检索结果-内蒙古大学图书馆

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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Introduction of Materials Genome Technology and Its Applications in the Field of Biomedical Materials

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MATERIALS 2023年第5期16卷 1906页

作者： Qiu, Yashi Wu, Zhaoying Wang, Jiali Zhang, Chao Zhang, Heye Sun Yat sen Univ Sch Biomed Engn Shenzhen Campus Shenzhen 518107 Peoples R China

Traditional research and development (R&D) on biomedical materials depends heavily on the trial and error process, thereby leading to huge economic and time burden. Most recently, materials genome technology (MGT) has been recognized as an effective approach to addressing this problem. In this paper, the basic concepts involved in the MGT are introduced, and the applications of MGT in the R&D of metallic, inorganic non-metallic, polymeric, and composite biomedical materials are summarized;in view of the existing limitations of MGT for R&D of biomedical materials, potential strategies are proposed on the establishment and management of material databases, the upgrading of high-throughput experimental technology, the construction of data mining prediction platforms, and the training of relevant materials talents. In the end, future trend of MGT for R&D of biomedical materials is proposed.

关键词： biomedical material material genome technology database high-throughput technology algorithm model

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Artificial intelligence in digital marketing: application and development trend analysis 24

Artificial intelligence in digital marketing: application an...

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2024 5th International Conference on Big Data Economy and Information Management, BDEIM 2024

作者： Huang, Zhaojing College of Business Hunan Agricultural University Hunan Changsha China

ISBN: (纸本)9798400711862

In the era of rapid development of information technology, all industries are embracing new opportunities for growth. In the area of marketing, artificial intelligence (AI) technology is widely used, and it is important for improving staff productivity and increasing the power of staffs. In the paper, we combine literature review, and empirical study to build a model of Digital Marketing Based on BP neural network, and study its *** experiment shows that digitalized marketing can offer audiences individualized services and products, on one hand, and the intelligent data analysis based can carry out the target audience aiming and personalized marketing strategies design on the other. At the last, this paper narrates the key importance and the contribution of the BP combining mode, and gives guidance for further promotion of the digital marketing industry. © 2024 Copyright held by the owner/author(s).

关键词： algorithm model Artificial Intelligence Digital Marketing Digitalization Information Technology Marketing

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Application analysis of collaborative filtering algorithm in course system construction and recommendation system under new business background 23

Application analysis of collaborative filtering algorithm in...

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Proceedings of the 2023 6th International Conference on E-Business, Information Management and Computer Science

作者： Liu Hui Long Wei Wuhan Business University China

ISBN: (纸本)9798400709333

Filtering out required information from a large amount of complex data has always been a difficult point in the construction of curriculum systems. This article starts from the perspective of cultivating cross-functional high-quality accounting talents under the new business background, draws on the idea of course learning, divides course stages and training routes, and finally uses a user-based collaborative filtering algorithm to establish a set of student course learning recommendation system algorithm models. , which provides certain practical reference for in-depth study of the "new business + general education" model.

关键词： Collaborative filtering algorithm model general education general education curriculum system construction recommendation system

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Bie-Modernism and Cultural Computing 9th

Bie-Modernism and Cultural Computing

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9th International Conference on Culture and Computing (C&C) part of the 23rd International Conference on Human-Computer Interaction (HCI)

作者： Wang, Jianjiang Chen, Haiguang Shanghai Normal Univ Shanghai 200234 Peoples R China

ISBN: (纸本)9783030774301;9783030774318

Civilization is the parasitic host of barbarism. Pseudo-modernity has lodged in modern civilization through high technology including computer technology and artificial intelligence technology, and has formed a threat to human modern civilization. Bie-modernism is a doctrine that expresses the idea of distinguishing the true modernity or true world from the pseudo modernity or the false world. Bie-modernist Culture Computing (BCC) is a kind of cultural computing guided by Bie-modernism, so as to defend the legitimate interests of people. It includes two parts, one is Visual Identification System, which combined with the background of the times and the humanistic environment, we can build a supporting platform for calculating the characteristics of the characters, and combine big data and NLP and other computer technologies to identify the real and pseudo characters. Another is Digital Identification System, which is from the perspective of cultural value, the feature model library of real and pseudo identification system is built to form a new foundation of digital global identification system. For paper regulation here we just introduce one of them.

关键词： Bie-modernism Pseudo modernity Distinguish Culture computing Bie-modernist culture computing Visual identification system algorithm model

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Real-time monitoring of the oil shale pyrolysis process using a bionic electronic nose

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FUEL 2022年 313卷

作者： Zhao, Rongsheng Kong, Cheng Ren, Luquan Sun, Youhong Chang, Zhiyong Jilin Univ Earth Sci Coll 2199 Jianshe St Changchun 130061 Peoples R China Jilin Univ Key Lab Bion Engn Minist Educ Changchun 130022 Peoples R China Jilin Univ Coll Biol & Agr Engn Changchun 130022 Peoples R China China Univ Geosci Beijing 100080 Peoples R China Jilin Univ Natl Local Joint Engn Lab Situ Convers Drilling & Exploitat Technol Oil Shale Changchun 130021 Peoples R China

Accurate and rapid prediction of thermal maturity during oil shale exploitation is important to optimize pyrolysis processes and decrease production costs. Then, a bionic electronic nose (BEN) was used for the first time for the real-time monitoring of the oil shale pyrolysis process. The results show that the calculated Easy%Ro change, unlike that of the measured vitrinite reflectance (%Ro), was small (+/- 0.004) in decomposition stages (dewatering, hydrocarbon generation stage and inorganic matter decomposition) at the different heating rate. This time-temperature-related parameter made it a perfect intermediary to associate the thermal maturation process with the BEN signal during oil shale pyrolysis. The monitoring of the pyrolysis process of oil shale in real time was divided into two steps: 1) qualitatively checking whether the oil shale had entered the hydrocarbon generation stage, and 2) when entering this stage, quantitatively predicting the Easy%Ro evolution. Combined with different feature extraction methods, a support vector machine (SVM) as a classifier was used to complete the first step, which had the best recognition rate of 91.13%. For the second step, random forest (RF) was applied to quantitatively measure the Easy%Ro values, with an R2 reaching 0.95. During the verification stage, the established integration model was used to recognize a heating rate, which, between the trained heating rate in the algorithm model, performed much better in the first step (SVM, 92.96%) than in the second step (RF, 0.57), and those results were given in 38 to 125 s. Thus, the BEN technique can be applied in the real-time detection of oil shale exploration.

关键词： Oil shale Pyrolysis process Bionic electronic nose Real -time monitoring algorithm model

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The algorithm model of visual monitoring communication for PLC

The algorithm model of visual monitoring communication for P...

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2014 International Conference on Advanced Control, Automation and Robotics (ACAR2014)

作者： C.J.Ju G.K.Yang Z.Y.Li S.D.Zhang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781784660468

A new visual monitoring communication algorithm model is described in this paper which can improve the communication efficiency between the PLC(programmable logic controller) and its programming software. The model consists of dynamic packaging algorithm and scrolling view algorithm aiming at meeting real-time visual monitoring. It is described in detail that how to adjust the length of communication data frame and refresh the data of current scrolling view of the programming software concerning the algorithm model. The experimental results show that the communication efficiency and the interface refreshing properties of visual monitoring will be improved with the increase of communication traffic load which proves that the algorithm modelling is effective.

关键词： programmable logical controller PLC's programming software visual monitoring algorithm model

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Support vector machine combined with magnetic resonance imaging for accurate diagnosis of paediatric pancreatic cancer

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IET IMAGE PROCESSING 2020年第7期14卷 1233-1239页

作者： Zhang, Yuling Wang, Shuchang Qu, Shuqiang Zhang, Hongli Habin Med Univ Affiliated Hosp 2 Dept Pediat Habin 150086 Peoples R China Habin Second Hosp Dept Thyroid & Breast Surg Habin 150001 Peoples R China Harbin Med Univ Canc Hosp Dept Hemolymph Habin 150081 Peoples R China

When performing paediatric pancreatic cancer (PC) diagnosis, magnetic resonance imaging (MRI) will be interfered by many factors, resulting in poor diagnosis. In order to improve the diagnostic effect of MRI on PC, this study is based on machine learning and combines SVM (support vector machine) and MRI detection with the support of image processing technology and improves the diagnostic accuracy of MRI for PC by the computer-assisted method. At the same time, in the research, this study combines the characteristics of MRI detection to construct an MRI detection model based on SVM. In addition, this study obtains test samples through data collection, analyses the performance of the algorithm model through actual cases, and combines image processing to improve the detection effect. Studies have shown that the algorithm model proposed in this study can effectively improve the accuracy of MRI diagnosis of PC and provide a theoretical reference for subsequent related research.

关键词： medical image processing learning (artificial intelligence) cancer patient diagnosis paediatrics support vector machines biomedical MRI image processing technology PC computer-assisted method MRI detection model algorithm model combines image processing detection effect MRI diagnosis support vector machine magnetic resonance imaging performing paediatric pancreatic cancer diagnosis poor diagnosis diagnostic effect machine learning SVM

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Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia

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IET IMAGE PROCESSING 2020年第11期14卷 2610-2615页

作者： Zheng, Yunsong Tong, Hangbin Zhao, Teng Guo, Xiaoxia Xu, Hui Yang, Ruwu Shaanxi Univ Tradit Chinese Med Sch Med Technol Xian 712046 Peoples R China Shaanxi Univ Tradit Chinese Med Affiliated Hosp Dept Radiol Xianyang 712000 Peoples R China Shaanxi Univ Tradit Chinese Med Affiliated Hosp 2 Dep Radiol Xianyang 712000 Peoples R China Xian Gaoxin Hosp Dept Ultrasound Xian 710075 Peoples R China Xi An Jiao Tong Univ Dept Med Imaging Affiliated Hosp 1 Xian 710061 Peoples R China Univ Toronto Fac Dent Ctr Study Pain Toronto ON M5G 1G6 Canada Xian XD Grp Hosp Dept Radiol Xian 710077 Peoples R China

The brain avatar of schizophrenic patients is different from the normal human brain avatar, and it is difficult to overcome the complex environmental effects of the brain through traditional magnetic resonance imaging (MRI). In order to improve the accuracy of MRI in detecting brain information in patients with schizophrenia, this study is based on the support vector machine classification algorithm and combined with multimodal MRI detection method to construct a detection model suitable for patients with schizophrenia. In addition, this study combines the existing test cases to divide the brain into regions and design a comparative experiment to study the accuracy of the model proposed in this study. Finally, the study draws the results by sub-regional comparison. Studies have shown that the algorithm model of this study has certain effects on brain detection in patients with schizophrenia, and can be applied to practice, and can provide theoretical reference for subsequent related research.

关键词： support vector machines biomedical MRI brain medical image processing multimodal magnetic resonance imaging schizophrenia schizophrenic patients normal human brain avatar complex environmental effects traditional magnetic resonance imaging brain information support vector machine classification algorithm multimodal MRI detection method detection model existing test cases algorithm model brain detection

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algorithm for quickly improving quantitative analysis of risk assessment of large-scale enterprise information systems 4

Algorithm for quickly improving quantitative analysis of ris...

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4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Teng, Yangxin Li, Mingxuan He, Ling Li, Feng Chen, Tao Chen, Jia Wang, Xu State Grid Xinjiang Elect Power Res Inst Urumqi Xinjiang Peoples R China

ISBN: (纸本)9781728143903

Information system security is a systematic project with integrity, complexity, and uncertainty. Relying solely on technical means cannot fundamentally eliminate information system security issues. It is necessary to consider the establishment of comprehensive safeguards, comprehensive management and monitoring. The information security risk assessment can solve the problem that is difficult to quantify in the security of the information system. Through the quantitative analysis of the asset risk, the original difficult-to-qualify risk value becomes easy to confirm and assign. After extensive research on large-scale enterprise information systems and multiple risk assessment projects, the in-depth study of the general computing model and quantitative computing model of information security risk assessment, a quantitative information security risk assessment model with a risk correction value is proposed. The algorithm of this model makes the risk analysis and risk value calculation in the information security risk assessment process faster, more accurate, and more practical. At the same time, it can be quickly applied to the information system risk assessment of large enterprises.

关键词： Rapid risk assessment quantitative algorithm model asset correction value

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