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Characterization of spatio-temporal patterns of grassland utilization intensity in the Selinco watershed of the Qinghai-Tibetan Plateau from 2001 to 2019 based on multisource remote sensing and artificial intelligence algorithms

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GISCIENCE & REMOTE SENSING 2022年第1期59卷 2217-2246页

作者： Ma, Changhui Xie, Yaowen Duan, Si-Bo Qin, Wenhua Guo, Zecheng Xi, Guilin Zhang, Xueyuan Bie, Qiang Duan, Hanming He, Lei Lanzhou Univ Coll Earth & Environm Sci Lanzhou 730000 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Key Lab Agr Remote Sensing Minist Agr Beijing 100081 Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Peoples R China Neijiang Normal Univ Sch Geog & Resource Sci Neijiang 641100 Peoples R China Lanzhou Univ Coll Earth & Environm Sci South Tianshui Rd Lanzhou 730000 Peoples R China

Due to the limitations of spatial quantification methods, the spatio-temporal patterns of grassland utilization intensity (GUI) in the Selinco watershed (SLCW), the core region of ecological security on the Qinghai-Tibetan Plateau, is unclear under multiple utilization modes. This paper quantified GUI by constructing the association between the potential and actual Enhanced Vegetation Index (EVI) of grasslands in terms of interannual variability. To obtain an accurate spatio-temporal dataset of potential EVI, the following two components were considered on. Firstly, the temporal lag effects of each raw climate factor were investigated to determine the optimal climate variables affecting vegetation productivity. Secondly, four machine learning (ML) algorithms, including an artificial neural network, random forest, support vector machine, and gradient boosting regression tree combined with the Bayesian model average, were used to construct grassland potential EVI models involving EVI, grassland type, and environmental factors (topography, soil, raw climate, and bioclimatic). Meanwhile, to maximize the performance of ML models, variable selection, variable transformation, and hyperparameter optimization were systematically implemented, where the hyperparameter optimization algorithms employ the grid search algorithm, Bayesian optimization, genetic algorithm, and particle swarm optimization. Then, the spatio-temporal dataset of GUI in the SLCW from 2001 to 2019 was established by using the above quantification method based on multisource remote sensing and artificial intelligence algorithms. The analysis of spatio-temporal variation in GUI showed that the implementation of ecological restoration projects leads to a significant and rapid decline in the overall GUI of the SLCW after 2010 (declining by 4.8%), which is more obvious in the non-nature reserve (declining by 9.3%). In the Qiangtang Nature Reserve within the SLCW, although the GUI shows a declining trend after 2

关键词： Grassland utilization intensity spatial quantification method artificial intelligence algorithms spatio-temporal patterns Qinghai-Tibetan Plateau nature reserve

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Research on Digital Economy Data Processing Systems Based on artificial intelligence algorithms 24

Research on Digital Economy Data Processing Systems Based on...

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4th International Conference on Internet of Things and Machine Learning

作者： Mao, Ganfeng EAC Emilio Aguinaldo Coll Manila Philippines

ISBN: (纸本)9798400710353

In the context of the rapid development of the digital economy, data processing technology has become a crucial means for enterprises and organizations to gain competitive advantages. This paper aims to study a digital economy data processing system based on artificial intelligence algorithms, exploring how advanced machine learning and deep learning technologies can efficiently process and analyze large volumes of heterogeneous data. By analyzing existing data processing challenges and designing and implementing system architecture and core algorithms, this paper proposes an innovative system solution. Experimental results show that the system excels in data preprocessing, analysis, and prediction, significantly improving the efficiency and accuracy of data processing. This research not only provides new insights into data processing in the context of the digital economy but also offers valuable references for academic research and practical applications in related fields.

关键词： Digital Economy artificial intelligence algorithms Data Processing Machine Learning

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Research on Smart Teaching Cloud Platform Based on artificial intelligence algorithms 24

Research on Smart Teaching Cloud Platform Based on Artificia...

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4th International Conference on Internet of Things and Machine Learning

作者： Wang, Dong Changchun Polytech Coll Informat Changchun Peoples R China

ISBN: (纸本)9798400710353

In the field of modern education, digital smart teaching cloud platforms are becoming an important trend in teaching reform. This study aims to explore the design and application of digital smart teaching cloud platforms based on artificial intelligence algorithms. Combining personal experience in educational technology, we analyze the specific applications of AI in personalized teaching, intelligent recommendation systems, and natural language processing. Through in-depth case analysis and data research, we find that these platforms not only improve teaching efficiency but also greatly enhance the student learning experience. However, challenges remain in building these platforms, such as data privacy issues and content quality control. This paper provides new ideas and suggestions for the future development of smart teaching cloud platforms through detailed examples and unique insights.

关键词： artificial intelligence algorithms digital smart teaching personalized learning intelligent recommendation system

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Pronunciation Correction for Learners of Oral English in College English under artificial intelligence algorithms: Based on Pronunciation Correction Model of Graph Neural Network

Pronunciation Correction for Learners of Oral English in Col...

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International Conference on AI and Metaverse in Supply Chain Management (AIMSCM)

作者： Gao, Jie Wuhan Polytech Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9798400708251

The traditional method of correcting oral English pronunciation is based on manual experience. However, this method has the problem of low accuracy in correcting oral English pronunciation, and the actual application effect is not ideal. The purpose of this article was to provide an efficient and accurate pronunciation correction method through graph neural networks, enabling learners to better master oral English pronunciation and improve communication skills. Graph neural networks have utilized the characteristics of graph structures to accurately analyze and correct learners' pronunciation problems, helping them better improve their oral expression abilities. The graph neural network-based pronunciation correction model has provided a new pronunciation correction method for college English oral learners. The experimental results of this article indicated that the number of students with pronunciation accuracy, fluency, and expression ability scores above 82 after manual correction was 14, 13, and 10 fewer than those with pronunciation accuracy, fluency, and expression ability scores above 82 after graph neural network correction, respectively.

关键词： artificial intelligence algorithms Oral English Graph Neural Network Pronunciation Correction Model Learner Pronunciation Correction

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English-Speaking Teaching System with Natural Language Processing Technology Based on artificial intelligence algorithms 10

English-Speaking Teaching System with Natural Language Proce...

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10th International Conference on Information and Education Technology (ICIET)

作者： Shi, Jing Ni, Li Su, Zhihao Gannan Normal Univ Sci & Technol Coll Ganzhou Peoples R China Guangzhou Coll Appl Sci & Technol Guangzhou Peoples R China Guangzhou Coll Appl Sci & Technol Comp Sch Guangzhou Peoples R China

ISBN: (纸本)9781665480482

English speaking is a very important skill of English ability for students. At the same time, English speaking learning needs a demanding environment. Because English teachers have heavy teaching tasks and limited time. This research developed an English-speaking teaching system with natural language processing technology based on artificial intelligence algorithms, which can solve this problem very well. The system engages the most advanced natural language processing technology based on artificial intelligence algorithms to correct and feedback students' oral problems in a timely, fast, and targeted manner, which is beneficial to improve students' speaking learning efficiency and interest. This system proposes an improved Bidirectional Encoder Representations from Transformers model, which is simply called BERT and based on Transformer's bidirectional encoding representation. The accuracy of this algorithm for spoken English recognition is significantly higher than other classical algorithms. This system has been tested and evaluated in some universities, and the learning efficiency and satisfaction of students is significantly higher than other oral English learning systems.

关键词： English education formatting natural language processing technology artificial intelligence algorithms

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Interactive Teaching Path Analysis of Higher Vocational Ideological and Political Courses Based on artificial intelligence algorithms 11

Interactive Teaching Path Analysis of Higher Vocational Ideo...

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11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Zhang Yunfang Wang Yuexi Shandong Inst Commerce & Technol Sch Marxism Jinan 250103 Shandong Peoples R China

ISBN: (纸本)9781728121659

In today's society, science and technology are the primary productive forces, and the reform of any mode can not be separated from science and technology, especially in education and teaching. Most vocational college students lack good learning habits, poor understanding of classroom knowledge, and are not good at solving all kinds of difficulties encountered in learning independently. In order to solve these problems, an interactive teaching path analysis method for ideological and political courses in Higher Vocational Colleges Based on artificial intelligence algorithm is proposed. The objective function of teaching path analysis is established and the constraints are determined. Analyse and use artificial intelligence algorithm to establish interactive teaching path research model. The experimental results show that the interactive teaching path analysis method based on artificial intelligence algorithm has higher practicability than the traditional interactive teaching path research method of Ideological and political course in Higher Vocational colleges, the interactive teaching path of Ideological and political courses in higher vocational colleges can be accurately analyzed.

关键词： artificial intelligence algorithms Research Model Teaching path Higher Vocational Education Objective Function

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Driver Behaviour Prediction Models Using artificial intelligence algorithms And Statistical Modeling

Driver Behaviour Prediction Models Using Artificial Intellig...

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作者： Yangliu Dou McMaster University

学位级别：博士

To improve the safety and comfort of intelligent vehicles, advanced driver models offer promising solutions. However, several shortcomings of these models prevent them from being widely applied in reality. To address these shortcomings, advanced artificial intelligence algorithms in conjunction with the sufficient driving environmental factors are proposed based on real-life driving data. More specifically, three typical problems will be addressed in this thesis: Mandatory Lane Changing (MLC) suggestion at the highway entrance; Discretionary Lane Changing (DLC) intention prediction; Car-Following gap model considering the effect of cuts-in from the adjacent lanes. For the MLC suggestion system, in which the main challenges are efficient decision making and high prediction accuracy of both non-merge and merge events, an additional gated branch neural network (GBNN) is proposed. The proposed GBNN algorithm not only achieves the highest accuracy among conventional binary classifiers in terms of great performance on the non-merge accuracy, the merge accuracy, and receiver operating characteristic score but also takes less time. For the DLC, we propose a recurrent neural network (RNN)-based time series classifier with a gated recurrent units (GRU) architecture to predict the surrounding vehiclesâ intention. It can predict the surrounding vehiclesâ lane changing maneuver 0.8 s in advance at a recall and precision of 99.5% and 98.7%, respectively, which outperforms conventional algorithms such as the Hidden Markov Model (HMM). Finally, drivers are typically faced with two competing challenges when following a preceding vehicle. A method is proposed to address the problem through an overall objective function of car-following gap and velocity. Based on this, seeking the strategic car-following gap translates to finding the optimal solution that minimizes the overall objective function. With the support of field data, the method along with concrete models are instantiate

关键词： DRIVER BEHAVIOUR PREDICTION MODELS artificial intelligence algorithms

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A Method and Path for Predicting the Digital Transformation of New Agricultural Management Entities Based on artificial intelligence algorithms

A Method and Path for Predicting the Digital Transformation ...

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作者： Zhao, Xiaoguang Changchun College of Finance and Economics Jilin Province Changchun130012 China

Due to the complex factors affecting the digital transformation, there is a significant deviation between the corresponding prediction results and the actual situation. Therefore, research on the digital transformation prediction method and path of new agricultural management entities based on artificial intelligence algorithms is proposed. After analyzing the driving factors of digital transformation from the perspectives of technology, market, policy, and talent, and considering that the prediction problem of digital transformation belongs to a linear and indi-visible learning problem, based on adding relaxation variables to the linear support vector, the kinetic energy of digital transformation of new agricultural management entities in the feature space was calculated. And by propagating the calculation results forward and backward in the BP artificial neural network algorithm model, the final prediction result under error convergence state is determined. In the test results, the error between the predicted proportion of digital sales channels for 101 agricultural management entities and the actual situation using the design method is only 0.08%. Based on the predicted results, specific digital transformation paths have been proposed from the perspectives of talent and policy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： artificial intelligence algorithms BP artificial Neural Network Algorithm Model Digital Transformation Driving Factors Linear Indivisibility New Agricultural Management Entities Prediction Methods Relaxation Change Transformation Momentum

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Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2024年第8期83卷 314-314页

作者： Wang, Yaxu Liu, Bin Wang, Jiwen Meng, Qingyang Liu, Zhengyu Shandong Univ Inst Geotech & Underground Engn 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Univ Sch Qilu Transportat Jinan 250061 Peoples R China Shandong Univ Sch Civil Engn Jinan 250061 Peoples R China

Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method.

关键词： Tunnel boring machine Laser-induced breakdown spectroscopy Quartz content artificial intelligence algorithms

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Prediction of main particulars of container ships using artificial intelligence algorithms

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OCEAN ENGINEERING 2022年 265卷

作者： Majnaric, Darin Segota, Sandi Baressi Lorencin, Ivan Car, Zlatan Univ Zagreb Fac Mech Engn & Naval Architecture Ul Ivana Lucica 5 Zagreb 10000 Croatia Univ Rijeka Fac Engn Vukovarska 58 Rijeka 51000 Croatia

The value of the main ship particulars are key values to determine during initial design of a vessel, but they can be complex to determine, as they depend on a large number of factors. The presented research attempts to model the main particulars: length between perpendiculars (LPP), length overall (LOA), modulated breadth (B), depth (D), draught (T), gross tonnage (GT), net tonnage (NT), deadweight (DWT), and engine power, using only key request factors: number of twenty-foot equivalent units (TEU) the vessel is expected to carry, and the expected speed of the ship (V). As this is a complex task, artificial intelligence (AI) techniques are applied to the dataset consisting of 250 container ships. Two modeling techniques are used: multilayer perceptron (MLP) and gradient boosted trees (GBT). The model hyperparameters are trained using a grid search procedure and evaluated using mean absolute percentage error (MAP E) and coefficient of determination (R2) in a 5-fold cross-validation scheme. The obtained results show that a quality model can be achieved using both techniques, except in the case of engine power for which a high-quality model has not been regressed. Models presented here can have practical application in the determination of the ship's main particulars at the preliminary design stage.

关键词： artificial intelligence algorithms Container ships Main particulars Preliminary ship design

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