enabling the integration of suitable therapeutic elements into the game mechanics. Subsequently, Semantic Web technologies are utilized to construct an ontology capturing crucial information about the player's condition, medical history, and game play preferences. In conclusion, the research bridges the gap between pediatric healthcare and therapeutic gaming. the innovative video game prototype promises in enhancing the lives of children with Absence Epilepsy, enhancing their cognitive abilities, memory, and cognitive attention, and paving the way for further exploration of video games as a valuable complement to traditio

关键词： Ontology

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Analysis of Anti-Multipath Performance Based on Traditional Acquisition Algorithm of BOC Signal 5

Analysis of Anti-Multipath Performance Based on Traditional ...

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5th IEEE international conference on Artificial Intelligence and big data, ICAIBD 2022

作者： Ji, Yuanfa Song, Sisi Sun, Xiyan Ghazal, Kamarul Hawari Bin Zhao, Songke Guilin University of Electronic Technology National and Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service Guilin China Guilin University of Electronic Technology Information and Communication School Guilin China Universiti Malaysia Pahang Pahang Malaysia

ISBN: (纸本)9781665499132

At present, multipath interference is still an inevitable natural influencing factor for high-precision navigation and positioning. the signal transmitted by the satellite is reflected and refracted by the interference of the surrounding environment, which will cause certain error interference to the phase and pseudorange of the direct wave signal. In order to analyze the degree of interference caused by multipath errors in the signal acquisition process, and compare the anti-multipath performance advantages of the acquisition algorithm. In this paper, starting from the formation principle of multipath signals. Based on the analysis of the code loop phase detector technology of incoherent lead minus lag (EML), the MATLAB simulation analysis platform is built, and then the comparison and simulation experiment of multipath error of sine BOC modulation signal and cosine BOC modulation signal is carried out for two traditional acquisition algorithms which are ASPeCT acquisition algorithm and SCPC acquisition algorithm. the simulation results show that the anti-multipath performance of the ASPeCT acquisition algorithm is better than that of the SCPC algorithm. © 2022 IEEE.

关键词： MATLAB

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An industrial part recognition algorithm combining multiscale feature extraction and lightweight improvement 5

An industrial part recognition algorithm combining multiscal...

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5th international conference on Computer Vision and data Mining, ICCVDM 2024

作者： Zhao, Jie Hu, Xiaojuan Wang, Yichen School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130022 China

ISBN: (纸本)9781510682863

Aiming at the problems of low accuracy and large number of parameters of the current general-purpose detection methods in coping with the tasks of complex background, part occlusion and multi-scale part recognition, a lightweight industrial part detection algorithm is proposed on the basis of YOLOv8s. Designing C2f-DRRCB efficient feature extraction module in the backbone network to improve the multi-scale feature fusion performance;by introducing a lightweight Adown downsampling module, more feature-critical information is retained while reducing the number of model parameters and computation;the EMA multi-scale attention mechanism is added at the end of the backbone network to improve the network's attention to the important feature regions and reduce the background information's interference;Design of a new lightweight inspection head, SCAD-Head, to improve accurate localization of part targets by capturing long range dependencies and shared convolution. the improved algorithm is applied to the SIP-17 industrial part synthetic dataset for validation, and the results show that compared with the baseline YOLOv8s model, the average detection accuracy of the improved model is improved by 5.7%, the number of parameters is reduced by 35.85%, and the amount of computation is reduced by 27.72%, which is also a certain degree of improvement in the detection performance compared with the other latest detection algorithms. © 2024 SPIE.

关键词： Photons

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Unmanned Aerial Vehicle Image data-Driven Detection of Tobacco Plant Height 5

Unmanned Aerial Vehicle Image Data-Driven Detection of Tobac...

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5th international conference on Electronic engineering and Informatics, ICEEI 2023

作者： He, Xingyao Liu, Rui Liang, Hong Wu, Kaixiang Yuan, Jiahui School of Information Science and Technology Yunnan University Kunming650504 China School of Earth Science Yunnan University Kunming650504 China

ISBN: (纸本)9781643685021

the study and application of high-throughput plant height data acquisition technology for tobacco crops are limited. A tobacco plant height detection method based on deep learning and UAV tilting photography is proposed to address this. Firstly, tilting photography by drone is used to obtain high-throughput plant height data, and a 3D model of plants within the region is generated to extract elevation information. then, an improved YOLOv5 algorithm with a CBAM attention mechanism embedded in the C3 module of the backbone network is proposed. this algorithm is designed to reduce shallow noise information, enhance the detection capability of small and dense targets, and achieve target detection of plants in the region. this will result in estimated plant heights for each plant. the experimental results show that the CBAM-YOLOv5 model significantly improves the recognition effect, increasing the AP value for tobacoo crop recognition to 98.04%. Compared with the original YOLOv5 model, the AP value of our model has increased by 0.7%. the plant target detection has a high correlation between the estimated values calculated from the 3D model and the measured values, with a linear slope of 0.9544 and R2-value of 0.4971, achieving the detection of high-throughput plant height data for tobacco crops. © 2024 the Authors.

关键词： 3D modeling

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Accelerating big data Application by Eliminating Redundancy on Hadoop Cluster 29

Accelerating Big Data Application by Eliminating Redundancy ...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lei, Kelun Du, Shaokang You, Xin Xuan, Zhibo Kong, Haoran Yang, Hailong Shang, Jing Xiao, Zhiwen Wu, Zhihui Luan, Zhongzhi Qian, Depei Beihang University School of Computer Science and Engineering Beijing China China Mobile Information Technology Center China

ISBN: (纸本)9798350330717

big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement. © 2023 IEEE.

关键词： Hadoop Performance Optimization Redundant Patterns

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Trajectory classification based car loan collection scoring model using a hierarchical CNN-LSTM method 5

Trajectory classification based car loan collection scoring ...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Pei, Ming School of Data Science University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9781510668621

To avoid economic losses on loan borrowers who are in default, financial institutions and companies employ plenty of debt collection measures like making phone calls, sending messages and even resorting to legal action. Since these measures also incur non-negligible costs and differ significantly in expenses, debt collection scoring models are proposed to assess the likelihood of a client’s payment delinquency thus helping companies in determining appropriate collection measures and improving effectiveness on allocating resources. Traditional methods in this area utilize clients’ personal information and are highly dependent on the accuracy of data which in practice can’t be guaranteed. In this work, we formulate collection scoring as a trajectory classification method and build a discriminative network that mines clients’ personal car trajectories which is an informative and stably accessible data resource in the car loan business scenario. We first propose a novel preprocessing method to extract features and transform raw trajectories into sequences of fixed-shape tensors. then a convolutional auto-encoder is employed to condense extracted tensors into a low-dimensional representation. Finally, a Bi-LSTM is adopted to capture latent characteristics in the feature sequences and makes a prediction. Experiments on a real-world collected dataset confirm the effectiveness of our model. © 2023 SPIE.

关键词： Tensors

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