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3rd International Conference on artificial intelligence and Autonomous Robot Systems, AIARS 2024

作者： Teng, Jinjie Changchun Humanities and Sciences College Changchun China

ISBN: (纸本)9798350376173

The huge amount of data, complexity and changeability are difficulties that technological financial service platforms have always struggled to solve. Only by overcoming this difficulty can we carry out data analysis and decision support efficiently and accurately. This article solves this problem by introducing artificial intelligence algorithms. The article discusses the specific application methods of artificial intelligence algorithms in technology and financial data service platform systems. These methods include algorithm integration, model optimization, feature selection and extraction. algorithm integration improves overall performance and stability by combining multiple models;model optimization improves the performance and generalization capabilities of the model by adjusting hyperparameters and optimization algorithms;feature selection and extraction reduces the complexity of the model and improves efficiency by selecting the most relevant and useful features. Experimental results show that the system response time of this algorithm is between 0.01-0.08 seconds, and it has achieved remarkable results in science and technology financial data analysis, prediction and decision support. © 2024 IEEE.

关键词： artificial intelligence algorithm grid search random forest science and technology financial data service platform

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Determination of the shear failure areas of rock joints using a laser scanning technique and artificial intelligence algorithms

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ENGINEERING GEOLOGY 2021年 293卷 106320-106320页

作者： Ge, Yunfeng Xie, Zhiguo Tang, Huiming Du, Bin Cao, Bei China Univ Geosci Fac Engn Wuhan Hubei Peoples R China China Univ Geosci Minist Educ Three Gorges Res Ctr Geohazards Wuhan Hubei Peoples R China

Two artificial intelligence models-backpropagation neural network (BPNN) and support vector machines (SVM)-were created to investigate the effects of mesostructure characteristics on the shear mechanical behaviors of rock joints. This was achieved through learning training samples for the evaluation of the five basic geometrical parameters sensitivity (i.e., slope angle, horizontal orientation, elevation difference, curvature, and aperture distribution), and for determination of the shear failure regions when rock joints were subjected to low-normal and shear loads. First, the digital elevation models (DEMs) of rock joints were produced through point clouds collected using a laser scanning system. Five geometrical parameters were specified as the inputs for the artificial intelligence models, and an approach was developed to calculate the 3D aperture distribution using a point cloud registration algorithm. Shear failure regions were considered as the outputs, which were extracted from images taken after direct shear testing via the global threshold algorithm. Secondly, BPNN and SVM models were employed in order to establish relationships between the geometrical parameters and shear failure areas by machine learning on training samples. Thirdly, the information value (IV) algorithm was used to verify the two trained models. Results showed that the BPNN and SVM models made acceptable determinations of the shear failure areas, which corresponded to the real situation. The predictions from the BPNN and SVM models were more accurate than those from the IV algorithm. Furthermore, shear failure regions depended primarily on the aperture distribution of rock joints during the shearing process, followed by horizontal orientation, elevation difference, and then the slope angle. The effect of parameter curvature on shear failure was less than the other four parameters.

关键词： Rock joint Shear failure areas Geometrical parameters Aperture distribution artificial intelligence algorithm Laser scanning

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Construction of a Distributed 3D Interior Design System Based on artificial intelligence algorithms

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Procedia Computer Science 2024年 243卷 450-457页

作者： Liang Xiang Junjie Hou Jianfeng Wang Lulu Liu TED Graduate School of Design Korea National University Seongbuk 02707 Seoul Korea College of Design Hanyang University Ansan Gyeonggi Province 02452 Korea

Interior design is a complex system that includes many elements, such as user needs, spatial structure, material selection, color matching, lighting layout, etc. The relationships between them are complex and intricate. How to design space according to user needs is one of the most challenging issues in interior design. A distributed 3D interior design system based on artificial intelligence algorithms was adopted to address this issue. The system divided indoor spatial information into several subsets. It assigned subsets to various professional designers based on user needs, and generated multiple design schemes through artificial intelligence algorithms, which were then screened, modified, and optimized by each professional designer. In this way, designers from different professional backgrounds can personalized design the space according to their own understanding, thereby avoiding issues such as information fragmentation and low efficiency in traditional interior design. The results showed that the distributed 3D interior design system based on artificial intelligence algorithms can improve the overall performance from an average of 82.68% to 94.94%, effectively avoiding the problem of information fragmentation.

关键词： artificial intelligence algorithm 3D Interior Design System Distributed System Backpropagation Neural Network Support Vector Machine

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Application of Nonlinear Fractional Differential Equations in Computer artificial intelligence algorithms

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第1期8卷 1145-1154页

作者： Xie, Jue Al Dmour, Ayman Lakys, Yahya Hunan Urban Construct Coll Xiangtan 411104 Peoples R China Appl Sci Univ Coll Arts & Sci Eker Bahrain Amer Univ Middle East Coll Engn & Technol Egaila Al Ahmadi Kuwait

In order to study the application of nonlinear fractional differential equations in computer artificial intelligence algorithms. First, the concept, properties and commonly used neural network models of artificial neural network are introduced, the domestic and foreign status quo of the application of fractional calculus theory to neural network technology is described. Then, the definition, properties and numerical calculation methods of fractional calculus theory are introduced in detail. Then, based on the analysis of artificial intelligence neural network algorithm, the theory of fractional differentiation is introduced, construct BP neural network based on fractional order theory. The Sigmoid function is used as the node function of the neural network, and the actual data is used as the sample set, train a fractional-order network. Finally, by training the network, summarize the change of the two parameters a and p in the function, the impact on the training of the entire network, and make a simple comparison with the fractional order neural network based on the sigmoid function. Experiments show that a variable-order iterative learning algorithm is proposed and applied to the training of neural networks, the results show the feasibility of this algorithm and its advantages in convergence speed and convergence accuracy.

关键词： Nonlinear Fractional differential equation artificial intelligence algorithm

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Resilience of healthy cities in the post-pandemic era: Findings based on internet of things data and artificial intelligence algorithms

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INTERNET OF THINGS 2023年 23卷

作者： Jiang, Chuntao Guan, Xin Zhu, Junfan Wang, Zeyu Song, Feifei Zhao, Changheng Foshan Univ Sch Math & Big Data Foshan 528000 Guangdong Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Guangzhou Xinhua Univ Dongguan 523133 Guangdong Peoples R China Guangdong Coll Finance & Commerce Financial Investment Coll Qingyuan 511500 Guangdong Peoples R China Guangzhou Univ Sch Publ Adm Guangzhou 510006 Guangdong Peoples R China Hubei Univ Econ Sch Finance & Publ Adm Wuhan 430205 Hubei Peoples R China Lanzhou Univ Sch Econ Lanzhou 730000 Gansu Peoples R China

This study aims to improve urban resilience in the face of public health emergencies. Therefore, based on the urban resilience development theory, a resilience evaluation index system for healthy cities is constructed. In addition, the Back Propagation Neural Network (BPNN) algorithm and entropy method are combined to empirically evaluate 7 representative cities in the Yangtze River Delta urban agglomeration. Linear regression analysis is carried out on the index weight of the evaluation system, relative proximity degree, and score of urban resilience. The results reveal that social environment and infrastructure are two important factors in the resilience evaluation of healthy cities. Nanjing and Shanghai have relatively good resilience, while Ma'anshan has the worst resilience. It is also found that economic status is the most critical factor affecting urban resilience. Thus, this study enriches the theories related to urban resilience, expands the evaluation system of urban resilience, and opens up a new approach to the quantitative evaluation of urban resilience.

关键词： Post-pandemic era Internet of things Healthy city artificial intelligence algorithm

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A new vaccine supply chain network under COVID-19 conditions considering system dynamic: artificial intelligence algorithms

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年 85卷 101378页

作者： Kamran, Mehdi A. Kia, Reza Goodarzian, Fariba Ghasemi, Peiman German Univ Technol Fac Business & Econ Dept Logist Tourism & Serv Management Muscat Oman Urmia Univ Technol Dept Ind Engn Orumiyeh Iran Birmingham City Univ Fac Comp Engn & Built Environm Sch Engn & Built Environm Dept Engn Curzon St Birmingham B4 7XG England Univ Seville Sch Engn Engn Grp Camino Descubrimientos S-N Seville 41092 Spain

With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multicommodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable c

关键词： Vaccine supply chain Stochastic optimization Simulation model Desirability for the vaccination Distribution -location -allocation Distributive justice artificial intelligence algorithm

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artificial intelligence Medical Ultrasound Equipment: Application of Breast Lesions Detection

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ULTRASONIC IMAGING 2020年第4-5期42卷 191-202页

作者： Zhang, Xuesheng Lin, Xiaona Zhang, Zihao Dong, Licong Sun, Xinlong Sun, Desheng Yuan, Kehong Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China Peking Univ Dept Ultrasound Shenzhen Hosp Shenzhen Peoples R China

Breast cancer ranks first among cancers affecting women's health. Our work aims to realize the intelligence of the medical ultrasound equipment with limited computational capability, which is used for the assistant detection of breast lesions. We embed the high-computational deep learning algorithm into the medical ultrasound equipment with limited computational capability by two techniques: (1) lightweight neural network: considering the limited computational capability of ultrasound equipment, a lightweight neural network is designed, which greatly reduces the amount of calculation. And we use the technique of knowledge distillation to train the low-precision network helped with the high-precision network;(2) asynchronous calculations: consider four frames of ultrasound images as a group;the image of the first frame of each group is used as the input of the network, and the result is respectively fused with the images of the fourth to seventh frames. An amount of computation of 30 GFLO/frame is required for the proposed lightweight neural network, about 1/6 of that of the large high-precision network. After trained from scratch using the knowledge distillation technique, the detection performance of the lightweight neural network (sensitivity = 89.25%, specificity = 96.33%, the average precision [AP] = 0.85) is close to that of the high-precision network (sensitivity = 98.3%, specificity = 88.33%, AP = 0.91). By asynchronous calculation, we achieve real-time automatic detection of 24 fps (frames per second) on the ultrasound equipment. Our work proposes a method to realize the intelligence of the low-computation-power ultrasonic equipment, and successfully achieves the real-time assistant detection of breast lesions. The significance of the study is as follows: (1) The proposed method is of practical significance in assisting doctors to detect breast lesions;(2) our method provides some practical and theoretical support for the development and engineering of intel

关键词： artificial intelligence algorithm medical ultrasound equipment breast lesions lightweight neural network real-time detection knowledge distillation

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A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method

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APPLIED MATHEMATICS AND COMPUTATION 2019年 361卷 499-516页

作者： Zhang, X. Chen, M. Y. Wang, M. G. Ge, Y. E. Stanley, H. E. Shanghai Maritime Univ Coll Transport & Commun Shanghai 201306 Peoples R China Boston Univ Ctr Polymer Studies Boston MA 02215 USA Boston Univ Dept Phys Boston MA 02215 USA Nanjing Normal Univ Sch Math Sci Nanjing 210042 Jiangsu Peoples R China Nanjing Normal Univ Taizhou Coll Dept Math Taizhou 225300 Peoples R China

Enhancing the accuracy of freight rate forecasting is crucial when agents in the shipping industry make their business decisions and strive to avoid or reduce possible risks. Although there has been a lot of freight rate trend analysis in the literature, the time-varying and volatile nature of the shipping market makes the accurate prediction of it extremely difficult, if not impossible. In this research, we first propose the use of a dynamic fluctuation network (DFN) to transform a time series data set into an evolving directed network, which removes the noise in the data and allows us to extract time-varying features in it. We then develop a hybrid approach that combines the DFN and artificial intelligence (AI) techniques to forecast the Baltic Dry Index (BDI), which is new to the literature. The utilization of DFN with AI enables the non-linear, cyclical and dynamic features of the BDI to be extracted effectively and the prediction accuracy is not impacted by the length and time-scale of sample selection either in long-term or short-term forecasting. These advantages of DFN offset the well-known limitations of traditional AI-based algorithms and econometric models in BDI forecasting. The empirical results from applying the resultant model to multi-time-scale datasets in a random sampling case show that the model is more accurate than the model based on the AI technique only. We test the accuracy of the DFN-AI model in three challenging cases which respectively contain a sudden rise, a decline, or frequent fluctuations of the BDI. The DFN-AI model has fewer errors and a higher trend matching rate than the corresponding AI-based model. Our hybrid approach also shows its superiority in working with data containing an extreme market downturn, which shed light on the predictability of BDI. This study has important implications for overall business, commercial, and hedging strategies in the shipping industry. (C) 2019 Elsevier Inc. All rights reserved.

关键词： artificial intelligence algorithm Complex network Dynamic fluctuation network Baltic Dry Index prediction

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Machine Learning-XGBoost Analysis of Re-employment Intention of Full-time Mothers 24

Machine Learning-XGBoost Analysis of Re-employment Intention...

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Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management

作者： Junbi Wu Jie Zhou Institute of Psychology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400711862

This study focused on the effects of social support from strong and weak relationships on the re-employment intention of full-time mothers after the implementation of the three-child birth policy in China. This study received 358 valid questionnaires, and utilized the XGBoost machine learning algorithm to create model. After conducting an optimal search for the network parameters, we achieved an accuracy of 97.22% on the test set. Furthermore, XGBoost can effectively rank the importance of independent variables, facilitating timely and effective prediction and adjustment of re-employment intention based on significant factors.

关键词： artificial intelligence algorithm

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Research Progress of Photovoltaic Power Prediction Technology Based on artificial intelligence Methods

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Energy Engineering 2024年第12期121卷 3573-3616页

作者： Daixuan Zhou Yujin Liu Xu Wang Fuxing Wang Yan Jia College of Energy and Power Engineering Inner Mongolia University of TechnologyHohhot010051China College of Electric Power Inner Mongolia University of TechnologyHohhot010080China

With the increasing proportion of renewable energy in China’s energy structure,among which photovoltaic power generation is also developing *** the photovoltaic(PV)power output is highly unstable and subject to a variety of factors,it brings great challenges to the stable operation and dispatch of the power ***,accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV ***,the short-term prediction of PV power has received extensive attention and research,but the accuracy and precision of the prediction have to be further ***,this paper reviews the PV power prediction methods from five aspects:influencing factors,evaluation indexes,prediction status,difficulties and future *** summarizes the current difficulties in prediction based on an in-depth analysis of the current research status of physical methods based on the classification ofmodel features,statistical methods,artificial intelligence methods,and combinedmethods of ***,the development trend ofPVpower generation prediction technology and possible future research directions are envisioned.

关键词： Photovoltaic power generation power prediction artificial intelligence algorithm

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