Archives of Computational Methods in Engineering最新文献

{"title":"A Critical Review on Segmentation of Glioma Brain Tumor and Prediction of Overall Survival","authors":"Novsheena Rasool,&nbsp;Javaid Iqbal Bhat","doi":"10.1007/s11831-024-10188-2","DOIUrl":"10.1007/s11831-024-10188-2","url":null,"abstract":"<div><p>In recent years, the surge in glioma brain tumor cases has positioned it as the 10th most prevalent tumor affecting individuals across diverse age groups. Gliomas, characterized by their invasive nature, unpredictable localization, and heterogeneous subregions, pose a substantial threat to public health. Accurate segmentation of glioma subregions within magnetic resonance imaging (MRI) images is pivotal for the efficient planning of treatment and the prognostication of overall patient survival. This review examines recent advancements in glioma brain tumor segmentation (BTS) and overall survival (OS) prediction while addressing inherent biases and proposing innovative solutions. We explore the evolution of convolutional neural network (CNN) architectures, from traditional 2D CNNs to advanced 2.5D and 3D CNNs, which have greatly enhanced segmentation accuracy and efficiency. Furthermore, cutting-edge techniques for BTS, including attention mechanisms, ensemble methods, transformer-based models, and generative adversarial networks (GANs), are discussed. Additionally, we examine machine learning (ML) models for OS prediction, including support vector machines (SVM) and random forest regressors (RFRs), as well as pioneering methods such as radiomics-based approaches, consensus-based classifiers, and explainable artificial intelligence (XAI). By comparing different preprocessing techniques, model architectures, data sources, and evaluation metrics, we identify the most effective methods and emphasize the importance of collaboration in developing reliable prognostic tools. By consolidating current research, this paper advances ongoing investigations and offers a visionary path for future studies, providing guidance to healthcare stakeholders for refining patient care strategies in glioma management.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1525 - 1569"},"PeriodicalIF":9.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769828","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 and Optimization Algorithms for Vibration, Bending and Buckling Analyses of Composite/Nanocomposite Structures: A Systematic and Comprehensive Review","authors":"Dervis Baris Ercument,&nbsp;Babak Safaei,&nbsp;Saeid Sahmani,&nbsp;Qasim Zeeshan","doi":"10.1007/s11831-024-10186-4","DOIUrl":"10.1007/s11831-024-10186-4","url":null,"abstract":"<div><p>Composite/nanocomposite structures have been commonly utilized in a variety of applications. The ability to tailor composite materials to attain superior performance in aspects such as weight and strength has made these materials a popular option in numerous applications, such as automotive, marine, aerospace, and civil engineering. With this wide range of use cases, the composite/nanocomposite structures in practice present themselves in different geometries, such as shells, plates, or beams. It is of great importance to make the most out of these materials, as they are often more difficult or costly to manufacture. As such, with such a wide range of applications, it is of the essence to have a good understanding of composite/nanocomposite materials’ vibrational, buckling, or bending behaviors to grant us the ability to properly design these composite structures. To improve our design of composite/nanocomposite structures, researchers have used a large selection of optimization methods over the years, and recently, with the advent of machine learning, great focus has been placed on studying and improving composite/nanocomposite structures. This review aims to provide a comprehensive summary of the findings on the studies concerned with the bending, buckling, or vibration behaviors of composite/nanocomposite plate, shell, or beam structures in the context of optimization or machine learning methods from 2014 to 2024. The review is split into two main sections of optimization and machine learning, with subsections for buckling, vibration, and bending, with further subsections for plate, shell, and beam structures. The review is intended to act as a valuable resource for scholars invested in the use of optimization/machine learning methods for the study of vibration/buckling/bending of composite/nanocomposite shell/plate/beam structures.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1679 - 1731"},"PeriodicalIF":9.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769826","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 Review on Developments in Evolutionary Computation Approaches for Road Traffic Flow Prediction","authors":"Bharti Naheliya,&nbsp;Poonam Redhu,&nbsp;Kranti Kumar","doi":"10.1007/s11831-024-10189-1","DOIUrl":"10.1007/s11831-024-10189-1","url":null,"abstract":"<div><p>Widespread traffic congestion significantly impacts the quality of life, posing several problems and challenges. To reduce traffic congestion, it is necessary to have accurate information about traffic flow. Accurately predicting traffic flow is challenging due to its uncertainty, nonlinearity and time-varying characteristics. A traffic management system in a city is the most important component for traffic flow prediction. This can assist drivers in selecting the best routes to their intended destinations. Therefore, cities need a comprehensive system for more precise traffic flow forecasts. Consequently, various Artificial Intelligence (AI) based techniques have been developed over time to address the issues associated with traffic flow forecasts. One of them is computational intelligence (CI), a subset of AI that can be used with AI techniques to deal with the nonlinearity and randomness of traffic flow in a better way. This review presents a detailed analysis of evolutionary computation (EC) methodologies, which belong to the field of computational intelligence. Swarm intelligence (SI) and evolutionary algorithms are types of EC techniques, each presenting optimization frameworks characterized by unique theories and objective functions. Most often employed techniques such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO) and artificial bee colony (ABC) are discussed and have been used to solve road traffic flow prediction problems. Additionally, hybrid approaches that combine EC techniques with AI-based techniques leverage the strengths of both methods to enhance the prediction accuracy of traffic flow models. This study examines and summarizes the most recent articles on the application of EC techniques in traffic flow prediction. Challenges and possibilities for future research work are also illustrated. The objective of this paper is to contribute to the existing knowledge by compiling, analyzing and evaluating the developments in evolutionary computation techniques as they relate to the prediction of traffic flow on roads.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1499 - 1523"},"PeriodicalIF":9.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769921","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 on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms","authors":"Vishnu Vardhana Reddy Karna,&nbsp;Viswavardhan Reddy Karna,&nbsp;Varaprasad Janamala,&nbsp;V. N. Koteswara Rao Devana,&nbsp;V. Ravi Sankar Ch,&nbsp;Aravinda Babu Tummala","doi":"10.1007/s11831-024-10194-4","DOIUrl":"10.1007/s11831-024-10194-4","url":null,"abstract":"<div><p>Cardiovascular diseases claim approximately 17.9 million lives annually, with heart attacks and strokes accounting for over 80% of these deaths. Key risk factors, including hypertension, hyperglycemia, dyslipidemia, and obesity, are identifiable, offering opportunities for timely intervention and reduced mortality. Early detection of heart disease enables individuals to adopt lifestyle changes or seek medical treatment. However, conventional diagnostic methods, such as electrocardiograms—commonly used in clinics and hospitals to detect abnormal heart rhythms—are not effective in identifying actual heart attacks. Additionally, angiography, while more precise, is an invasive method, financial strain on patients, and high chances of incorrect diagnosis, highlighting the need for alternative approaches. The main goal of this study was to assess the accuracy of machine learning techniques, including both individual and combined classifiers, in early detection of heart diseases. Furthermore, the study aims to highlight areas where additional research is necessary. Our investigation covers a decade period from 2014 to 2024, including a thorough review of pertinent literature from international conferences and top journals from the databases like Springer, ScienceDirect, IEEEXplore, Web of Science, PubMed, MDPI, Hindawi and so on. The following keywords were used to search the articles: heart disease risk, heart disease prediction, data mining, data preprocessing, machine learning algorithms, ensemble classifiers, deep learning algorithms, feature selection, hyperparameter optimization techniques. We examine the methodologies used and evaluate their effectiveness in predicting cardiovascular conditions. Our findings reveal notable progress in applying machine learning and deep learning in cardiology. The study concludes by proposing a framework that incorporates current machine learning techniques to enhance heart disease prediction.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1763 - 1795"},"PeriodicalIF":9.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769830","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 Survey on Automatic Image Captioning Approaches: Contemporary Trends and Future Perspectives","authors":"Garima Salgotra,&nbsp;Pawanesh Abrol,&nbsp;Arvind Selwal","doi":"10.1007/s11831-024-10190-8","DOIUrl":"10.1007/s11831-024-10190-8","url":null,"abstract":"<div><p>The automatic generation of image captions is one of the complex computer vision tasks that involve integration of object detection and natural language processing (NLP). In recent times, one of the significant aspects is to design image captioning approaches that accurately and efficiently generate appropriate image captions in a particular domain. With the emergence of deep learning paradigms, the task of image captioning becomes comparatively easier than traditional template-based approaches. In this article, we expound an in-depth examination of state of the art (SOTA) image captioning methods, along with the key conceptions. Besides, a comparative analysis of evaluation protocols is presented that are presently used to access the efficacy of the algorithms. Moreover, the study reveals open research issues in the existing methods that can be further investigated by the research community. One of the key challenges is to develop larger corpora of language specific dataset to design image captioning approaches in other regional languages such as Hindi, Marathi, Sanskrit, Telugu, and Gujarati etc. Furthermore, designing accurate and efficient image captioning approaches requisite the notion of attention mechanism in the images.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1459 - 1497"},"PeriodicalIF":9.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769856","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":"Development on Unsteady Aerodynamic Modeling Technology at High Angles of Attack","authors":"Baigang Mi,&nbsp;Shixin Cheng,&nbsp;Hao Zhan,&nbsp;Jingyi Yu,&nbsp;Yiming Wang","doi":"10.1007/s11831-024-10180-w","DOIUrl":"10.1007/s11831-024-10180-w","url":null,"abstract":"<div><p>Directly obtaining the dynamic values of the unsteady aerodynamics at large angle of attack by either the CFD or experimental technologies to present further analysis should pay great costs. Therefore, the unsteady aerodynamic modeling based on a few calculations or experimental data has been established and developed. This study mainly discusses the development and challenges of unsteady aerodynamic modeling of aircraft at high angle of attack, investigates the accuracy, efficiency, and future development of the conventional and modern intelligent models divided according to the established physical basis. The conventional methods have been built on valuating changing law of either the macroscopic aerodynamic performance or microscopic flow separating characteristics, which is mainly composed of linear/nonlinear aerodynamic derivative model, integrated models, differential models, aerodynamic incremental model and angular rate model. The intelligent methods are represented by fuzzy logic, support vector machines and shallow / deep neural network models, all of which are proposed by training the sample data based on various intelligence algorithms. Compared to the conventional aerodynamic models, these intelligent models have strong generalization ability and high predication efficiency. However, they are poorly interpretable due to the lack of physical basis on the dynamic flow fields. In general, the future unsteady aerodynamic models should be developed by focusing on the intelligently characterization of physical meaning of the nonlinear dynamic flow fields to improve the predication accuracy and efficiency on the complex aerodynamic forces/moments, and the applications in aircraft design and flight dynamics.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4305 - 4357"},"PeriodicalIF":9.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826393","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":"Detection and Identification of Hazardous Hidden Objects in Images: A Comprehensive Review","authors":"Satyajit Swain,&nbsp;K. Suganya Devi","doi":"10.1007/s11831-024-10173-9","DOIUrl":"10.1007/s11831-024-10173-9","url":null,"abstract":"<div><p>Hidden object detection has attracted a lot of attention recently due to its importance in security surveillance and other real-world applications. It is considered one of the most challenging tasks in computer vision. Thanks to deep learning for playing a significant role in the rapid technical evolution in this field over the past decade. This article presents a roadmap of hidden object detection, starting from its insightful evolution in 1984, and extensively reviews the technical evolution and shifts in detection approaches. To the best of our knowledge, this is the first ever review work carried out in this field. Various aspects related to hidden object detection have been discussed, including basic building blocks of the detection system, historical milestone detectors, detection datasets, challenges, pre-processing techniques, modern state-of-the-art detection frameworks, and the various evaluation metrics used to assess the detection performance. Towards the end, the paper emphasizes on some unanswered research concerns and possible future prospects in the field of hidden object detection. This review paper aims to serve as a valuable resource for researchers, practitioners, and enthusiasts seeking a thorough understanding of the concepts, advancements, and challenges in this dynamic area of computer vision as hidden object detection continues to have an impact on a variety of interdisciplinary fields of research.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"1135 - 1183"},"PeriodicalIF":9.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602438","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":"Generative Adversarial Networks (GANs) for Medical Image Processing: Recent Advancements","authors":"Mohd Ali,&nbsp;Mehboob Ali,&nbsp;Mubashir Hussain,&nbsp;Deepika Koundal","doi":"10.1007/s11831-024-10174-8","DOIUrl":"10.1007/s11831-024-10174-8","url":null,"abstract":"<div><p>Generative Adversarial Networks (GANs) constitute an advanced category of deep learning models that have significantly transformed the domain of generative modelling. They demonstrate a profound capability to produce realistic and high-quality synthetic data across various domains. Recently, GANs have also emerged as a powerful and innovative approach in medical image processing. Numerous scholarly investigations consistently highlight the superiority of GAN-based methodologies in this context. The generation of realistic synthetic images aims to advance segmentation precision, augment image quality, and facilitate multimodal analysis. These enhancements significantly bolster the analytical capabilities of medical professionals, leading to more precise diagnostic evaluations and the formulation of personalized treatment plans, thereby contributing to improved patient prognosis. In this work, we rigorously review the latest advancements in the application of Generative Adversarial Networks (GANs) within the domain of medical imaging, encompassing research published between 2018 and 2024. The corpus of literature selected for this review is derived from the most relevant and authoritative databases, including Elsevier, Springer, IEEE Xplore, and Google Scholar, among others. This review rigorously evaluates scholarly publications employing Generative Adversarial Networks (GANs) for the synthesis and generation of medical images, segmentation of medical imaging data, image-to-image translation in medical contexts, and denoising or reconstruction of medical imagery. The findings of this review present a thorough synthesis of contemporary applications of Generative Adversarial Networks (GANs) in the domain of medical imaging. This investigation serves as a prospective reference in the realm of GAN utilization for medical image processing, offering guidance and insights for current and future research endeavors.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"1185 - 1198"},"PeriodicalIF":9.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602119","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":"Deep Learning Approaches for Early Prediction of Conversion from MCI to AD using MRI and Clinical Data: A Systematic Review","authors":"Gelareh Valizadeh,&nbsp;Reza Elahi,&nbsp;Zahra Hasankhani,&nbsp;Hamidreza Saligheh Rad,&nbsp;Ahmad Shalbaf","doi":"10.1007/s11831-024-10176-6","DOIUrl":"10.1007/s11831-024-10176-6","url":null,"abstract":"<div><p>Due to the absence of definitive treatment for Alzheimer’s disease (AD), slowing its development is essential. Accurately predicting the conversion of mild cognitive impairment (MCI) -a potential early stage of AD- to AD is challenging due to the subtle distinctions between individuals who will develop AD and those who will not. As an increasing body of evidence in the literature suggests, advanced magnetic resonance imaging (MRI) scans, coupled with high-performance computing techniques and novel deep learning techniques, have revolutionized the ability to predict MCI to AD conversion. This study systematically reviewed the publications from 2013 to 2023 (July) to investigate the contribution of deep learning in predicting the MCI conversion to AD, concentrating on the MRI data (structural or functional) and clinical information. The search conducted across seven different databases yielded a total of 2273 studies. Out of these, 78 relevant studies were included, which were thoroughly reviewed, and their essential details and findings were extracted. Furthermore, this study comprehensively explores the challenges associated with predicting the conversion from MCI to AD using deep learning methods with MRI data. Also, it identifies potential solutions to address these challenges. The research field of predicting MCI to AD conversion from MRI data using deep learning techniques is constantly evolving. There is an increasing focus on employing explainable approaches to improve transparency in the analysis process. The paper concludes with an overview of future perspectives and recommends conducting further studies in MCI to AD conversion prediction using deep learning methods.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"1229 - 1298"},"PeriodicalIF":9.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602410","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":"Critical Comparison of Motion Integration Strategies and Discretization Choices in the Material Point Method","authors":"Sacha Duverger,&nbsp;Jérôme Duriez,&nbsp;Pierre Philippe,&nbsp;Stéphane Bonelli","doi":"10.1007/s11831-024-10170-y","DOIUrl":"10.1007/s11831-024-10170-y","url":null,"abstract":"<div><p>To simulate large, history-dependent material displacements, the Material Point Method (MPM) solves for the kinematics of Lagrangian material points being embedded with mechanical variables while moving freely within a fixed mesh. The MPM procedure makes use of the latter mesh as a computational grid, where the momentum balance equation with the acceleration field are first projected onto nodes, before material points can be moved. During that process, a number of different choices have been adopted in the literature for what concerns the computational definition of time increments of velocity and position, from the knowledge of nodal acceleration. An overview of these different motion integration strategies is herein proposed, with a particular emphasis on their impact onto the MPM conservative properties. Original results illustrate the discussion, considering either simple configurations of solid translation and rotation or a more complex collapse of a frictional mass. These analyses furthermore reveal hidden properties of some motion integration strategies regarding conservation, namely a direct influence of the time step value during a time integration being inspired by the Particle In Cell (PIC) ancestor of the MPM. The spatial, resp. temporal (in comparison with vorticity), discretizations are also shown to affect the angular momentum conservation of the FLIP method, resp. an affine extension of PIC (APIC).</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 3","pages":"1369 - 1397"},"PeriodicalIF":9.7,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769897","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}