Pub Date : 2024-07-25DOI: 10.54254/2755-2721/55/20241403
Keqian Liu, Ang Li, Xinran Lin, Zhuobin Mao, Weiyang Zhang
This paper examines the accuracy of stock price rise-or-fall predictions of seven different machine learning algorithms, including support vector machines and random forests, for three industry types: securities, banks, and Internet companies. The purpose of the research is to explore the effects of different models in the stock market, so as to help people choose the optimal machine learning model in predicting different types of stocks. The study produced nine features based on the study by Patel et al for prediction. By collecting 9 types of stock data from companies in different industries, we performed necessary preprocessing on the data, fitted the model, tuned the parameters of the model and get the prediction result. Through the result, we found that the random forest algorithm has obvious advantages in binary classification prediction of stock prices. Linear discriminant analysis (LDA), Quadratic Discriminant Analysis (QDA) and logistic regression also have good fitting effects in this type of problem. K-Nearest Neighbor (KNN) and Naive Bayes algorithms exhibit poor prediction accuracy.
{"title":"Empirical study on the performance of various machine learning models in predicting stock price movements as a binary classification task","authors":"Keqian Liu, Ang Li, Xinran Lin, Zhuobin Mao, Weiyang Zhang","doi":"10.54254/2755-2721/55/20241403","DOIUrl":"https://doi.org/10.54254/2755-2721/55/20241403","url":null,"abstract":"This paper examines the accuracy of stock price rise-or-fall predictions of seven different machine learning algorithms, including support vector machines and random forests, for three industry types: securities, banks, and Internet companies. The purpose of the research is to explore the effects of different models in the stock market, so as to help people choose the optimal machine learning model in predicting different types of stocks. The study produced nine features based on the study by Patel et al for prediction. By collecting 9 types of stock data from companies in different industries, we performed necessary preprocessing on the data, fitted the model, tuned the parameters of the model and get the prediction result. Through the result, we found that the random forest algorithm has obvious advantages in binary classification prediction of stock prices. Linear discriminant analysis (LDA), Quadratic Discriminant Analysis (QDA) and logistic regression also have good fitting effects in this type of problem. K-Nearest Neighbor (KNN) and Naive Bayes algorithms exhibit poor prediction accuracy.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"57 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/69/20241497
Xuyang Zhang, Lidong Xu, Ningxin Li, Jianke Zou
Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.
{"title":"Research on credit risk assessment optimization based on machine learning","authors":"Xuyang Zhang, Lidong Xu, Ningxin Li, Jianke Zou","doi":"10.54254/2755-2721/69/20241497","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241497","url":null,"abstract":"Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"72 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/55/20241440
Fangyu Sun
For a long time, the academic community has been searching for the best strategy to replenish inventory from multiple suppliers. To address these optimization problems,inventory managers need to decide how much to order from each vendor in the case of net inventory and outstanding orders in order to minimize the expected backlog,holding and procurementocsts jointly.Especially in terms of perishable products, there are many factors to consider. Scholars have been studying this issue for a long time, and there are many factors that need to be considered, such as how to minimize procurement costs. This article incorporates dynamic inventory and dynamic demand into the design of recurrent neural networks from the perspective of neural networks. The results indicate that using deep neural network optimization methods can obtain high-quality solutions and open up a new approach for effective management of complex high-dimensional inventory dynamics
{"title":"Research on the dual source inventory system of perishable product warehouse using recurrent neural networks","authors":"Fangyu Sun","doi":"10.54254/2755-2721/55/20241440","DOIUrl":"https://doi.org/10.54254/2755-2721/55/20241440","url":null,"abstract":"For a long time, the academic community has been searching for the best strategy to replenish inventory from multiple suppliers. To address these optimization problems,inventory managers need to decide how much to order from each vendor in the case of net inventory and outstanding orders in order to minimize the expected backlog,holding and procurementocsts jointly.Especially in terms of perishable products, there are many factors to consider. Scholars have been studying this issue for a long time, and there are many factors that need to be considered, such as how to minimize procurement costs. This article incorporates dynamic inventory and dynamic demand into the design of recurrent neural networks from the perspective of neural networks. The results indicate that using deep neural network optimization methods can obtain high-quality solutions and open up a new approach for effective management of complex high-dimensional inventory dynamics","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"12 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/55/20241422
Luyang Yu, Shanglin Han, Muheng He, Zekai Yang, Xinyue Hu
The rapid expansion of e-commerce has led to product oversaturation on online platforms. To help users find their preferred products in a more efficient and accurate way, many e-commerce platforms have introduced personalized recommendation systems. Collaborative filtering is one of the most successful techniques, while its improvement, Invariant Collaborative Filtering (Inv-CF), aims to address the popularity bias problem of traditional CF models by capturing unbiased preferences that remain constant despite the change in popularity distributions. However, Inv-CF model still experiences some problems such as ignoring the influence of attention, causing performance less effective when analyzing the representation of implicit feedback. This paper explores the enhancement of Inv-CF, a recommendation system model designed to mitigate the influence of popularity bias. We defined the experimental framework and evaluated the performance of the improved Inv-CF on two benchmark datasets, Yahoo! R3 and COAT. And the results demonstrate significant performance gains over the original Inv-CF, highlighting the effectiveness of the proposed enhancements. In conclusion, this paper presents improvements to the Inv-CF model's loss functions, addressing issues related to popularity bias in collaborative filtering.
{"title":"Improvement research of Invariant Collaborative Filtering","authors":"Luyang Yu, Shanglin Han, Muheng He, Zekai Yang, Xinyue Hu","doi":"10.54254/2755-2721/55/20241422","DOIUrl":"https://doi.org/10.54254/2755-2721/55/20241422","url":null,"abstract":"The rapid expansion of e-commerce has led to product oversaturation on online platforms. To help users find their preferred products in a more efficient and accurate way, many e-commerce platforms have introduced personalized recommendation systems. Collaborative filtering is one of the most successful techniques, while its improvement, Invariant Collaborative Filtering (Inv-CF), aims to address the popularity bias problem of traditional CF models by capturing unbiased preferences that remain constant despite the change in popularity distributions. However, Inv-CF model still experiences some problems such as ignoring the influence of attention, causing performance less effective when analyzing the representation of implicit feedback. This paper explores the enhancement of Inv-CF, a recommendation system model designed to mitigate the influence of popularity bias. We defined the experimental framework and evaluated the performance of the improved Inv-CF on two benchmark datasets, Yahoo! R3 and COAT. And the results demonstrate significant performance gains over the original Inv-CF, highlighting the effectiveness of the proposed enhancements. In conclusion, this paper presents improvements to the Inv-CF model's loss functions, addressing issues related to popularity bias in collaborative filtering.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"33 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241588
Zewen Guo
This review has discussed framework of the decision making in automatic vehicles which is rarely adopted in current researches. Autonomous driving, a step forward in assisted driving technology and the rapid advancement of automotive electronics, has become an essential means to address traffic issues in the future. This area has been a major focus for research on technology worldwide. The history of human transportation has been significantly altered by autonomous driving in recent years. This paper will concisely outline the evolution of this technology and its associated components. On this basis, this paper also reviews the development in different sorts of decision making. It also analyses characteristics, as well as their advantages and disadvantages of some typical application among the different decision making. Summarizing the current predicaments of automated driving, this paper looks to what lies ahead for autonomous driving technology's future development.
{"title":"A review of decision-making frameworks for autonomous vehicles","authors":"Zewen Guo","doi":"10.54254/2755-2721/79/20241588","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241588","url":null,"abstract":"This review has discussed framework of the decision making in automatic vehicles which is rarely adopted in current researches. Autonomous driving, a step forward in assisted driving technology and the rapid advancement of automotive electronics, has become an essential means to address traffic issues in the future. This area has been a major focus for research on technology worldwide. The history of human transportation has been significantly altered by autonomous driving in recent years. This paper will concisely outline the evolution of this technology and its associated components. On this basis, this paper also reviews the development in different sorts of decision making. It also analyses characteristics, as well as their advantages and disadvantages of some typical application among the different decision making. Summarizing the current predicaments of automated driving, this paper looks to what lies ahead for autonomous driving technology's future development.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"45 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/69/20241513
Xinyuan Lu
Since the outbreak of COVID-19 at the end of 2019, this global public health crisis has profoundly impacted the socio-economic conditions and daily life of countries worldwide. To effectively combat the pandemic, scientists and public health experts rely on vast amounts of data to track the progression of the disease, evaluate the effectiveness of control measures, and predict future trends. Big data technology plays a crucial role in the analysis of pandemic data and trend forecasting. This paper will explore the methods of analyzing COVID-19 pandemic data and the application of trend forecasting.
{"title":"Analysis and trend prediction of COVID-19 pandemic data based on big data visualization","authors":"Xinyuan Lu","doi":"10.54254/2755-2721/69/20241513","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241513","url":null,"abstract":"Since the outbreak of COVID-19 at the end of 2019, this global public health crisis has profoundly impacted the socio-economic conditions and daily life of countries worldwide. To effectively combat the pandemic, scientists and public health experts rely on vast amounts of data to track the progression of the disease, evaluate the effectiveness of control measures, and predict future trends. Big data technology plays a crucial role in the analysis of pandemic data and trend forecasting. This paper will explore the methods of analyzing COVID-19 pandemic data and the application of trend forecasting.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"22 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241272
Yanzhe Wu, Zhan Yang
The purpose of this review paper is to explore the development history, core algorithms, application domains, and future trends of recommendation systems. In the era of information overload, recommendation systems are essential tools that have proven to be highly successful in diverse fields, such as e-commerce, social media, and movie recommendations. The paper examines various types of recommendation systems, including collaborative filtering, content filtering, and deep learning methods, analyzing their strengths and limitations. By delving into the intricate details of these systems, this study provides valuable insights into the advancements and challenges in recommendation technology. Understanding the evolution and capabilities of recommendation systems is crucial in harnessing their potential and improving user experiences in the dynamic digital landscape.
{"title":"Unveiling the landscape of recommendation systems: Evolution, algorithms, applications, and future prospects","authors":"Yanzhe Wu, Zhan Yang","doi":"10.54254/2755-2721/79/20241272","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241272","url":null,"abstract":"The purpose of this review paper is to explore the development history, core algorithms, application domains, and future trends of recommendation systems. In the era of information overload, recommendation systems are essential tools that have proven to be highly successful in diverse fields, such as e-commerce, social media, and movie recommendations. The paper examines various types of recommendation systems, including collaborative filtering, content filtering, and deep learning methods, analyzing their strengths and limitations. By delving into the intricate details of these systems, this study provides valuable insights into the advancements and challenges in recommendation technology. Understanding the evolution and capabilities of recommendation systems is crucial in harnessing their potential and improving user experiences in the dynamic digital landscape.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"30 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241401
Shaopeng Cheng
With the unexpected spread of Covid-19 in 2019, such disease took away millions of peoples lives. Therefore, investigating and curing Covid-19 become a very mandatory issue in different areas, such as biology, medicine, and statistics. This paper investigates different models of CNN in deep learning of computers in analyzing X-ray pictures of normal pneumonia and Covid-19 caused pneumonia patients. The database is from Kaggle and contains over 8000 images of X-rays of the chest. Besides, this paper discusses the imaging process technology, such as ConvNeXt, to edit X-ray images more convenient for computers to analyze and dispose of. According to the comparison of the sequential model and DenseNet model in CNN, the sequential model has better performance and accuracy. In the conclusion part, this paper also investigates whether better image processing work can improve the performance of models. Overall, these results shed light on guiding further exploration of both analyzing and distinguishing Covid-19 patients and normal pneumonia patients in order to decrease the work of hospitals and cure different patients in time.
随着 Covid-19 在 2019 年的意外传播，这种疾病夺走了数百万人的生命。因此，研究和治疗 Covid-19 成为生物学、医学和统计学等不同领域的一个非常重要的课题。本文研究了计算机深度学习中的 CNN 在分析正常肺炎和 Covid-19 引起的肺炎患者的 X 光图片时的不同模型。数据库来自 Kaggle，包含 8000 多张胸部 X 光图片。此外，本文还讨论了 ConvNeXt 等成像处理技术，以编辑 X 光图像，更方便计算机分析和处置。根据 CNN 中顺序模型和 DenseNet 模型的比较，顺序模型具有更好的性能和准确性。在结论部分，本文还研究了更好的图像处理工作是否能提高模型的性能。总之，这些结果为进一步探索分析和区分 Covid-19 患者和正常肺炎患者提供了指导，以减少医院的工作量，及时治愈不同的患者。
{"title":"Classification of pneumonia caused by Covid-19 based on deep learning model","authors":"Shaopeng Cheng","doi":"10.54254/2755-2721/79/20241401","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241401","url":null,"abstract":"With the unexpected spread of Covid-19 in 2019, such disease took away millions of peoples lives. Therefore, investigating and curing Covid-19 become a very mandatory issue in different areas, such as biology, medicine, and statistics. This paper investigates different models of CNN in deep learning of computers in analyzing X-ray pictures of normal pneumonia and Covid-19 caused pneumonia patients. The database is from Kaggle and contains over 8000 images of X-rays of the chest. Besides, this paper discusses the imaging process technology, such as ConvNeXt, to edit X-ray images more convenient for computers to analyze and dispose of. According to the comparison of the sequential model and DenseNet model in CNN, the sequential model has better performance and accuracy. In the conclusion part, this paper also investigates whether better image processing work can improve the performance of models. Overall, these results shed light on guiding further exploration of both analyzing and distinguishing Covid-19 patients and normal pneumonia patients in order to decrease the work of hospitals and cure different patients in time.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"98 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241551
Xiang Huang
This paper explores three fundamental machine learning techniqueslinear regression, k-means clustering, and decision treesand their applications in predictive modeling. In the era of data proliferation, machine learning stands at the intersection of computer science and artificial intelligence, playing a pivotal role in algorithm and model development for enhanced predictions and decision-making. The study delves into the intricacies of these techniques, starting with a focus on linear regression, a supervised learning algorithm for establishing relationships between independent and dependent variables. The process involves data preparation, exploration, feature selection, model building, and evaluation. A practical example demonstrates the application of linear regression in analyzing the relationship between income and happiness. The exploration then extends to k-means clustering, an unsupervised learning algorithm used for grouping unlabeled datasets into distinct clusters. The iterative nature of k-means involves assigning data points to clusters based on centroid proximity, contributing to efficient data exploration. A graphical representation illustrates the step-by-step process of data point grouping and centroid recalibration. The advantages of k-means, including computational efficiency and simplicity, are discussed, along with considerations such as sensitivity to initialization and the manual specification of the number of clusters. The paper concludes with an examination of decision trees, versatile algorithms used for both classification and regression tasks. Decision trees construct hierarchical structures based on features, facilitating straightforward decision-making processes. A practical example illustrates how decision trees assess credit risk based on credit history and loan term. The strengths of decision trees, such as visual representation and non-linear pattern capture, are outlined, alongside considerations like overfitting. In summary, this paper provides insights into the strengths, limitations, and applications of linear regression, k-means clustering, and decision trees. These techniques offer valuable tools in data analysis and prediction, with their effectiveness dependent on specific problem domains and datasets. The study contributes to a comprehensive understanding of these machine learning methods and suggests future research directions, including exploring advanced variations and real-world applications.
{"title":"Predictive Models: Regression, Decision Trees, and Clustering","authors":"Xiang Huang","doi":"10.54254/2755-2721/79/20241551","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241551","url":null,"abstract":"This paper explores three fundamental machine learning techniqueslinear regression, k-means clustering, and decision treesand their applications in predictive modeling. In the era of data proliferation, machine learning stands at the intersection of computer science and artificial intelligence, playing a pivotal role in algorithm and model development for enhanced predictions and decision-making. The study delves into the intricacies of these techniques, starting with a focus on linear regression, a supervised learning algorithm for establishing relationships between independent and dependent variables. The process involves data preparation, exploration, feature selection, model building, and evaluation. A practical example demonstrates the application of linear regression in analyzing the relationship between income and happiness. The exploration then extends to k-means clustering, an unsupervised learning algorithm used for grouping unlabeled datasets into distinct clusters. The iterative nature of k-means involves assigning data points to clusters based on centroid proximity, contributing to efficient data exploration. A graphical representation illustrates the step-by-step process of data point grouping and centroid recalibration. The advantages of k-means, including computational efficiency and simplicity, are discussed, along with considerations such as sensitivity to initialization and the manual specification of the number of clusters. The paper concludes with an examination of decision trees, versatile algorithms used for both classification and regression tasks. Decision trees construct hierarchical structures based on features, facilitating straightforward decision-making processes. A practical example illustrates how decision trees assess credit risk based on credit history and loan term. The strengths of decision trees, such as visual representation and non-linear pattern capture, are outlined, alongside considerations like overfitting. In summary, this paper provides insights into the strengths, limitations, and applications of linear regression, k-means clustering, and decision trees. These techniques offer valuable tools in data analysis and prediction, with their effectiveness dependent on specific problem domains and datasets. The study contributes to a comprehensive understanding of these machine learning methods and suggests future research directions, including exploring advanced variations and real-world applications.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"16 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241597
Yucheng Zhang
The study of robot research necessitates the exploration of path planning. The goal of this paper is to enable multiple robots with different functions to reach the designated place quickly and accurately in the orchard environment. In comparison, avoiding collisions, deadlocks, and other complications. This paper has chosen to employ the upgraded A-star algorithm in the global layer as its strategy. An improved DWA algorithm is used in the local layer. In addition, the Voronoi graph method is introduced to limit the search area and solve the obstacle avoidance problem of multiple robots. The left-turn method is applied to the deadlock problem of multiple robots. Finally, the obstacle avoidance efficiency in a multi-obstacle environment is simulated to verify the algorithm's effectiveness.
机器人研究必须对路径规划进行探索。本文的目标是使具有不同功能的多个机器人在果园环境中快速、准确地到达指定地点。相比之下，避免碰撞、死锁和其他复杂情况。本文选择在全局层采用升级版 A-star 算法作为策略。在局部层采用了改进的 DWA 算法。此外，还引入了 Voronoi 图法来限制搜索区域，解决多机器人避障问题。左转法被应用于多个机器人的死锁问题。最后，模拟了多障碍物环境下的避障效率，以验证算法的有效性。
{"title":"An improved DWA algorithm in agricultural robot","authors":"Yucheng Zhang","doi":"10.54254/2755-2721/79/20241597","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241597","url":null,"abstract":"The study of robot research necessitates the exploration of path planning. The goal of this paper is to enable multiple robots with different functions to reach the designated place quickly and accurately in the orchard environment. In comparison, avoiding collisions, deadlocks, and other complications. This paper has chosen to employ the upgraded A-star algorithm in the global layer as its strategy. An improved DWA algorithm is used in the local layer. In addition, the Voronoi graph method is introduced to limit the search area and solve the obstacle avoidance problem of multiple robots. The left-turn method is applied to the deadlock problem of multiple robots. Finally, the obstacle avoidance efficiency in a multi-obstacle environment is simulated to verify the algorithm's effectiveness.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"54 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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