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Development and Validation of an algorithm model for Predicting Heat Sink Effects during Pulmonary Thermal Ablation

CURRENT MEDICAL IMAGING 2024年第1期20卷 e15734056280329-e15734056280329页

作者： Du, Peng Chen, Zenan Chang, Yuwen Meng, Liangliang Feng, Wei Ge, Zongyuan Zhang, Xiao Xiao, Yueyong PLA Med Coll Beijing Peoples R China Peoples Liberat Army Gen Hosp Dept Pathol Med Ctr 6 Beijing Peoples R China Peoples Liberat Army Gen Hosp Med Ctr 1 Dept Radiol Beijing Peoples R China Monash Univ Dept Elect & Comp Syst Engn Clayton Vic Australia Shukun Beijing Technol Co Ltd Beijing Peoples R China Chinese PAP Hosp Beijing Dept Radiol Beijing Peoples R China

Aims: The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols. Background: The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time. Objective: To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular. Methods: First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results. Results: In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the

关键词： Machine learning Lung tumor CT Segmentation method Pulmonary thermal ablation algorithm model

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The conception of the algorithm model of traditional Chinese medicine(TCM) prescription based on deep learning

The conception of the algorithm model of traditional Chinese...

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2018 4th International Conference on Systems,Computing,and Big Data(ICSCBD 2018)

作者： Yuan Zhou Yaofeng Miao School of engineering Xi'an International University

This project takes the neural network as the basic architecture of the improved algorithm, and uses a deep nonlinear network structure to learn the deep learning technology of complex function approximation and integrates the "cause, location, nature and trend of disease" of traditional Chinese medicine. Also, this algorithm model explores the thinking mode of selecting legal parties for famous experts of traditional Chinese medicine, and checks whether the treatment process of doctors is standard and reasonable, also finds out cases of wrong treatments, so as to improve clinical efficacy and standardize the treatment process of traditional Chinese medicine.

关键词： "Cause,location,nature and trend of disease" Deep learning algorithm model choose the therapy and determine the prescription

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Simulation of UML graph classification model by using data preprocessing and convolutional neural network

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OPTICAL AND QUANTUM ELECTRONICS 2024年第2期56卷 277-277页

作者： Wang, Fangli Guangzhou City Univ Technol Sch Comp Engn Guangzhou 510800 Guangdong Peoples R China

With the gradual strengthening of science and technology and the continuous progress of the times, various fields of technology have been improved, resulting in an increasing amount of data generated year by year. In terms of UML diagram processing, the classification of UML diagrams has always been a hot topic of discussion, and for more complex systems, it has very good expressive power. Therefore, it has been applied in many fields and has become a priority for people to consider. This article will be based on the convolutional neural network graph classification algorithm to obtain data from UML diagrams and partition them. Firstly, analyzing and discussing the content overview of UML, it was found that it has wide applications in various fields;Then, through the research and analysis of convolutional neural networks, a classification model is constructed based on their characteristics. Finally, by collecting data on UML diagrams, a UML diagram dataset is designed. By preprocessing the dataset, the overall iteration count of the model, the UML diagram classification model, the accuracy of classification results, and the impact of average time can be obtained. After analyzing the existing research results, it was found that the graph classification algorithm model mentioned in this article can better analyze and classify UML diagrams.

关键词： Convolutional neural network UML Figure classification algorithm model

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algorithmic model for Job Stability Based on Factor Analysis Method 14

Algorithmic Model for Job Stability Based on Factor Analysis...

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14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Luo, Qifeng Li, Hang Yang, Fan Gao, Xiaolan Luo, Shuxian Zhongshan Power Supply Bureau Co. Ltd. Guangdong Power Grid Corporation Zhongshan528400 China

ISBN: (纸本)9781643685632

Job stability is an important indicator for assessing the consistency and reliability of jobs under different conditions. This study proposes a job stability algorithm model based on the KMO(Kaiser-Meyer-Olkin) test to assess the appropriateness of using factor analysis on data and factor analysis method, aiming to evaluate the stability level of jobs by analyzing various factors within the job. The model collects data related to the stability status of jobs, assesses the suitability of the data through the KMO test to determine if it is suitable for factor analysis, and uses an appropriate factor extraction method to extract key factors. Factor validation is then conducted, and reliable indicators of the factors, such as Cronbach's alpha coefficient, and validity indicators, such as the proportion of variance explained, are calculated. Based on the extracted factors and their loading, scores or indices for job stability are calculated, and an appropriate method is selected to compute the comprehensive assessment of job stability. This algorithm model provides an effective method for evaluating job stability. By extracting and analyzing key factors within jobs, it is possible to objectively assess the consistency and reliability of jobs. The application of this model can help improve job stability and provide guidance for optimization and improvement in the job execution process. © 2024 The Authors.

关键词： algorithm model job stability key factors KMO test

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algorithm for environmental risk assessment of cosmetics to reduce their environmental impact

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TOXICOLOGICAL RESEARCH 2023年第1期39卷 15-24页

作者： Choi, Min-Seok Kim, Seoyoung Lee, Si Eun Yeon, Sanghyeon Park, Sanghee Yang, Jun Yong An, Susun AMOREPACIFIC 1920 Yonggu Daero Yongin 17074 Gyeonggi Do South Korea Chemtopia Co Ltd 12th FloorDaerung Post Tower 7th Seoul 08377 South Korea

Cosmetics, especially rinse-off personal care products (PCPs), such as shampoo, facial cleanser, and body wash, are composed of various chemicals and are one of the sources of chemicals released into aquatic ecosystems. Therefore, the cosmetic industry strives to reduce the impact of their products on the aquatic environment. In this study, we proposed an algorithm based on persistence, bioaccumulation potential, and toxicity (PBT) for the environmental risk assessment of cosmetics. PBT features are generally used in the evaluation of the environmental impact of chemicals. Based on the PBT assessment, it is possible to predict the short- and long-term effects of chemicals on the environment. Our algorithm derives substance and product scores from PBT features, allowing for the risk assessment of each ingredient in the product. Furthermore, we proposed a criterion for the environmental impact grade through which each component can be classified. We intend to use this grade and factors determined through the algorithm to manufacture products with low environmental impact.

关键词： Cosmetics Environmental risk assessment Environmental impact PBT algorithm model

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An algorithmic software model for the characterization of radioactive waste

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JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY 2021年第1期330卷 279-292页

作者： Tessaro, Ana Paula Gimenes de Araujo, Leandro Goulart Tsutsumiuchi, Victor Keichi de Oliveira Junior, Julio Vicente, Roberto Inst Pesquisas Energet & Nucl IPEN CNEN SP Serv Gestao Rejeitos Radioat SEGRR Av Prof Lineu Prestes 2242 BR-05508000 Sao Paulo SP Brazil

Non-consolidated information base, repetitive work, and data unreliability are some of the issues that may hamper a radioactive waste characterization program. Therefore, an algorithmic software model for the characterization of radioactive waste is proposed to boost waste management in nuclear-based facilities. The data obtained have enabled the construction of a guide code in the pseudotechnical language. The developed system fully covered the characterization stage, and met the needs identified in the radioactive waste management service. Here, we propose systematic documentation of the characterization methods that are employed during the radioactive waste management processes, besides formulating the system's data recording.

关键词： algorithm model Radioactive waste management Nuclear data Characterization

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Research of Evolutionary Multi-objective Optimization algorithm model Based on AIS

Research of Evolutionary Multi-objective Optimization Algori...

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International Conference on Materials Engineering and Mechanical Automation (MEMA 2013)

作者： Li, Ming-song Wang, Na Duan, Zhuo-hua ShaoGuan Univ Shaoguan Guangdong Peoples R China

ISBN: (纸本)9783037859018

Problem of multi-objective optimization based on Artificial Immune System (AIS) is an important research area of current evolutionary computing. Starting from the intelligent information processing mechanism of immune theory and the immune system itself, by researching the calculating model of immune evolution and Based on the biological mechanism of immune system, a general algorithm frame for solving optimization problem is proposed. The researching content has good theoretical value and practicability, also provides base for further research of specific type of problems.

关键词： Artificial Immune System Evolutionary multi-objective optimization algorithm model

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Research and application of push model based on digital twin for complex solutions

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INTERNATIONAL JOURNAL OF modelING SIMULATION AND SCIENTIFIC COMPUTING 2024年第3期15卷

作者： Wang, Hui Deng, Zhiji Liu, Ming Wang, Yiyou Zhang, Yadong Kong, Weisheng Wu, Rongji Zhejiang Dahua Technol Co Ltd Hangzhou 310053 Zhejiang Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Zhejiang Univ Sch Informat & Elect Engn Hangzhou 310058 Peoples R China Inventron Hangzhou Inc Hangzhou 310053 Zhejiang Peoples R China

In order to solve the selection problems such as multiple products, complex scenarios, and difficulty in supporting complex solutions, the product selection push model for complex solutions was proposed, the selection cloud platform was set up, and the platform architecture, composition, and operating mechanism were given. Based on the digital twin five-dimensional framework, the modeling process of the product selection cloud platform was detailed, and in the demand stage, the function model of the model was determined by the collaborative filter algorithm, which gave a comprehensive description of its functions. Through the construction of multi-platform fusion and algorithm model, the active push, scheme automatic process, and verification function of solution product selection have been realized. The solution has been applied in the selection of the smart security community scheme, verifying the effectiveness and practicality of the platform.

关键词： Product model digital twin five-dimensional model algorithm model

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Product perceptual design optimization model based on BP neural network

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FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND 2024年 10卷

作者： Yong, Lei Ningbo Polytech Coll Mech & Elect Engn Ningbo Peoples R China

Background User reviews of online shopping platforms can truly reflect users' feelings about the use of products. They have the advantages of large sample size, wide range and uniform distribution, and can help optimize the perceptual model of product *** The web crawler crawls user comments, and TFIDF-SP quantifies them. The principal component analysis method selects the perceptual evaluation index, the morphological analysis method disassembles the product into several main structures, and the BP neural network constructs the perceptual optimization *** Taking the paint tray as an example, according to the trained BP neural network, the shape factor combination with the highest perceptual evaluation value is predicted. The experimental results verify the accuracy of the *** The model based on BP neural network has the ability to quickly and accurately combine the best form factors, improves the design efficiency of product design perceptual optimization, improves the rationality of product design, and provides a new idea for consumer demand market-oriented product design.

关键词： kansei engineering BP neural network algorithm model product design optimization paint tray

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Deep learning-based image target detection and recognition of fractal feature fusion for BIOmetric authentication and monitoring

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NETWORK modelING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022年第1期11卷 1-14页

作者： Liu, Duolin Teng, Wei Shenyang Ligong Univ Coll Econ & Management Shenyang 110159 Liaoning Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engineer Anshan 114000 Liaoning Peoples R China

Image detection and recognition play an important factor in processing and field of view. In recent years, the rapid growth of deep learning in self-learning features has spanned traditional research algorithms in the field of image research and recognition. Of course, the ability to achieve this is determined by the technical characteristics of deep learning. This is a recently popular technology. Therefore, its changes naturally affect the heartstrings of every researcher. Of course, it can also change the traditional target detection and recognition methods. In order to deeply study the current situation of image detection and recognition algorithms based on deep learning and fractal feature fusion technology, this paper uses graph analysis, recognition detection, and model building methods to collect samples, analyzes image recognition technology, and streamlines the algorithm, and created an image recognition algorithm based on deep learning theory. When studying the recognition rate of the two algorithm models under SOC acquisition conditions, the research results show that the recognition rate of the scn-1 algorithm model can reach 96.12%, and the recognition rate of the Scn-2 algorithm model is 93.55%, which is not only slow in convergence, but also the overall recognition performance is also worse than that of scn-1. In the process of further optimization of the studied algorithm model, the optimization methods sdg and adadelta are compared, and the recognition rate after sdg is finally stable is 92.89% on average. The performance of the adadelta algorithm model is stable when the epoch is 36, and the recognition rate after stability is 95.69% on average. Then set the learning rate attenuation coefficient a= 0.94, and the recognition rate is 96.12%, which shows that the optimization algorithm has good robustness for parameter selection. It is basically realized that starting from deep learning and fractal feature fusion technology, an algorithm model that c

关键词： Deep learning Fractal feature fusion Target detection and recognition algorithm model

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