Archives of Computational Methods in Engineering最新文献

{"title":"A Comparative Study and Systematic Analysis of XAI Models and their Applications in Healthcare","authors":"Jyoti Gupta,&nbsp;K. R. Seeja","doi":"10.1007/s11831-024-10103-9","DOIUrl":"10.1007/s11831-024-10103-9","url":null,"abstract":"<div><p>Artificial intelligence technologies such as machine learning and deep learning employ techniques to anticipate results more effectively without human involvement. Since AI models are viewed as opaque models, their application in healthcare is still restricted. Explainable artificial intelligence (XAI) has been designed to increase the use of artificial intelligence (AI) algorithms in the healthcare sector by increasing trust in the model's predictions and explaining how they are developed. The aim of this article is to critically review, compare, and summarize existing research and to find new research possibilities of XAI for applications in healthcare. This study is conducted by finding articles related to XAI in biological and healthcare domains from the PubMed, Science Direct, and Web of Science databases using the PRISMA method. A comparative study of the state-of-the-art XAI techniques to evaluate its applications in healthcare has also been done using an experimental demonstration on the Diabetes dataset. XAI techniques, namely LIME, SHAP, PDP, and decision tree, were used to explain how various input attributes contributed to the outcome of the model. This study found that the explanations provided by these models are not easily understandable for different users of the model, like doctors and patients, and need expertise. This study found that the potential of XAI in the medical domain is high as it increases trust in the AI model. This survey will motivate the researchers to build more XAI techniques that provide user-friendly explanations, especially for the less explored areas of medical data, such as biomedical signals and biomedical text.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3977 - 4002"},"PeriodicalIF":9.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611178","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}

{"title":"Advances in Discrete Element Modeling of Asphalt Mixture: A Literature Review","authors":"Xinman Ai,&nbsp;Junyan Yi,&nbsp;Zhongshi Pei,&nbsp;Wenyi Zhou,&nbsp;Decheng Feng","doi":"10.1007/s11831-024-10104-8","DOIUrl":"10.1007/s11831-024-10104-8","url":null,"abstract":"<div><p>The complicated composition structure of the asphalt mixture makes it difficult to determine its multi-scale performance. It is impossible to understand the internal interaction mechanism of asphalt mixture only through laboratory tests, especially under complex conditions at the microscale, which can be effectively solved by the discrete element method (DEM). This paper summarized the progress and advances in DEM modeling of asphalt mixture mainly consisting of the principle of DEM, DEM simulation for asphalt mixture, asphalt mixture compaction and mechanical behavior based on the DEM. The basis of DEM modeling is the well-known Newton's second law, through which the discrete elements are determined primarily according to the motion equation and force–displacement law. The DEM modeling of asphalt mixture often includes the simulation of coarse aggregates, asphalt mortar and air voids regardless of two-dimensional (2D) and three-dimensional (3D) DEM. The morphological characteristics of coarse aggregates and spatial distribution of air voids are essential to the simulation results of asphalt mixtures. In addition, the commonly used DEM contact models in asphalt mixture, including the <i>linear model</i>, <i>Burgers model</i> and <i>linear parallel bond model</i>, are introduced and analyzed. The main micro-parameters of various contact models are usually obtained through laboratory test results by trial and error. And the selection of contact modeling and determination of macro-parameters are discussed. Then, asphalt mixture compaction based on DEM, mainly containing Superpave gyratory compaction (SGC) and Marshall impact compaction (MIC), is estimated in this paper. It is concluded that displacement, rotation and contact stress of aggregate particles can be accurately captured similar to SmartRock. Moreover, varying mechanical behavior and the significant influencing factors based on DEM are evaluated comprehensively. Finally, further prospects in DEM modeling of asphalt mixture are proposed to promote the development and application of numerical simulation in pavement engineering.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4003 - 4029"},"PeriodicalIF":9.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560681","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}

{"title":"A Computational Framework for Precise Aerial Agricultural Spray Delivery Processes","authors":"J. O. Betancourt,&nbsp;I. Li,&nbsp;E. Mengi,&nbsp;L. Corrales,&nbsp;T. I. Zohdi","doi":"10.1007/s11831-024-10106-6","DOIUrl":"10.1007/s11831-024-10106-6","url":null,"abstract":"<div><p>As the world’s population is expected to increase, so is the global demand for food. Sustainable intensification via precision agriculture of existing farms can increase crop production. Agricultural spray drones have recently taken a physical role within precision agriculture, such as aerial application of fluids, solids, and biological control agents but have difficulties spraying in uncontrolled environments caused by wind shifting spray material away from intended target areas. This work proposes an efficient physics-based framework to provide drone operators with trajectory and spray nozzle configuration for optimal target crop-dusting to mitigate spray drifts while providing quantitative approximations of spray particle trajectory and ground concentration. The framework is coupled with a machine-learning algorithm (MLA) to aid users in their search for optimal results and includes two decoupled models that simulate wind and spray particle trajectories. In the model problem, a genetic algorithm (GA) is used to optimize the system where the optimal trajectory and spray nozzle configuration resulted in 64% of crop targets hit while only losing minimal spray material from spray drifts.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4149 - 4162"},"PeriodicalIF":9.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560510","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}

{"title":"Machine Learning in Healthcare Analytics: A State-of-the-Art Review","authors":"Surajit Das,&nbsp;Samaleswari P. Nayak,&nbsp;Biswajit Sahoo,&nbsp;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}

{"title":"Constructing Nitsche’s Method for Variational Problems","authors":"Joseph Benzaken,&nbsp;John A. Evans,&nbsp;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}

{"title":"A Comprehensive Review: Applications of the Kozeny–Carman Model in Engineering with Permeability Dynamics","authors":"Maryam Rehman,&nbsp;Muhammad Bilal Hafeez,&nbsp;Marek Krawczuk","doi":"10.1007/s11831-024-10094-7","DOIUrl":"10.1007/s11831-024-10094-7","url":null,"abstract":"<div><p>In this review article, we investigate the dynamic nature of the Kozeny–Carman Model concerning permeability and its application in engineering contexts. Providing insights into the changing dynamics of permeability within mining, petroleum, and geotechnical engineering, among other engineering applications. While some are complex and require additional modifications to be applicable, others are simple and still function in specific situations. Therefore, having a thorough understanding of the most recent permeability evolution model would help engineers and researchers in finding the right solution for engineering issues for prospects. The permeability evolution model Kozeny–Carman (KC) put forth by previous and current researchers is compiled in this paper, with a focus on its features and drawbacks.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3843 - 3855"},"PeriodicalIF":9.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560501","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}

{"title":"A Systematic Review on Game-Theoretic Models and Different Types of Security Requirements in Cloud Environment: Challenges and Opportunities","authors":"Komal Singh Gill,&nbsp;Anju Sharma,&nbsp;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}

{"title":"A Metaheuristic Perspective on Extracting Numeric Association Rules: Current Works, Applications, and Recommendations","authors":"Salma Yacoubi,&nbsp;Ghaith Manita,&nbsp;Amit Chhabra,&nbsp;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}

{"title":"Machine Learning Optimization Techniques: A Survey, Classification, Challenges, and Future Research Issues","authors":"Kewei Bian,&nbsp;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}

{"title":"Computational Modelling and Mechanical Characteristics of Polymeric Hybrid Composite Materials: An Extensive Review","authors":"Ankit Gangwar,&nbsp;Vikash Kumar,&nbsp;Murat Yaylaci,&nbsp;Subrata Kumar Panda","doi":"10.1007/s11831-024-10097-4","DOIUrl":"10.1007/s11831-024-10097-4","url":null,"abstract":"<div><p>This study explores the reinforcement of foreign materials (fibers/particles) in polymeric composites, aiming to improve structural characteristics under variable loads. The article critically reviews experimental techniques for composite fabrication, computational modelling, and analysis. It also offers a detailed examination of mechanical properties, manufacturing defects, and applications associated with these composites. Hybrid composites (HC) are highlighted for their exceptional potential across various engineering applications, demonstrating enhanced structural attributes without imposing a weight penalty or surpassing the parent structure’s overall weight. The review explores into the influences of multiple defects, surface treatment, and other parameters affecting the structural integrity of HC during fabrication and application. Furthermore, the article provides a comprehensive understanding, including HC classifications, benefits, and limitations.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3901 - 3921"},"PeriodicalIF":9.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369678","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}