Archives of Computational Methods in Engineering最新文献_第9页

Machine Learning in Healthcare Analytics: A State-of-the-Art Review 医疗分析中的机器学习：最新技术回顾

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-04-04 DOI: 10.1007/s11831-024-10098-3

Surajit Das, Samaleswari P. Nayak, Biswajit Sahoo, Sarat Chandra Nayak

{"title":"Machine Learning in Healthcare Analytics: A State-of-the-Art Review","authors":"Surajit Das, Samaleswari P. Nayak, Biswajit Sahoo, Sarat Chandra Nayak","doi":"10.1007/s11831-024-10098-3","DOIUrl":"10.1007/s11831-024-10098-3","url":null,"abstract":"<div><p>The use of machine learning (ML) models have become a crucial factor in the growing field of healthcare, ushering in a new era of medical research and diagnosis. This study rigorously reviews research publications published in reputable journals during the last five years. The pace and dynamic nature of machine learning in the healthcare domains demonstrated by the arduous criteria, which are used to sort through these articles. Disease-centric analysis uncovered a wide range of deep learning and machine learning models which are designed to address particular medical problems. Convolutional neural networks (CNNs), one of the most complex deep learning architectures, coexist with more conventional statistical models like logistic regression and support vector machines. CNNs are particularly prominent when it comes to disorders that need picture processing, which highlights the significant influence of deep learning in deciphering complex medical patterns. The popularity of ensemble methods, such as Random Forest, Gradient Boosting, and AdaBoost, indicates that their ability to combine predictive capability and strengthen model resilience is well acknowledged. Hybrid techniques, which integrate the advantages of many models, provide novel approaches to tackle distinct healthcare problems. This research also sheds light on a nuanced approach for model selection, wherein deep learning models performs well with huge datasets and image analysis, while statistical and ensemble models provides better results with numerical and categorical data. The adaptability needed in healthcare analytics is shown by hybrid models, which frequently combine standard models for classification with deep learning for feature extraction. The present review can endow problems related to ML in healthcare domain, possible solutions, potential directions and some knowledge to the researchers working in this field.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3923 - 3962"},"PeriodicalIF":9.7,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constructing Nitsche’s Method for Variational Problems 为变量问题构建尼采方法

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-04-03 DOI: 10.1007/s11831-023-09953-6

Joseph Benzaken, John A. Evans, Rasmus Tamstorf

{"title":"Constructing Nitsche’s Method for Variational Problems","authors":"Joseph Benzaken, John A. Evans, Rasmus Tamstorf","doi":"10.1007/s11831-023-09953-6","DOIUrl":"10.1007/s11831-023-09953-6","url":null,"abstract":"<div><p>Nitsche’s method is a well-established approach for weak enforcement of boundary conditions for partial differential equations (PDEs). It has many desirable properties, including the preservation of variational consistency and the fact that it yields symmetric, positive-definite discrete linear systems that are not overly ill-conditioned. In recent years, the method has gained in popularity in a number of areas, including isogeometric analysis, immersed methods, and contact mechanics. However, arriving at a formulation based on Nitsche’s method can be a mathematically arduous process, especially for high-order PDEs. Fortunately, the derivation is conceptually straightforward in the context of variational problems. The goal of this paper is to elucidate the process through a sequence of didactic examples. First, we show the derivation of Nitsche’s method for Poisson’s equation to gain an intuition for the various steps. Next, we present the abstract framework and then revisit the derivation for Poisson’s equation to use the framework and add mathematical rigor. In the process, we extend our derivation to cover the vector-valued setting. Armed with a basic recipe, we then show how to handle a higher-order problem by considering the vector-valued biharmonic equation and the linearized Kirchhoff–Love plate. In the end, the hope is that the reader will be able to apply Nitsche’s method to any problem that arises from variational principles.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 4","pages":"1867 - 1896"},"PeriodicalIF":9.7,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Review: Applications of the Kozeny–Carman Model in Engineering with Permeability Dynamics 全面回顾：科泽尼-卡曼模型在渗透动力学工程中的应用

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-04-01 DOI: 10.1007/s11831-024-10094-7

Maryam Rehman, Muhammad Bilal Hafeez, Marek Krawczuk

引用次数: 0

A Systematic Review on Game-Theoretic Models and Different Types of Security Requirements in Cloud Environment: Challenges and Opportunities 关于云环境中游戏理论模型和不同类型安全要求的系统综述：挑战与机遇

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-04-01 DOI: 10.1007/s11831-024-10095-6

Komal Singh Gill, Anju Sharma, Sharad Saxena

{"title":"A Systematic Review on Game-Theoretic Models and Different Types of Security Requirements in Cloud Environment: Challenges and Opportunities","authors":"Komal Singh Gill, Anju Sharma, Sharad Saxena","doi":"10.1007/s11831-024-10095-6","DOIUrl":"10.1007/s11831-024-10095-6","url":null,"abstract":"<div><p>The presented survey paper explores the application of game theoretic models for addressing security challenges in cloud computing environments. It highlights the significance of cloud computing as an integral part of modern technology due to its accessibility, scalability, and cost-effectiveness. However, the paper acknowledges that security issues pose a considerable concern in cloud computing, surpassing the effectiveness of traditional security measures. To overcome these challenges, the paper focuses on game theory as a valuable framework for modeling security scenarios by considering strategic interactions among multiple parties with conflicting interests. By analyzing existing research, the presented paper investigates the practical utilization of game theoretic models to enhance security in real-world cloud computing environments. The findings suggest that while game theory holds promise in offering effective security solutions, further research is imperative to address the practical limitations of these models in the context of cloud computing.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3857 - 3890"},"PeriodicalIF":9.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Metaheuristic Perspective on Extracting Numeric Association Rules: Current Works, Applications, and Recommendations 提取数值关联规则的元启发式视角：当前工作、应用和建议

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-29 DOI: 10.1007/s11831-024-10109-3

Salma Yacoubi, Ghaith Manita, Amit Chhabra, Ouajdi Korbaa

{"title":"A Metaheuristic Perspective on Extracting Numeric Association Rules: Current Works, Applications, and Recommendations","authors":"Salma Yacoubi, Ghaith Manita, Amit Chhabra, Ouajdi Korbaa","doi":"10.1007/s11831-024-10109-3","DOIUrl":"10.1007/s11831-024-10109-3","url":null,"abstract":"<div><p>In the vast field of data mining, the increasing significance of Numerical Association Rule Mining (NARM) lies in its capacity to unearth recurrent patterns and correlations across diverse attribute types, resonating across multifarious sectors such as healthcare, commercial databases, and beyond. This article explores in depth the intricacies of optimization algorithms and metaheuristic approaches within the NARM framework, highlighting their essential role in amplifying the effectiveness and computational efficiency of the algorithms developed. In particular, the integration of metaheuristic optimization appears to be a significant advance, improving the accuracy and reliability of derived rules while avoiding the computational rigors of conventional processes. Exploration in this study, covers various areas of association rules, including numerical, fuzzy and high-utility sets, providing a solid synthesis of a meta-study and offering a holistic view that interweaves historical, methodological and future-oriented perspectives, thus seeking to immerse future research efforts in a comprehensive understanding of the incessant optimization approaches inherent in NARM’s vast scope in data mining. In particular, this survey considered the extensive metaheuristic-based NARM research works between 2015 and 2023. Initially commencing with a large corpus of 19,500 papers, a stringent filtration process was employed, resulting in the identification of 180 pertinent papers that contributed significantly to this survey.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4087 - 4128"},"PeriodicalIF":9.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Optimization Techniques: A Survey, Classification, Challenges, and Future Research Issues 机器学习优化技术：调查、分类、挑战和未来研究课题

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-29 DOI: 10.1007/s11831-024-10110-w

Kewei Bian, Rahul Priyadarshi

{"title":"Machine Learning Optimization Techniques: A Survey, Classification, Challenges, and Future Research Issues","authors":"Kewei Bian, Rahul Priyadarshi","doi":"10.1007/s11831-024-10110-w","DOIUrl":"10.1007/s11831-024-10110-w","url":null,"abstract":"<div><p>Optimization approaches in machine learning (ML) are essential for training models to obtain high performance across numerous domains. The article provides a comprehensive overview of ML optimization strategies, emphasizing their classification, obstacles, and potential areas for further study. We proceed with studying the historical progression of optimization methods, emphasizing significant developments and their influence on contemporary algorithms. We analyse the present research to identify widespread optimization algorithms and their uses in supervised learning, unsupervised learning, and reinforcement learning. Various common optimization constraints, including non-convexity, scalability issues, convergence problems, and concerns about robustness and generalization, are also explored. We suggest future research should focus on scalability problems, innovative optimization techniques, domain knowledge integration, and improving interpretability. The present study aims to provide an in-depth review of ML optimization by combining insights from historical advancements, literature evaluations, and current issues to guide future research efforts.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4209 - 4233"},"PeriodicalIF":9.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational Modelling and Mechanical Characteristics of Polymeric Hybrid Composite Materials: An Extensive Review 聚合物混合复合材料的计算建模和机械特性：广泛综述

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-28 DOI: 10.1007/s11831-024-10097-4

Ankit Gangwar, Vikash Kumar, Murat Yaylaci, Subrata Kumar Panda

引用次数: 0

The Role of Machine Learning in Earthquake Seismology: A Review 机器学习在地震地震学中的作用：综述

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-28 DOI: 10.1007/s11831-024-10099-2

Anup Chitkeshwar

引用次数: 0

A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing 图像处理中的多级阈值分割方法综述

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-27 DOI: 10.1007/s11831-024-10093-8

Mohammad Amiriebrahimabadi, Zhina Rouhi, Najme Mansouri

引用次数: 0

Regression Method in Data Mining: A Systematic Literature Review 数据挖掘中的回归方法：系统性文献综述

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-27 DOI: 10.1007/s11831-024-10088-5

Mohammad Vahid Sebt, Yaser Sadati-Keneti, Misagh Rahbari, Zohreh Gholipour, Hamid Mehri

{"title":"Regression Method in Data Mining: A Systematic Literature Review","authors":"Mohammad Vahid Sebt, Yaser Sadati-Keneti, Misagh Rahbari, Zohreh Gholipour, Hamid Mehri","doi":"10.1007/s11831-024-10088-5","DOIUrl":"10.1007/s11831-024-10088-5","url":null,"abstract":"<div><p>Regression is one of the most important supervised learning methods in data mining that is used to predict and discover knowledge in data mining science. After reviewing the studies conducted in the field of regression, it has been found that the tendency to use this method is increasing day by day among researchers. This study reviews 500 articles from about 230 reputable journals under one framework over the twenty-first century and also discusses the status and use of regression in data mining research. The systematic framework presented in this study includes the following steps: 1—Examining the position of regression in research conducted in data mining and determining the trend of different journals to conduct research in the field of regression in different years 2—Examining different study areas in the field of regression and determining the trend to conduct research in various areas of study in different years 3—Examining the algorithms used in the field of regression and determining the most widely used and trend to use algorithms by researchers in different years 4—Examining the keywords used in regression research in data mining and determining the strongest and most attractive rules obtained from the relationships of these keywords with each other using the Apriori algorithm.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3515 - 3534"},"PeriodicalIF":9.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140316030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0